Deep Reinforcement Learning for Mobile Robot Path Planning
<|reference_start|>Deep Reinforcement Learning for Mobile Robot Path Planning: 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.<|reference_end|>
@article{liu2024deep,
    title={Deep Reinforcement Learning for Mobile Robot Path Planning},
    author={Hao Liu, Yi Shen, Shuangjiang Yu, Zijun Gao, Tong Wu},
    journal={arXiv preprint arXiv:2404.06974},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06974},
    primaryClass={cs.RO}
}