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.

翻译：路径规划是一个重要问题，在视频游戏、机器人等多个领域均有应用。本文提出了一种新方法，用于解决基于深度强化学习（DRL）的移动机器人路径规划问题。我们设计了基于DRL的算法，包括奖励函数和参数优化，以避免在二维环境中进行耗时的工作。同时，我们设计了一种双向搜索混合A*算法，以提高局部路径规划的质量。我们将所设计的算法移植到简单的嵌入式环境中，以测试算法在移动机器人上运行时的计算负载。实验表明，在机器人平台上部署时，本文基于DRL的算法能够实现更好的规划效果，并消耗更少的计算资源。