Collision-free motion is essential for mobile robots. Most approaches to collision-free and efficient navigation with wheeled robots require parameter tuning by experts to obtain good navigation behavior. This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robot utilizes LiDAR sensor data and a deep neural network to generate control signals guiding it toward a specified target while avoiding obstacles. We employ two reinforcement learning algorithms in the Gazebo simulation environment: Deep Deterministic Policy Gradient and proximal policy optimization. The study introduces an enhanced neural network structure in the Proximal Policy Optimization algorithm to boost performance, accompanied by a well-designed reward function to improve algorithm efficacy. Experimental results conducted in both obstacle and obstacle-free environments underscore the effectiveness of the proposed approach. This research significantly contributes to the advancement of autonomous robotics in complex environments through the application of deep reinforcement learning.

翻译：无碰撞运动对于移动机器人至关重要。大多数轮式机器人的无碰撞高效导航方法需要专家进行参数调优以获得良好的导航行为。本研究探讨了应用深度强化学习训练移动机器人在复杂环境中实现自主导航的方法。机器人利用LiDAR传感器数据和深度神经网络生成控制信号，引导其朝向指定目标并规避障碍物。我们在Gazebo仿真环境中采用了两种强化学习算法：深度确定性策略梯度算法和近端策略优化算法。本研究在近端策略优化算法中引入了一种增强的神经网络结构以提升性能，并配合精心设计的奖励函数来提高算法效能。在含障碍物和无障碍物环境中进行的实验结果验证了所提方法的有效性。本研究通过应用深度强化学习，显著推动了复杂环境下自主机器人技术的发展。