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.
@article{arxiv.2404.06974,
title = {Deep Reinforcement Learning for Mobile Robot Path Planning},
author = {Hao Liu and Yi Shen and Shuangjiang Yu and Zijun Gao and Tong Wu},
journal= {arXiv preprint arXiv:2404.06974},
year = {2024}
}