Deep Reinforcement Learning for Mobile Robot Path Planning
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929309050994688 |
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| author | Liu, Hao Shen, Yi Yu, Shuangjiang Gao, Zijun Wu, Tong |
| author_facet | Liu, Hao Shen, Yi Yu, Shuangjiang Gao, Zijun Wu, Tong |
| contents | Path planning is an important problem with the the applications in many aspects, such as video games, robotics etc. This paper proposes a novel method to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. We design DRL-based algorithms, including reward functions, and parameter optimization, to avoid time-consuming work in a 2D environment. We also designed an Two-way search hybrid A* algorithm to improve the quality of local path planning. We transferred the designed algorithm to a simple embedded environment to test the computational load of the algorithm when running on a mobile robot. Experiments show that when deployed on a robot platform, the DRL-based algorithm in this article can achieve better planning results and consume less computing resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06974 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Mobile Robot Path Planning Liu, Hao Shen, Yi Yu, Shuangjiang Gao, Zijun Wu, Tong Robotics Path planning is an important problem with the the applications in many aspects, such as video games, robotics etc. This paper proposes a novel method to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. We design DRL-based algorithms, including reward functions, and parameter optimization, to avoid time-consuming work in a 2D environment. We also designed an Two-way search hybrid A* algorithm to improve the quality of local path planning. We transferred the designed algorithm to a simple embedded environment to test the computational load of the algorithm when running on a mobile robot. Experiments show that when deployed on a robot platform, the DRL-based algorithm in this article can achieve better planning results and consume less computing resources. |
| title | Deep Reinforcement Learning for Mobile Robot Path Planning |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.06974 |