摘 要
随着人工智能技术的快速发展，智能机器人在复杂动态环境中的路径规划成为研究热点。传统路径规划方法在面对不确定性和多变场景时存在局限性，而强化学习作为一种数据驱动的优化方法，能够有效提升机器人的自主决策能力。本研究旨在探索基于强化学习的智能机器人路径规划方法，以提高其在复杂环境中的适应性和效率。研究采用深度强化学习算法，结合卷积神经网络与Q学习框架，构建了适用于动态环境的路径规划模型。通过设计奖励函数和状态空间表示，使机器人能够在未知环境中实时调整策略，实现高效导航。实验结果表明，所提出的模型在多种复杂场景下表现出优异的性能，相较于传统方法具有更高的成功率和更低的时间成本。此外，该方法还具备较强的泛化能力，可应用于不同类型的机器人平台。
关键词：强化学习 路径规划 深度强化学习
Abstract
With the rapid development of artificial intelligence technology, the path planning of intelligent robots in complex dynamic environment has become a research hotspot. Traditional path planning methods have limitations in the face of uncertain and changeable scenarios, while reinforcement learning, as a data-driven optimization method, can effectively improve the autonomous decision-making ability of robots. This study aims to explore intelligent robot path planning methods based on reinforcement learning to improve their adaptability and efficiency in complex environments. This paper uses a deep reinforcement learning algorithm, combining convolutional neural network and Q learning fr amework, to construct a path planning model suitable for dynamic environment. By designing the reward function and state-space representation, the robot is able to adjust navigation in real time in an unknown environment. The experimental results show that the proposed model shows excellent performance in multiple complex scenarios, with a higher success rate and lower time cost compared to traditional methods. In addition, the method also has strong generalization ability and can be applied to different types of robot platforms.
Keyword:Reinforcement Learning Path Planning Deep Reinforcement Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3强化学习在路径规划中的应用方法 1
1.4本文研究内容与创新点 2
2强化学习基础理论与路径规划模型构建 2
2.1强化学习基本原理概述 2
2.2路径规划问题的数学建模 2
2.3基于强化学习的路径规划框架设计 3
2.4强化学习算法的选择与优化策略 3
2.5模型验证与初步实验结果 4
3动态环境下的路径规划算法研究 4
3.1动态环境特征分析 4
3.2强化学习在动态环境中的适应性改进 5
3.3障碍物规避算法设计与实现 5
3.4实时路径更新机制的研究 6
3.5动态场景实验与性能评估 6
4复杂场景中路径规划的优化与应用 7
4.1复杂场景的定义与挑战 7
4.2基于深度强化学习的路径优化方法 7
4.3多目标路径规划的实现策略 8
4.4实际应用场景中的算法测试与调整 8
4.5性能对比与结果分析 9
结论 9
参考文献 10
致谢 11
