Motion Planning for Mobile Robots-Focusing on Deep Reinforcement Learning: A Systematic Review

Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots. This paper reviews the methods based on motion-planning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured e...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.69061-69081
Main Authors: Sun, Huihui, Zhang, Weijie, Yu, Runxiang, Zhang, Yujie
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
deep reinforcement learning
Dynamics
Heuristic algorithms
Human motion
Mobile robot
Motion planning
Multiple robots
Planning
Robot dynamics
Robot kinematics
Robot sensing systems
Robots
Sensors
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Summary:Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots. This paper reviews the methods based on motion-planning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment. The conventional methods of DRL are categorized to value-based, policy-based and actor-critic-based algorithms, and the corresponding theories and applications are surveyed. Furthermore, the recently-emerged methods of DRL are also surveyed, especially the ones involving the imitation learning, meta-learning and multi-robot systems. According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3076530