At present, the application of mobile robots is more and more extensive, and the movement of mobile robots cannot be separated from effective navigation, especially path exploration. Aiming at navigation problems, this article proposes a method based on deep reinforcement learning and recurrent neural network, which combines double net and recurrent neural network modules with reinforcement learning ideas. At the same time, this article designed the corresponding parameter function to improve the performance of the model. In order to test the effectiveness of this method, based on the grid map model, this paper trains in a two-dimensional simulation environment, a three-dimensional TurtleBot simulation environment, and a physical robot environment, and obtains relevant data for peer-to-peer analysis. The experimental results show that the proposed algorithm has a good improvement in path finding efficiency and path length.

中文翻译：

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一种基于深度强化学习的移动机器人导航新方法

目前，移动机器人的应用越来越广泛，移动机器人的运动离不开有效的导航，尤其是路径探索。针对导航问题，本文提出了一种基于深度强化学习和循环神经网络的方法，将双网和循环神经网络模块与强化学习思想相结合。同时，本文设计了相应的参数函数来提高模型的性能。为了测试该方法的有效性，本文基于网格图模型，分别在二维仿真环境、三维 TurtleBot 仿真环境和实体机器人环境中进行训练，并获取相关数据供peer-to ——同行分析。