Intelligent Robotic Perception Systems
Open accessAbstract
Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics.
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Robot perception functions depend on the robot's operational environment.
“Like localization and navigation, robot perception functions depend on the robot's operational environment [185].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [185]
Most robotic perception systems rely on ML techniques, ranging from classical to DL approaches.
“These days, most robotic perception systems rely on ML techniques, ranging from classical to DL approaches where the learning can be in the form of unsupervised learning or supervised classifiers using handcrafted features, or DL neural networks [185].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [185]
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Multimodal vehicle detection: fusing 3D-LIDAR and color camera data
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Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“However, in the majority of applications, the fundamental role of environment mapping is to model data from exteroceptive sensors mounted onboard the robot to facilitate reasoning, and inference regarding the robot‘s real-world environment [185].”
BackgroundInfluential
- Authors
- Premebida, C., Ambrus, R., & Marton, Z.-C.
- Affiliations
- University of Coimbra (PT)
- Corresponding author
- Premebida, Cristiano
- ORCID
- Premebida, Cristiano
- Venue
- Applications of Mobile Robots
- Publisher
- IntechOpen
- Published
- 2019-03-20
- OpenAlex
- W2912196091
- Cited by
- 52
- References
- 4
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 11 · 2023: 8 · 2022: 12 · 2021: 5 · 2020: 2 · 2019: 1
- FWCI
- 15.86
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IntechOpen eBooks (publishedVersion, cc-by)
- elib (German Aerospace Center) (acceptedVersion)
- BiblioBoard Library Catalog (Open Research Library) (submittedVersion, cc-by)
- Contact-less Assessment of In-vivo Body Signals Using Microwave Doppler Radar (InTech) (submittedVersion, cc-by-nc)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- robot perception, machine learning in robotics, deep learning, object detection, scene understanding, human detection, pedestrian detection, activity recognition, object modeling, human-robot interaction, robot decision-making
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