The DOI matches its Crossref record; 148 of the 149 references it cites were found in a registry or an index; 62 of the 145 sources its claims cite were checked for support.
Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Open accessAbstract
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the field of advanced robotics in recent years. AI, ML, and DL are transforming the field of advanced robotics, making robots more intelligent, efficient, and adaptable to complex tasks and environments. Some of the applications of AI, ML, and DL in advanced robotics include autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance. These technologies are also being used in the development of collaborative robots (cobots) that can work alongside humans and adapt to changing environments and tasks. The AI, ML, and DL can be used in advanced transportation systems in order to provide safety, efficiency, and convenience to the passengers and transportation companies . Also, the AI, ML, and DL are playing a critical role in the advancement of manufacturing assembly robots, enabling them to work more efficiently, safely, and intelligently. Furthermore, they have a wide range of applications in aviation management, helping airlines to improve efficiency, reduce costs, and improve customer satisfaction. Moreover, the AI, ML, and DL can help taxi companies in order to provide better, more efficient, and safer services to customers. The research presents an overview of current developments in AI, ML, and DL in advanced robotics systems and discusses various applications of the systems in robot modification. Further research works regarding the applications of AI, ML, and DL in advanced robotics systems are also suggested in order to fill the gaps between the existing studies and published papers. By reviewing the applications of AI, ML, and DL in advanced robotics systems, it is possible to investigate and modify the performances of advanced robots in various applications in order to enhance productivity in advanced robotic industries.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This narrative review surveys how artificial intelligence (AI), machine learning (ML), and deep learning (DL) are being applied across advanced robotics, including manufacturing, transportation, drones, ship navigation, aviation, and taxi services. It argues these technologies can make robots more intelligent, efficient, adaptable, and safe, while also outlining major data, computation, safety, and ethical challenges.
Key findings
- AI, ML, and DL are reported to enable applications such as autonomous navigation, object recognition and manipulation, natural language processing, predictive maintenance, collaborative robots, and autonomous vehicles.
- The review lists advantages including automation, enhanced accuracy, adaptability, predictive maintenance, improved decision-making, efficiency, safety, and cost reduction.
- Specific performance examples include a Random Forest-based localization method achieving 98.8% accuracy, CNN use for object recognition, DDPG for motion planning, PPO for robotic arm control, and Faster R-CNN/YOLO for object detection.
- Challenges identified include the need for large amounts of high-quality data, real-time processing and computational/energy constraints, adaptability in dynamic environments, safety around humans, and ethical/societal concerns such as job displacement and bias.
Methods
- Design: narrative review/overview of current developments in AI, ML, and DL in advanced robotics systems.
- Data or sample: no primary dataset or experimental sample is reported; the paper synthesizes published literature and examples across robotics domains.
- Main analysis: thematic discussion of applications, advantages, challenges, and future research directions, supported by references and figures; no formal systematic search or quantitative meta-analysis is described.
Limitations
- The paper does not explicitly state limitations of the review itself; it lists challenges for AI/ML/DL in robotics, including data quality and availability, real-time computation, energy constraints, adaptability, safety, and ethical/societal risks.
- Evident from the text: as a narrative review, it does not report a systematic search strategy, inclusion/exclusion criteria, or quantitative synthesis, and its coverage is broad and example-based rather than exhaustive.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Verification
Claims the paper makes
69 No full text of the sources · 44 Own interpretation · 38 Partly supported · 20 Supported · 13 Not checked yet · 12 No basis given · 4 Couldn’t confirm · 1 Sources not found · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Together, these technologies have enabled the development of robots that can perform a wide range of tasks, from simple pick-and-place operations to complex manipulation and navigation in unstructured environments [5] .”
What the sources say
- Partly supportedcites [5] Arinez, 2020, Artificial intelligence in advanced manufacturing: current status and future outlook, J. Manuf. Sci. Eng. sourceExperimental — accuracy not yet measured
Section 3.1 (page unavailable) links advances in robot sensing, perception, control, teaching, and learning to greater flexibility and adaptability. Sections 3.4.1 and 3.4.6 describe AI-assisted planning in uncertain environments and collaborative assembly, but the copy does not establish the claimed range from pick-and-place to complex manipulation and navigation in unstructured environments. (LLM, conf=0.82)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The technologies are essential for optimizing the performance of advanced manufacturing robots and AGVs, allowing them to work more efficiently, accurately, and safely in a variety of settings [75] .”
What the sources say
- Partly supportedcites [75] Bałazy, 2021, Positioning algorithm for AGV autonomous driving platform based on artificial neural networks, Robotic Syst. Appl. sourceExperimental — accuracy not yet measured
Section 5 (Conclusions; page not provided) reports that the algorithm lets a mobile platform determine an optimal trajectory and avoid obstacles, supporting the claim's efficiency and safety aspects for an AGV. The paper studies a simulated platform intended to assist people with mobility impairments, not advanced manufacturing robots operating in a variety of settings; it also says implementation in a real AGV and field tests are future work. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Improved Decision-making: By using AI and ML algorithms, robots can make informed decisions based on data analysis, resulting in better overall performance [55] .”
What the sources say
- Partly supportedcites [55] Shrestha, 2021, Augmenting organizational decision-making with deep learning algorithms: principles, promises, and challenges, J. Bus. Res. sourceExperimental — accuracy not yet measured
The Introduction says AI and ML can use data to augment organizational decision-making, and §3.1 says autonomous robots draw on reinforcement learning to select actions. The paper does not establish that robots using these algorithms achieve better overall performance; its decision-making and performance examples concern organizational applications. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Another challenge is the need for robots to be able to operate safely and effectively in a wide range of environments [15] .”
What the sources say
- Supportedcites [15] Linaza, 2021, Data-driven artificial intelligence applications for sustainable precision agriculture, Agronomy source
Section 3.6 (p. 11) says agricultural robots need to detect their surroundings and obstacles for safe, successful navigation. It discusses guidance in open fields and orchards, including where dense foliage blocks GPS, supporting the claim in the agricultural context. (LLM, conf=0.87)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Cost Reduction: The implementation of AI and ML applications in advanced robotics can significantly reduce costs associated with labor and maintenance [54] .”
What the sources say
- Could not verifycites [54] Dimiduk, 2018, Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering, Integr. Mater. Manuf. Innov. sourceExperimental — accuracy not yet measured
The Introduction and Motivation describes ML/AI in autonomous machines, including robots, but does not discuss labor or maintenance costs. Materials and Processes Discovery discusses the expense of experiments and computation, not cost reductions from advanced robotics. Searches for labor costs, maintenance, and cost reduction found no passage supporting the attributed claim. (LLM, conf=0.76)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“By analyzing data from sensors and other sources, predictive maintenance algorithms can predict when a robot's components may fail, allowing for proactive repairs or replacements [8] .”
What the sources say
- Partly supportedcites [8] Lee, 2019, Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data, Procedia Cirp sourceExperimental — accuracy not yet measured
The Introduction says predictive maintenance monitors equipment condition to predict when machine components will fail and help optimize maintenance tasks. Section 4 describes sensor data collected from machine-tool and bearing experiments. The source supports the predictive-maintenance mechanism, but it studies machine tools, not robot components specifically. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“AI and ML algorithms are used to analyze data and make decisions based on the information gathered [14] .”
What the sources say
- Partly supportedcites [14] Wasilow, 2019, Artificial intelligence, robotics, ethics, and the military: a Canadian perspective, AI Magazine sourceExperimental — accuracy not yet measured
The Abstract says AI can analyze surveillance video and might combine sensor data to help military personnel make more informed decisions. It anticipates robots using machine-learning algorithms, but does not establish that AI and ML algorithms are already used in military robotics to make the decisions themselves. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Real-time decision making: AI algorithms can enable robots and AGVs to make real-time decisions based on sensor data, allowing them to adapt to changing conditions in a manufacturing environment [77] .”
What the sources say
- Could not verifycites [77] Cho, 2021, Human gaze-aware attentive object detection for ambient intelligence, Eng. Appl. Artif. Intell. sourceExperimental — accuracy not yet measured
The Introduction discusses ambient intelligence recognizing users and their surroundings, and the Conclusion suggests human-robot symbiosis and autonomous control as possible applications. Neither passage addresses AGVs making real-time decisions from sensor data or adapting to changing manufacturing conditions. Searches for AGVs, manufacturing, and real-time decisions found no passage supporting that claim. (LLM, conf=0.76)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“Robotics applications often require robots to operate in dynamic and changing environments which need adaptability in operations [58] .”
What the sources say
- Supportedcites [58] Arents, 2022, Smart industrial robot control trends, challenges and opportunities within manufacturing, Appl. Sci. source
Section 2 describes dynamic obstacles and other uncertainties in industrial robots’ workspaces, explains that such tasks cannot usually be preprogrammed, and says robots need algorithms that adjust to different scenarios. This supports the claim that changing environments call for adaptability in robot operations. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This can help doctors to identify subtle changes in the images that may indicate the presence of disease [12] .”
What the sources say
- Partly supportedcites [12] Sarker, 2021, Robotics and artificial intelligence in healthcare during COVID-19 pandemic: a systematic review, Rob Auton. Syst. sourceExperimental — accuracy not yet measured
The COVID-19 diagnosis section says AI helps radiologists analyse image patterns and assess the probability of infection, and describes deep-learning models that detect COVID-19 in chest images. It does not say the changes are subtle, and its discussion is focused on COVID-19 rather than disease generally (page not provided). (LLM, conf=0.84)
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
- Probably not a substantial claim. 81
- It may state two separate claims as one. 77
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are all important technologies in the field of robotics [1] .”
What the sources say
- Partly supportedcites [1] Woschank, 2020, A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics, Sustainability sourceExperimental — accuracy not yet measured
Section 4.1 identifies AI, ML, and DL as technologies for improving logistics processes and includes swarm robotics as a smart-warehouse application. The paper does not establish the broader claim that all three are important technologies in robotics generally; its discussion is bounded to smart logistics. No page number is available in our copy. (LLM, conf=0.82)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Robots can detect and interact with their surroundings, make judgments, and carry out difficult tasks with the aid of AI. [2] .”
What the sources say
- Supportedcites [2] Fahle, 2020, Systematic review on machine learning (ML) methods for manufacturing processes–Identifying artificial intelligence (AI) methods for field application, Procedia CIRP source
Section 4.5 (Robotics; page not available in our copy) describes AI-based object recognition for robotic gripping, motion recognition for human–robot collaboration, and path planning. It also reports reinforcement learning used to teach a robot to flip a handkerchief and fold a T-shirt. Together, these examples support the broad paraphrase about sensing surroundings, making task-related decisions, and carrying out complex tasks with AI. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“For example, self-driving cars use computer vision to detect and recognize objects on the road, and ML algorithms to learn and adapt to new situations and road conditions [17] .”
What the sources say
- Supportedcites [17] Elallid, 2022, A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving, J. King Saud Univ.-Comput. Inf. Sci. source
Section 2.1 describes computer-vision models that detect and classify road objects for autonomous vehicles. Section 5.1 reports reinforcement-learning approaches that learn from the driving environment and adapt to different traffic situations; section 5.3 discusses approaches tested across different road and weather conditions. The survey cautions that many such results are from simulations and need real-world testing. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Smart Parking: AI-based parking systems can help drivers find available parking spots quickly and reduce congestion in busy areas.”
What the sources say
- Partly supportedcites [84] Zantalis, 2019, A review of machine learning and IoT in smart transportation, Future Int. sourceExperimental — accuracy not yet measured
Section 1.4 (Smart Transportation) says smart parking systems can minimize the time spent searching for parking, and Section 2.2.2 (Parking) describes systems that detect and recommend available spaces. The paper discusses traffic congestion elsewhere, but our copy does not say that parking systems reduce congestion in busy areas. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“There are also concerns about the potential for robots to be used for harmful purposes, such as military applications or surveillance [61] .”
What the sources say
- Partly supportedcites [61] Kapeller, 2020, A taxonomy of ethical, legal and social implications of wearable robots: an expert perspective, Sci. Eng. Ethics sourceExperimental — accuracy not yet measured
In Results (‘Wearable Robots in Society’), the paper raises concerns about harmful dual uses of wearable robots, including movement between civilian and military uses. It also says wearable robots may intensify workplace surveillance. The claim broadens these concerns to robots generally; the Introduction states that military applications were excluded from the paper’s assessment. (LLM, conf=0.83)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This helps manufacturers to identify and eliminate bottlenecks, reduce waste, and increase productivity [63] .”
What the sources say
- Partly supportedcites [63] Huang, 2021, A survey on AI-driven digital twins in industry 4.0: smart manufacturing and advanced robotics, Sensors sourceExperimental — accuracy not yet measured
Section 2.1 (Overview; page unavailable) describes digital twins as enhancing manufacturing productivity, and Section 2.4 (Process and Material; page unavailable) discusses resource savings and process optimization. The passages do not establish that manufacturers use them to identify and eliminate bottlenecks, so that part of the attributed claim is unsupported. (LLM, conf=0.82)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Automation: AI, ML, and DL can automate many repetitive and mundane tasks in robotics, freeing up human resources to focus on more complex tasks [49] .”
What the sources say
- Partly supportedcites [49] Sardar, 2019, Impact of artificial intelligence on interventional cardiology: from decision-making aid to advanced interventional procedure assistance, Cardiovasc. Intervent. sourceExperimental — accuracy not yet measured
The Robotics passage (section labeled Abstract; page unavailable) says AI has potential to automate procedures in the catheterization lab, and the paper describes an automated robotic guidewire movement. It does not say that AI, ML, and DL automate many repetitive or mundane robotic tasks, or that such automation frees personnel to focus on more complex work. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Robotics is used to automate tasks in manufacturing plants, such as assembly line tasks, painting, and welding.”
What the sources say
- Partly supportedcites [2] Fahle, 2020, Systematic review on machine learning (ML) methods for manufacturing processes–Identifying artificial intelligence (AI) methods for field application, Procedia CIRP sourceExperimental — accuracy not yet measured
Section 4.5 (Robotics; page not provided) describes robotics applications for automation and discusses assembly use cases. It does not say that robots perform assembly-line tasks, painting, or welding in manufacturing plants. Section 4.8 discusses laser welding, but not robotic welding. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“AI and ML are used to help cars navigate roads and make driving decisions on their own.”
What the sources say
- Supportedcites [17] Elallid, 2022, A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving, J. King Saud Univ.-Comput. Inf. Sci. source
The Introduction says AI techniques, including deep and reinforcement learning, are used for autonomous-vehicle motion planning and decision making. Section 4.3 describes deep-learning methods that use road-scene images to make driving decisions, and Section 5.1 describes methods for navigating intersections and choosing driving actions. Page numbers are not available in the supplied passages. (LLM, conf=0.93)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“By doing so, downtime may be decreased and overall productivity can rise [65] .”
What the sources say
- Partly supportedcites [65] Pookkuttath, 2022, AI-enabled predictive maintenance framework for autonomous mobile cleaning robots, Sensors sourceExperimental — accuracy not yet measured
The Introduction says manual monitoring may cause extended downtime and that automated predictive maintenance overcomes those pitfalls; it also describes an ML-based approach intended to avoid unplanned downtime. The paper does not state that overall productivity rises, and its Conclusions report fault-classification performance rather than measured downtime or productivity gains. (LLM, conf=0.86)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This can help reduce inventory costs and improve overall efficiency [72] .”
What the sources say
- Supportedcites [72] Toorajipour, 2021, Artificial intelligence in supply chain management: a systematic literature review, J. Bus. Res. source
The Discussion reports that an AI algorithm for inventory replenishment is more cost-effective than conventional algorithms. The Conclusion also says AI improves supply-chain network orchestration efficiently. Together, these passages support the hedged claim that it can help lower inventory-related costs and improve efficiency; the paper does not quantify either benefit. (LLM, conf=0.85)
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“AI and ML algorithms are used to optimize the farming operations, such as predicting weather patterns, optimizing water usage, and monitoring crop health [15] .”
What the sources say
- Partly supportedcites [15] Linaza, 2021, Data-driven artificial intelligence applications for sustainable precision agriculture, Agronomy sourceExperimental — accuracy not yet measured
The Introduction describes AI-assisted analysis of field conditions and risks from pests and diseases, while the Conclusions describe AI-based irrigation that reduced water use in strawberry crops. These passages support crop-health monitoring and water-use optimization, but our copy does not show AI or ML being used to predict weather patterns. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This can be especially useful in large fleets of vehicles, such as those used in public transportation [83] .”
What the sources say
- Supportedcites [83] Theissler, 2021, Predictive maintenance enabled by machine learning: use cases and challenges in the automotive industry, Reliab. Eng. Syst. Saf. source
Section 5.2 describes predictive-maintenance methods that compare vehicles across fleets, including a fleet of city buses, and reports an approach for detecting faulty buses. The paper’s concluding discussion also identifies statistical predictive maintenance as a key approach when using vehicle-fleet data. Together, these passages support the claim’s large-fleet and public-transportation example. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Predictive Maintenance: ML algorithms can analyze data from sensors installed on vehicles and predict when maintenance is needed, allowing for proactive repairs and reducing downtime.”
What the sources say
- Supportedcites [83] Theissler, 2021, Predictive maintenance enabled by machine learning: use cases and challenges in the automotive industry, Reliab. Eng. Syst. Saf. source
Section 5.2 describes using sensor signals from vehicle fleets to detect faults. Section 3 says predictive maintenance uses system-health information to time repairs before failure, and the paper identifies increased system availability as a benefit of ML-based predictive maintenance. Together, these support the paraphrase, including reduced downtime. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“AI and ML are being used to develop robots that can autonomously navigate and manage crops, increasing efficiency and reducing labor costs.”
What the sources say
- Partly supportedcites [15] Linaza, 2021, Data-driven artificial intelligence applications for sustainable precision agriculture, Agronomy sourceExperimental — accuracy not yet measured
Section 2.3 says AI and robotics help or replace manual intervention and that robots autonomously weed, irrigate, and manage individual plants. Section 3.6 (p. 11) describes navigation and auto-steering for agricultural robots. The passages do not establish that these robots increase efficiency or reduce labor costs. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Supply Chain Optimization: AI and ML can be used to optimize the supply chain by predicting demand and ensuring that the right materials are available at the right time.”
What the sources say
- Supportedcites [72] Toorajipour, 2021, Artificial intelligence in supply chain management: a systematic literature review, J. Bus. Res. source
Section 3.2 describes AI methods for demand forecasting, while the Discussion describes AI-based inventory replenishment and provision balancing to avoid stockouts. Together, these support the claim that AI and ML can help optimize supply chains by predicting demand and keeping needed materials available. Page numbers are not provided in our copy. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Assembly robots: AI, ML, and DL technologies are enabling robots during assembly process to work smarter, faster, and more efficiently than ever before, and are helping manufacturers to improve quality, reduce costs, and increase productivity [67] .”
What the sources say
- Partly supportedcites [67] Murugamani, 2022, Wireless communication for robotic process automation using machine learning technique sourceExperimental — accuracy not yet measured
The Introduction says AI can improve robots’ image recognition and precision in assembly. Section 4 links human–robot work to higher production standards and cheaper prices, while §7.1 reports improved productivity and operating efficiency. The copy does not establish that ML and DL specifically produce all these benefits during assembly. (LLM, conf=0.85)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Better decision-making: AI, ML, and DL can enable robots to make better decisions based on data analysis and pattern recognition, leading to improved performance and outcomes [52] .”
What the sources say
- Partly supportedcites [52] Begoli, 2019, The need for uncertainty quantification in machine-assisted medical decision making, Nature Machine Intelligence sourceExperimental — accuracy not yet measured
Section 2.1 reports that AI and deep learning recognize patterns in medical images and that some predictive models outperform classical approaches. It does not address robots or show that robots make better decisions or achieve improved outcomes. Section 2 also cautions that adopting automated decision-making in medicine requires uncertainty quantification. Page numbers are unavailable in our copy. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Robotics is used in military operations for tasks such as reconnaissance, surveillance, and bomb disposal.”
What the sources say
- Partly supportedcites [14] Wasilow, 2019, Artificial intelligence, robotics, ethics, and the military: a Canadian perspective, AI Magazine sourceExperimental — accuracy not yet measured
The paper’s swarming-technologies discussion says UAV swarms could be used for surveillance and reconnaissance. A note in “Summary and Future Considerations” mentions robotics and sensors for IED and explosive detection, but it does not establish bomb disposal; the swarm passage also describes a potential use rather than an established operation. (LLM, conf=0.86)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Robotics is used to automate tasks in agriculture, such as planting, harvesting, and spraying.”
What the sources say
- Partly supportedcites [15] Linaza, 2021, Data-driven artificial intelligence applications for sustainable precision agriculture, Agronomy sourceExperimental — accuracy not yet measured
Section 2.3 says agricultural robots autonomously perform tasks such as weeding and irrigating, supporting the general point about automation. The Introduction identifies harvesting as a future role for robotic systems, but the paper does not identify planting or spraying as robotic tasks; its mention of spraying in the Conclusions concerns AI applications generally. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This can save waste and boost overall effectiveness. [71] .”
What the sources say
- Supportedcites [71] Abioye, 2021, Artificial intelligence in the construction industry: a review of present status, opportunities and future challenges, J. Build. Eng. source
Section 4.3.1, “Resource and waste optimisation,” says advanced data analytics could minimise construction waste. The “Optimisation” passage also lists increased efficiency as a benefit. Together, these support the hedged claim that it can reduce waste and improve overall effectiveness. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This can help improve the quality of the products and reduce the need for human intervention in the quality control process [64] .”
What the sources say
- Supportedcites [64] Sundaram, 2023, Artificial intelligence-based smart quality inspection for manufacturing, Micromachines (Basel) source
Section 2.2 describes improving product quality as a goal of quality control and says AI enables continuous assessment of product quality. Section 7 says the proposed AI approach automates inspection to an extent, supporting the qualified claim that it can reduce the need for human intervention; it does not claim to eliminate operators. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Quality Control: AI, ML, and DL algorithms can be used to monitor the manufacturing process in real-time and identify defects or anomalies in the products being produced.”
What the sources say
- Supportedcites [64] Sundaram, 2023, Artificial intelligence-based smart quality inspection for manufacturing, Micromachines (Basel) source
The Abstract describes real-time tracking of manufacturing systems and continuous assessment of product quality. Section 2.2 links continuous quality assessment to sensors and AI, while Sections 1 and 9.2 describe ML/DL-based inspection and a shop-floor CNN application that identifies defective products. Together, these support the claim as a statement of capability. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This can be particularly useful in situations where human intervention is not practical or safe, such as in hazardous environments or in situations where precision is critical [66] .”
What the sources say
- Partly supportedcites [66] Gumbs, 2021, Artificial intelligence surgery: how do we get to autonomous actions in surgery?, Sensors sourceExperimental — accuracy not yet measured
The Discussion describes autonomous surgical actions and says sensor-assisted needle insertions can become smarter and potentially safer. That lends limited support to their usefulness in precision-sensitive procedures, but the paper does not address hazardous environments or situations where human intervention is impractical or unsafe. The claim extends a surgery-focused discussion to those broader settings. (LLM, conf=0.78)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Route Optimization: ML algorithms can optimize delivery routes for logistics companies, reducing travel time, and improving fuel efficiency.”
What the sources say
- Partly supportedcites [85] Olugbade, 2022, A review of artificial intelligence and machine learning for incident detectors in road transport systems, Mathemat. Comput. Appl. sourceExperimental — accuracy not yet measured
The Abstract and §3.1 identify route optimization as an application of AI and machine learning in road transportation. Section 4 also discusses AI in the movement of goods by road, but the copy does not say that ML optimizes delivery routes for logistics companies or that doing so reduces travel time and improves fuel efficiency. Page numbers were not available in the indexed passages. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“AI and ML algorithms can be used to manage air traffic more efficiently, reducing the likelihood of delays and improving safety [119] .”
What the sources say
- Supportedcites [119] Degas, 2022, A survey on artificial intelligence (ai) and explainable ai in air traffic management: current trends and development with future research trajectory, Appl. Sci. source
The AI section describes algorithms for optimising air traffic and avoiding collisions. Section 8, “Scenarios XAI in ATM,” describes a hypothetical system that could address a predicted landing delay while helping avoid safety risks. These passages support the claim’s statement of potential use, not a claim that delay or safety improvements have been measured. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“ML algorithms can analyze customer data to personalize their experience. For example, by analyzing past trips, AI can suggest preferred routes or destinations to customers, reducing their travel time and improving their overall experience [134] .”
What the sources say
- Partly supportedcites [134] Abduljabbar, 2019, Applications of artificial intelligence in transport: an overview, Sustainability sourceExperimental — accuracy not yet measured
The Shared Mobility section says AI improves customer experience and that Uber suggests destinations based on a rider’s ride history. It also discusses efficient route planning and accurate travel-time estimates, but does not say that the history-based suggestions reduce customers’ travel time or suggest preferred routes. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“ML algorithms can analyze parking data to optimize parking space usage, while DL algorithms can recognize license plates and enforce parking regulations [84] .”
What the sources say
- Partly supportedcites [84] Zantalis, 2019, A review of machine learning and IoT in smart transportation, Future Int. sourceExperimental — accuracy not yet measured
Section 2.2.2 (Parking; page unavailable) reports that ML algorithms analyze parking-lot images to detect free spaces, supporting the claim about using parking data to improve space availability. Section 2.1.7 (Deep Learning; page unavailable) describes CNN-based detection of parking-lot occupancy, but the passages found do not say that deep learning recognizes license plates or enforces parking regulations. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“DL algorithms can also be used to monitor passenger behavior and detect potential safety issues [87] .”
What the sources say
- Partly supportedcites [87] Nikitas, 2020, Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era, Sustainability sourceExperimental — accuracy not yet measured
The Introduction discusses AI methods in transport, and Section 3 lists improved traffic security through surveillance and monitoring as a potential benefit of connected and autonomous vehicles. Neither passage says that deep-learning algorithms monitor passenger behavior or detect safety issues arising from it. (LLM, conf=0.78)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“DL algorithms are particularly effective at processing complex data, such as images and video, and can be used to identify cargo types and detect anomalies, such as damaged or dangerous cargo [114] .”
What the sources say
- Partly supportedcites [114] Brunton, 2021, Data-driven aerospace engineering: reframing the industry with machine learning, AIAA J. sourceExperimental — accuracy not yet measured
Section 2.1.4 describes deep convolutional neural networks as widely used for machine vision, and Section 5.3 says machine-learning tools can detect faults and potential failures in sensor data. Neither passage connects these capabilities to identifying cargo types or detecting damaged or dangerous cargo. Page numbers were unavailable in our copy. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This can result in cost savings and a reduced environmental footprint [85] .”
What the sources say
- Partly supportedcites [85] Olugbade, 2022, A review of artificial intelligence and machine learning for incident detectors in road transport systems, Mathemat. Comput. Appl. sourceExperimental — accuracy not yet measured
The Introduction says AI can optimize routes to cut emissions, and Section 4 says intelligent transportation systems may improve energy efficiency. These support a possible reduced environmental footprint, but the searched passages do not state that the applications produce cost savings. No page numbers were available in our copy. (LLM, conf=0.82)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These technologies are also being used in the development of collaborative robots (cobots) that can work alongside humans and adapt to changing environments and tasks.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Robotics is a rapidly evolving field, and the use of AI, ML, and DL is likely to continue to play a key role in shaping the future of robotics [6] .”
What the sources say
- Not checked — no full text of the sourcecites [6] Andras, 2020, Artificial intelligence and robotics: a combination that is changing the operating room, World J. Urol. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The AI, ML, and DL can be used in advanced transportation systems in order to provide safety, efficiency, and convenience to the passengers and transportation companies .”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, the AI, ML, and DL are playing a critical role in the advancement of manufacturing assembly robots, enabling them to work more efficiently, safely, and intelligently.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, they have a wide range of applications in aviation management, helping airlines to improve efficiency, reduce costs, and improve customer satisfaction.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The application of AI, ML, and DL in robotics has the potential to transform the field, enabling robots to become more intelligent, autonomous, and effective in a wide range of applications.”
What the sources say
- Not checked — no full text of the sourcecites [6] Andras, 2020, Artificial intelligence and robotics: a combination that is changing the operating room, World J. Urol. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, the AI, ML, and DL can help taxi companies in order to provide better, more efficient, and safer services to customers.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“DL has been particularly useful in robotics for tasks such as image and speech recognition, natural language processing, and object detection.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the field of advanced robotics in recent years.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Some of the applications of AI, ML, and DL in advanced robotics include autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI, ML, and DL are transforming the field of advanced robotics, making robots more intelligent, efficient, and adaptable to complex tasks and environments.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Predictive maintenance is a maintenance approach that uses AI and ML to detect potential issues before they occur.”
What the sources say
- Not checked yetcites [8] Lee, 2019, Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data, Procedia Cirp source
Checked before — its earlier verdict is held back until a new check runs.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not checked yet
In the paper
“Meanwhile, ML algorithms use data from sensors, cameras, and GPS to make navigation decisions [18] .”
What the sources say
- Not checked — no full text of the sourcecites [18] Ning, 2021, A survey on hybrid human-artificial intelligence for autonomous driving, IEEE Trans. Intell. Transp. Syst. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As robots become more autonomous and interact with humans, ensuring their safety becomes a critical challenge.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, while AI, ML, and DL offer many opportunities for robotics, there are also significant challenges that must be addressed in order to realize their full potential.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI and ML can be used to analyze production data and optimize production planning.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Robotic surgery is a field where AI and ML are revolutionizing the way operations are performed.”
What the sources say
- Not checked — no full text of the sourcecites [10] Panesar, 2019, Artificial intelligence and the future of surgical robotics, Ann. Surg. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This can be useful for robots and AGVs to navigate and interact with their surrounding [76] .”
What the sources say
- Not checked — no full text of the sourcecites [76] Chryssolouris, 2023, Artificial intelligence in manufacturing equipment, automation, and robots source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Improved efficiency: By optimizing processes and reducing waste, AI, ML, and DL can improve the overall efficiency of robotics systems, resulting in cost savings and increased productivity.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Improved Decision Making: AI and ML algorithms can analyze large amounts of data and make informed decisions based on that data, allowing robots to make better decisions and take appropriate actions [51] .”
What the sources say
- Not checked — no full text of the sourcecites [51] Abdar, 2022, The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process, IEEE Syst., Man, Cybernet. Mag. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Intelligent Transportation Systems (ITS): AI-based ITS can help improve traffic flow, reduce congestion, and enhance safety on roads.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Enhanced accuracy: These technologies can improve the accuracy and precision of robotic systems, reducing errors and improving overall performance.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the use of AI, ML, and DL in robotics has the potential to revolutionize the field and unlock new levels of performance, efficiency, and safety.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“These technologies enable vehicles to perceive and interpret their surroundings, make decisions based on data, and navigate roads safely without human intervention [81] .”
What the sources say
- Not checked — no full text of the sourcecites [81] Ma, 2020, Artificial intelligence applications in the development of autonomous vehicles: a survey, IEEE/CAA J. Automat. Sinica source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“However, data collection, labeling, and annotation can be expensive and time-consuming, and the data may be noisy or biased, which can affect the accuracy and reliability of the models [48] .”
What the sources say
- Not checked — no full text of the sourcecites [48] Baduge, 2022, Artificial intelligence and smart vision for building and construction 4.0: machine and deep learning methods and applications, Autom. Constr. source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI/ML/DL models must be designed to adapt to new situations and learn from experience, which can be challenging.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ML algorithms can analyze traffic patterns and optimize signal timings at intersections, while DL algorithms can identify potential hazards and alert drivers in real-time.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“AI/ML/DL algorithms must be designed to prevent accidents, detect and respond to potential hazards, and avoid collisions with humans and other objects [59] .”
What the sources say
- Not checked — no full text of the sourcecites [59] Garg, 2021, Overview of artificial intelligence source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This requires the development of robust AI and ML algorithms that can handle unpredictable situations and adapt to changing conditions.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Increased safety: By automating hazardous or dangerous tasks, AI, ML, and DL can improve safety in the workplace, reducing the risk of accidents and injuries.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“For example, robots like Pepper can recognize and respond to human gestures and speech, making them useful in a variety of contexts such as customer service or healthcare [9] .”
What the sources say
- Not checked — no full text of the sourcecites [9] Jiang, 2020, A survey on artificial intelligence in Chinese sign language recognition, Arabian J. Sci. Eng. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“It can enable robots to adapt to changing conditions, work collaboratively with human operators, and learn from past experiences to improve future performance.”
What the sources say
- Not checked yetcites [68] Dimitropoulos, 2021, Seamless human–robot collaborative assembly using artificial intelligence and wearable devices, Appl. Sci. source
Checked before — its earlier verdict is held back until a new check runs.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not checked yet
In the paper
“Adaptability: AI-powered robots can adapt to changing environments and tasks, making them highly versatile and useful in a range of industries and applications [48] .”
What the sources say
- Not checked — no full text of the sourcecites [48] Baduge, 2022, Artificial intelligence and smart vision for building and construction 4.0: machine and deep learning methods and applications, Autom. Constr. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“ITS includes technologies like smart traffic signals, electronic toll collection systems, and intelligent parking systems, which help in optimizing the transportation system [82] .”
What the sources say
- Not checked — no full text of the sourcecites [82] Iyer, 2021, AI enabled applications towards intelligent transportation, Transport. Eng. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For instance, robots like self-driving cars use AI to detect obstacles and predict traffic movements.”
What the sources say
- Not checked — no full text of the sourcecites [18] Ning, 2021, A survey on hybrid human-artificial intelligence for autonomous driving, IEEE Trans. Intell. Transp. Syst. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“In addition, robotics applications often require real-time processing, which can be computationally expensive and may require specialized hardware [56] .”
What the sources say
- Not checked yetcites [56] Károly, 2020, Deep learning in robotics: survey on model structures and training strategies, IEEE Trans. Syst., Man, Cybernet. source
Checked before — its earlier verdict is held back until a new check runs.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not checked yet
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This can be particularly challenging in robotics, where data can be difficult to obtain and may be subject to noise and uncertainty.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There are many potential applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in advanced manufacturing robots.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, there are concerns about the impact of automation on jobs and the potential for AI systems to be biased or to perpetuate existing inequalities.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“By using advanced algorithms, robotic surgeons can assist human surgeons during complex procedures, reducing the risk of complications and improving outcomes.”
What the sources say
- Not checked — no full text of the sourcecites [10] Panesar, 2019, Artificial intelligence and the future of surgical robotics, Ann. Surg. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This can help to prevent accidents and reduce the risk of injury to workers.”
What the sources say
- Not checked — no full text of the sourcecites [69] Hashmi, 2022, Artificial intelligence techniques for implementation of intelligent machining, Mater. Today: Proc. source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Object detection and recognition are critical tasks in robotics that have become possible thanks to deep learning.”
What the sources say
- Not checked — no full text of the sourcecites [7] Bai, 2020, Object detection recognition and robot grasping based on machine learning: a survey, IEEE Access source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Robotics is used to provide services to humans, such as cleaning, food delivery, and customer service.”
What the sources say
- Not checked — no full text of the sourcecites [16] Belk, 2021, Ethical issues in service robotics and artificial intelligence, Serv. Ind. J. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Adaptability: These technologies can enable robots to adapt to changing environments and situations, making them more versatile and capable of handling a wider range of tasks [53] .”
What the sources say
- Not checked yetcites [53] Zohuri, 2022, Business resilience system integrated artificial intelligence system, Int. J. Theoret. Computat. Phys.
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This helps in optimizing traffic flow and reducing congestion [80] .”
What the sources say
- Not checked — no full text of the sourcecites [80] Sharma, 2020, The role of blockchain, AI and IoT for smart road traffic management system source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, by increasing productivity, cutting costs, and raising product quality, the employment of AI, ML, and DL in advanced industrial robots has the potential to completely transform the manufacturing sector.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, there are ethical and societal challenges associated with the use of AI and robotics.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AI and ML algorithms are used to enable robots to interact with humans and understand their needs and preferences [16] .”
What the sources say
- Not checked — no full text of the sourcecites [16] Belk, 2021, Ethical issues in service robotics and artificial intelligence, Serv. Ind. J. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are all crucial technologies in the field of robotics, and they can play a significant role in improving the efficiency and safety of cargo handling at airports.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In advanced robotics, these technologies are used to create robots that can perform complex tasks and learn from experience.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The fields of robotics and artificial intelligence (AI) are rapidly advancing and merging, with machine learning (ML) and deep learning (DL) playing an increasingly important role in the development of intelligent robots.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, AI, ML, and DL are becoming increasingly important in the development and operation of advanced transportation systems, helping to improve efficiency, safety, and sustainability.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Unmanned aerial vehicles (UAVs), usually referred to as advanced drones, are being utilized more frequently in a wide range of industries, including agriculture, construction, mining, search and rescue, and military activities [89] .”
What the sources say
- Not checked — no full text of the sourcecites [89] Olawale, 2020, AI simulations and programming environments for drones: an overview, Drones Smart-Cities source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They can help to improve the accuracy, efficiency, and autonomy of drones, making them more useful in a variety of applications.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“The use of AI, ML, and DL in advanced drones has expanded their capabilities and made them more efficient and effective in performing various tasks of different drones [90] .”
- Also stated in: § 5.1. Drones · p. 10 · plain
“The use of AI, ML, and DL in advanced drones has expanded their capabilities and made them more efficient and effective in performing various tasks.”
What the sources say
- Not checked — no full text of the sourcecites [90] Heidari, 2023, Machine learning applications in internet-of-drones: systematic review, recent deployments, and open issues, ACM Comput. Surv. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § 5.1. Drones · p. 10 · plain
In the paper
“DL can be used to analyze traffic patterns and optimize routes for taxi drivers. This can help them avoid congestion and take the most efficient route to their destination, which can save time and reduce fuel costs [129] .”
What the sources say
- Not checked — no full text of the sourcecites [129] Jacob, 2021, Artificial bee colony optimization algorithm for enhancing routing in wireless networks, J. Artif. Intell. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ML can be used to train the system to recognize and respond to different situations, such as changing weather conditions or unexpected obstacles.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“This data can then be used to optimize crop yields and reduce waste [97] .”
What the sources say
- Not checked — no full text of the sourcecites [97] Saranya, 2023, A comparative study of deep learning and internet of things for precision agriculture, Eng. Appl. Artif. Intell. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Tesla's Autopilot system uses a combination of cameras, radar, and ultrasonic sensors to detect and respond to obstacles and other vehicles on the road.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the context of airport cargo handling, AI can be used to automate tasks such as cargo tracking, inventory management, and customs processing.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“For example, AI can be used to identify and track objects, detect obstacles and avoid collisions, and optimize flight paths for maximum efficiency [91] .”
What the sources say
- Not checked — no full text of the sourcecites [91] De Swarte, 2019, Artificial intelligence, ethics and human values: the cases of military drones and companion robots, Artif. Life Robot. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Algorithms can be used to predict and prevent accidents by alerting drivers of potential hazards and suggesting safer routes [86] .”
What the sources say
- Not checked — no full text of the sourcecites [86] Halim, 2016, Artificial intelligence techniques for driving safety and vehicle crash prediction, Artif. Intell. Rev. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“AI algorithms can be used to identify potential hazards or obstacles in a ship's path and make course corrections to avoid them [103] .”
What the sources say
- Not checked yetcites [103] Yan, 2021, Emerging approaches applied to maritime transport research: past and future, Commun. Transport. Res. source
Checked before — its earlier verdict is held back until a new check runs.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not checked yet
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI, ML, and DL are transforming the taxi industry by improving efficiency, reducing costs, and enhancing customer experience.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“DL can also be used to improve ship performance by optimizing engine settings or predicting equipment failures before they occur [105] .”
What the sources say
- Not checked — no full text of the sourcecites [105] Qiao, 2023, Survey of deep learning for autonomous surface vehicles in marine environments, IEEE Trans. Intell. Transp. Syst. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“AI algorithms can be used to analyze passenger data to predict behavior, preferences and improve personalized service offerings.”
What the sources say
- Not checked — no full text of the sourcecites [118] Iqbal, 2020, Big data analytics and computational intelligence for cyber–physical systems: recent trends and state of the art applications, Future Generat. Comput. Syst. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the combination of AI, ML, and DL with robotics has the potential to create a wide range of applications that can benefit society in various ways.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“AI algorithms can be used to optimize flight routes, taking into account factors such as weather conditions, air traffic, and fuel efficiency.”
What the sources say
- Not checked — no full text of the sourcecites [117] Choi, 2019, Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art, Adv. Robot. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Advanced robotics applications that use AI, machine learning, and deep learning include autonomous vehicles, drone navigation, industrial robots, healthcare robots, and search and rescue robots.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“DL can be used to monitor drivers and passengers for safety concerns, such as distracted driving or unruly behavior. This can help taxi companies maintain high safety standards and reduce the risk of accidents [132] .”
What the sources say
- Not checked — no full text of the sourcecites [132] Singh, 2021, Highway 4.0: digitalization of highways for vulnerable road safety development with intelligent IoT sensors and machine learning, Saf. Sci. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The combination of AI, ML, and DL in advanced robotics is enabling the development of robots that are more intelligent, versatile, and capable than ever before.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“DL is used in autonomous driving technology, which is being developed by taxi companies to reduce labor costs and improve safety.”
What the sources say
- Not checked — no full text of the sourcecites [133] Miglani, 2019, Deep learning models for traffic flow prediction in autonomous vehicles: a review, solutions, and challenges, Vehicular Commun. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“AI, ML, and DL can be used to improve the accuracy of autonomous vehicles, allowing robots to operate them more safely and effectively.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the case of Tesla machines, AI, ML, and DL are used to enable a range of advanced capabilities.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This allows the robot to perceive and understand its environment in a way that is similar to how humans do, and to make decisions based on that understanding.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The applications of AI, ML, and DL in advanced transportation systems have the potential to revolutionize the way we travel, making transportation safer, more efficient, and more sustainable.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“For example, robotic cargo handlers can use DL algorithms to identify and sort cargo based on size, weight, and destination, and they can use ML algorithms to optimize their movements and avoid collisions with other cargo handling equipment [115] .”
What the sources say
- Not checked — no full text of the sourcecites [115] Sukhorukov, 2020, Digital transformation of airline management as the basis of innovative development source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“DL can be used to enable the system to learn from past experiences and make more accurate predictions about future events.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ML algorithms are used to analyze this sensor data and make decisions about how to control the vehicle, such as adjusting its speed or steering to avoid collisions.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“AI algorithms can analyze transaction data to identify fraudulent activities, such as false claims of lost property or overcharging customers. This can help taxi companies reduce losses and improve customer trust [131] .”
What the sources say
- Not checked — no full text of the sourcecites [131] Wang, 2022, AI-empowered trajectory anomaly detection for intelligent transportation systems: a hierarchical federated learning approach, IEEE Trans. Intell. Transp. Syst. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These technologies are transforming the field of robotics and enabling robots to perform tasks that were once considered too difficult or dangerous for humans.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the use of AI, ML, and DL in robotics systems can improve the efficiency, safety, and accuracy of airport cargo handling, which can lead to faster delivery times, reduced costs, and improved customer satisfaction.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are increasingly being integrated into robotics, providing robots with the ability to learn, adapt, and improve their performance over time.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, AI, ML, and DL are all essential technologies in the development of robotics drones.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“AI and ML can be used to analyze historical data to predict future demand for taxi services. This can help taxi companies optimize their fleet management, scheduling, and pricing strategies to ensure that they are providing the right number of taxis in the right areas at the right times [135] .”
What the sources say
- Not checked — no full text of the sourcecites [135] Ramani, 2022, Predicting new york taxi trip duration based on regression analysis using ML and time series forecasting using DL source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are all technologies that have the potential to revolutionize the field of robotics taxi services.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the combination of AI, ML, and DL has the potential to make robotics taxi services safer, more efficient, and more enjoyable for passengers.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“AI and DL can be used to personalize the customer experience by analyzing data on passenger preferences and behavior, and providing recommendations for entertainment, food, and other services [130] .”
What the sources say
- Not checked — no full text of the sourcecites [130] Cheng, 2023, An investigation on the influencing factors of elderly people's intention to use financial AI customer service, Int. Res. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Additionally, DL algorithms are used to improve the accuracy of object detection and recognition, enabling the vehicle to identify and track pedestrians, cyclists, and other objects on the road.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“DL is particularly useful in robotics because it can be used to develop algorithms that enable the robot to learn from large amounts of sensory data, such as images or audio recordings.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The use of AI, ML, and DL in robotics autopilot systems has the potential to improve the reliability and safety of autonomous vehicles, making them a promising technology for the future of transportation.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In robotics autopilot systems, these technologies can be used to improve the accuracy, efficiency, and safety of the system.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These technologies can help improve the safety, efficiency, and overall customer experience of autonomous taxi services [123] .”
What the sources say
- Not checked yetcites [123] Arbatani, 2021, Competitive strategies of mobile applications in online taxi services: the cases of Snapp and Tap30 in Iran, Int. J. Emerg. Markets
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“ML models can be trained on historical data to predict potential collisions and provide real-time recommendations to avoid them [107] .”
What the sources say
- Not checked — no full text of the sourcecites [107] Hammedi, 2022, Federated deep learning-based framework to avoid collisions between inland ships source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
There are gaps between existing studies and published papers in the applications of AI, ML, and DL in advanced robotics systems.
“Further research works regarding the applications of AI, ML, and DL in advanced robotics systems are also suggested in order to fill the gaps between the existing studies and published papers.”
The paper relies on this without support.
Reviewing applications of AI, ML, and DL in advanced robotics systems can enable investigation and modification of the performances of advanced robots to enhance productivity in advanced robotic industries.
“By reviewing the applications of AI, ML, and DL in advanced robotics systems, it is possible to investigate and modify the performances of advanced robots in various applications in order to enhance productivity in advanced robotic industries.”
The paper relies on this without support.
AI can be used to optimize ship performance, such as fuel consumption and speed, based on environmental conditions.
“AI can also be used to optimize ship performance, such as fuel consumption and speed, based on environmental conditions.”
The paper relies on this without support.
By analyzing sensor data and forecasting equipment breakdowns beforehand, DL algorithms enable proactive maintenance and save downtime in ship navigation.
“By analyzing sensor data and forecasting equipment breakdowns beforehand, DL algorithms enable proactive maintenance and save downtime.”
The paper relies on this without support.
AI and ML can be used to develop autonomous mooring systems that can safely and efficiently dock ships without human intervention.
“AI and ML can be used to develop autonomous mooring systems that can safely and efficiently dock ships without human intervention.”
The paper relies on this without support.
AI-powered chatbots can be used to provide 24/7 customer support and help passengers book rides, track their taxis, and get answers to their questions.
“AI-powered chatbots can be used to provide 24/7 customer support and help passengers book rides, track their taxis, and get answers to their questions.”
The paper relies on this without support.
The wireless communication network can be used to provide an advanced distributed approach in cab booking systems to enhance the efficiency of intelligent cab service systems.
“The wireless communication network can be used to provide an advanced distributed approach in Cab booking system in order to enhance efficiency of intelligent cab service system.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Self-check results appear here once an independent check has confirmed them.
Cited works that were retracted
Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.
A note, not an issue: for example, a cited work that was retracted only after this paper was published, which its authors could not have known.
Cited work 10.1007/s11665-022-07721-4 · retracted 2024-07-10 · notice 10.1007/s11665-024-09804-w · this paper published 2023
A note, not an issue: for example, a cited work that was retracted only after this paper was published, which its authors could not have known.
Cited work 10.1177/13506501231158259 · retracted 2024-12-11 · notice 10.1177/13506501241299150 · this paper published 2023
Citations
Cites
2 retracted
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Metadata
- Authors
- Soori, M., Arezoo, B., & Dastres, R.
- Affiliations
- University of Kyrenia (CY)
- Cyprus International University (CY)
- Corresponding author
- Soori, Mohsen
- Venue
- Cognitive Robotics
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 3
- Pages
- 54-70
- ISSN
- 2667-2413 (ISSN-L)
- OpenAlex
- W4362663195
- Semantic Scholar
- d3b7dd1ebf5230503482afea2a255b14333e6c16
- Cited by
- 1,131
- References
- 149
- Citations by year
- 2026: 232 · 2025: 479 · 2024: 335 · 2023: 83
- FWCI
- 120.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cognitive Robotics (publishedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Figshare (cc-by)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in robotics, advanced robotics, autonomous navigation, collaborative robots, assembly robots, aviation management, taxi services, predictive maintenance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-03, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-03 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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