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

Review · Huihui Sun, Weijie Zhang, Runxiang Yu, Yujie Zhang

- Archive id: doi:10.1109/access.2021.3076530
- Published: 2021-01-01
- Venue: IEEE Access
- DOI: https://doi.org/10.1109/access.2021.3076530
- Citations: 142
- Source URL: https://doi.org/10.1109/access.2021.3076530
- PDF: https://ieeexplore.ieee.org/ielx7/6287639/9312710/09419029.pdf
- Provenance: openalex

## Abstract

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.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [Robot Learning & Embodied AI](https://space-kitty.com/topics/robotics-embodied)

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