Motion planning and control for mobile robot navigation using machine learning: a survey

Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigation systems to move mobile robots from one point to another. Despite their overall success, a recently emerging research...

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Vydáno v:Autonomous robots Ročník 46; číslo 5; s. 569 - 597
Hlavní autoři: Xiao, Xuesu, Liu, Bo, Warnell, Garrett, Stone, Peter
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2022
Springer Nature B.V
Témata:
Artificial Intelligence
Computer Imaging
Control
Deep learning
Engineering
Machine learning
Mechatronics
Motion control
Motion planning
Navigation systems
Pattern Recognition and Graphics
Planning
Robot control
Robot dynamics
Robotics
Robotics and Automation
Robots
Vision
Motion planning
Mobile robot navigation
Motion control
Machine learning
ISSN:0929-5593, 1573-7527
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Shrnutí:Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigation systems to move mobile robots from one point to another. Despite their overall success, a recently emerging research thrust is devoted to developing machine learning techniques to address the same problem, based in large part on the success of deep learning. However, to date, there has not been much direct comparison between the classical and emerging paradigms to this problem. In this article, we survey recent works that apply machine learning for motion planning and control in mobile robot navigation, within the context of classical navigation systems. The surveyed works are classified into different categories, which delineate the relationship of the learning approaches to classical methods. Based on this classification, we identify common challenges and promising future directions.
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ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-022-10039-8