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Autonomous Robots
Erschienen in: Autonomous Robots 5/2022

20.03.2022

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

verfasst von: Xuesu Xiao, Bo Liu, Garrett Warnell, Peter Stone

Erschienen in: Autonomous Robots | Ausgabe 5/2022

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Abstract

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.
Vorheriger Artikel External force observer aided push recovery for torque-controlled biped robots
Nächster Artikel Analysis and acceleration of TORM: optimization-based planning for path-wise inverse kinematics

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Fußnoten
1
In mobile robot navigation, “motion planning” mostly focuses on relatively long-term sequences of robot positions, orientations, and their high-order derivatives, while motion control generally refers to relatively low-level motor commands, e.g., linear and angular velocities. However, the line between them is blurry, and we do not adhere to any strict distinction in terminology in this survey.
 
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Metadaten
Titel
Motion planning and control for mobile robot navigation using machine learning: a survey
verfasst von
Xuesu Xiao
Bo Liu
Garrett Warnell
Peter Stone
Publikationsdatum
20.03.2022
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 5/2022
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-022-10039-8

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