摘 要：随着机器人技术的发展，自主机器人在复杂环境中的应用日益广泛，路径规划作为其核心问题备受关注。传统路径规划算法难以应对动态、不确定环境下的高效导航需求，基于强化学习的路径规划算法为解决该问题提供了新思路。本文旨在研究基于强化学习的自主机器人路径规划算法，以提高机器人在未知复杂环境中的自主导航能力。通过构建强化学习框架，设计适用于机器人路径规划的状态空间、动作空间及奖励函数，并引入深度神经网络以增强算法对高维状态空间的学习能力。实验结果表明，所提算法能够使机器人在复杂环境中快速找到最优或近似最优路径，且具有良好的鲁棒性和适应性。与传统算法相比，该算法在路径长度、规划时间和避障成功率等方面均有显著提升，创新性地将深度强化学习应用于机器人路径规划领域，为实现更加智能、高效的自主机器人导航提供了理论依据和技术支持。
关键词：强化学习；路径规划；自主机器人；深度神经网络；复杂环境导航
Research on Autonomous Robot Path Planning Algorithm Based on Reinforcement Learning
英文人名
Directive teacher:×××
Abstract：With the development of robotics technology, autonomous robots are being increasingly applied in complex environments, making path planning a critical issue that has garnered significant attention. Traditional path planning algorithms struggle to meet the demands of efficient navigation in dynamic and uncertain environments. Reinforcement learning-based path planning algorithms offer a novel approach to addressing this challenge. This study focuses on investigating reinforcement learning-based path planning algorithms for autonomous robots to enhance their autonomous navigation capabilities in unknown complex environments. By constructing a reinforcement learning fr amework, we design state spaces, action spaces, and reward functions suitable for robot path planning, while incorporating deep neural networks to improve the algorithm's ability to learn from high-dimensional state spaces. Experimental results demonstrate that the proposed algorithm enables robots to rapidly find optimal or near-optimal paths in complex environments, exhibiting excellent robustness and adaptability. Compared with traditional algorithms, the proposed method achieves significant improvements in path length, planning time, and obstacle avoidance success rate, innovatively applying deep reinforcement learning to the field of robot path planning and providing theoretical foundations and technical support for achieving smarter and more efficient autonomous robot navigation.
Keywords: Reinforcement Learning；Path Planning；Autonomous Robot；Deep Neural Network；Complex Environment Navigation
目 录
一、绪论 1
(一)研究背景与意义 1
(二)国内外研究现状 1
(三)本文研究方法与结构安排 1
二、强化学习理论基础 2
(一)强化学习基本概念 2
(二)强化学习算法分类 2
(三)强化学习在路径规划中的应用 3
三、自主机器人路径规划模型构建 4
(一)环境建模与表示 4
(二)机器人运动模型 5
(三)路径规划问题定义 5
四、基于强化学习的路径规划算法设计 6
(一)算法框架设计 6
(二)关键技术实现 7
(三)算法性能优化 8
结论 8
参考文献 9
致谢 9
关键词：强化学习；路径规划；自主机器人；深度神经网络；复杂环境导航
Research on Autonomous Robot Path Planning Algorithm Based on Reinforcement Learning
英文人名
Directive teacher:×××
Abstract：With the development of robotics technology, autonomous robots are being increasingly applied in complex environments, making path planning a critical issue that has garnered significant attention. Traditional path planning algorithms struggle to meet the demands of efficient navigation in dynamic and uncertain environments. Reinforcement learning-based path planning algorithms offer a novel approach to addressing this challenge. This study focuses on investigating reinforcement learning-based path planning algorithms for autonomous robots to enhance their autonomous navigation capabilities in unknown complex environments. By constructing a reinforcement learning fr amework, we design state spaces, action spaces, and reward functions suitable for robot path planning, while incorporating deep neural networks to improve the algorithm's ability to learn from high-dimensional state spaces. Experimental results demonstrate that the proposed algorithm enables robots to rapidly find optimal or near-optimal paths in complex environments, exhibiting excellent robustness and adaptability. Compared with traditional algorithms, the proposed method achieves significant improvements in path length, planning time, and obstacle avoidance success rate, innovatively applying deep reinforcement learning to the field of robot path planning and providing theoretical foundations and technical support for achieving smarter and more efficient autonomous robot navigation.
Keywords: Reinforcement Learning；Path Planning；Autonomous Robot；Deep Neural Network；Complex Environment Navigation
目 录
一、绪论 1
(一)研究背景与意义 1
(二)国内外研究现状 1
(三)本文研究方法与结构安排 1
二、强化学习理论基础 2
(一)强化学习基本概念 2
(二)强化学习算法分类 2
(三)强化学习在路径规划中的应用 3
三、自主机器人路径规划模型构建 4
(一)环境建模与表示 4
(二)机器人运动模型 5
(三)路径规划问题定义 5
四、基于强化学习的路径规划算法设计 6
(一)算法框架设计 6
(二)关键技术实现 7
(三)算法性能优化 8
结论 8
参考文献 9
致谢 9
