基于增强型PPO的深度强化学习用于移动机器人安全导航
Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation
AI总结：
本文研究在Gazebo仿真环境中利用深度强化学习实现移动机器人自主避障导航，通过改进PPO算法的神经网络结构和奖励函数提升性能，实验验证了其有效性。
摘要：
无碰撞运动对移动机器人至关重要。大多数针对轮式机器人无碰撞高效导航的方法需要专家进行参数调整才能获得良好的导航行为。本研究探讨了深度强化学习在复杂环境中训练移动机器人自主导航的应用。机器人利用LiDAR传感器数据和深度神经网络生成控制信号，引导其避开障碍物并朝指定目标前进。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和近端策略优化。本研究在近端策略优化算法中引入了一种增强型神经网络结构以提升性能，并设计了一个精心构造的奖励函数来提高算法效果。在含障碍物和无障碍物环境中进行的实验结果证明了所提方法的有效性。本研究通过应用深度强化学习，为复杂环境中自主机器人的发展做出了重要贡献。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
