Positioning algorithm for AGV autonomous driving platform based on artificial neural networks
Open accessAbstract
This paper presents an artificial intelligence algorithm responsible for the autonomy of a platform. The proposed algorithm allows the platform to move from an initial position to a set one without human intervention and with understanding and response to the dynamic environment. The implementation of such a task is possible by using a combination of a camera identifying the environment with a laser LIDAR sensor and a vision system. The signals from the sensors are analysed through convolutional neural networks. Based on AI inference, the platform makes decisions, including determining the optimal path for itself. A transfer learning method will be used to teach the neural network. This article presents the results of learning the applied neural algorithm.
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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.
“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] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [75]
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)
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Research works printed but not linked: 13
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Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Soori, M., Arezoo, B., & Dastres, R. (2023) · Cognitive Robotics · doi:10.1016/j.cogr.2023.04.001 · Cited by 1,131 · see citing works
- Authors
- Bałazy, P., Gut, P., & Knap, P.
- Affiliations
- AGH University of Krakow (PL)
- Venue
- Robotic Systems and Applications
- Publisher
- JVE International Ltd.
- Published
- 2021-08-11
- Volume
- 1
- Issue
- 2
- Pages
- 41-45
- ISSN
- 2669-2473 (ISSN-L)
- OpenAlex
- W3187663478
- Cited by
- 11
- References
- 0
- Citations by year
- 2025: 1 · 2024: 5 · 2023: 4 · 2022: 1
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Robotic Systems and Applications (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- automated guided vehicles, positioning algorithm, convolutional neural networks, LiDAR, camera-based vision system, path planning, transfer learning
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