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问题成为研究热点。传统路径规划方法在面对非结构化、不确定性和高维状态空间时存在局限性，而强化学习作为一种数据驱动的优化方法，能够通过与环境交互自主学习最优策略，为解决这一问题提供了新思路。本研究旨在探索强化学习在智能机器人路径规划中的应用潜力，提出了一种基于深度强化学习的路径规划算法，该算法结合深度神经网络和Q学习框架，通过引入注意力机制提升对复杂环境特征的感知能力，并设计了自适应奖励函数以平衡探索与利用过程。实验采用模拟环境与真实机器人平台相结合的方式，验证了所提算法在不同场景下的适用性与鲁棒性。结果表明，该算法能够在动态障碍物环境中实现高效路径规划，显著降低碰撞概率并缩短路径长度，同时具备较强的泛化能力。
关键词：智能机器人 深度强化学习 路径规划
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 unstructured, uncertain and high-dimensional state space, while reinforcement learning, as a data-driven optimization method, can independently learn the optimal strategy by interacting with the environment, providing a new idea for solving this problem. This study aims to explore the application potential of reinforcement learning in intelligent robot path planning, puts forward a path planning algorithm based on deep reinforcement learning, the algorithm combined with deep neural network and Q learning fr amework, by introducing attention mechanism improve perception of complex environment characteristics, and designed the adaptive reward function to balance the exploration and utilization process. The experiment combines the simulated environment and the real robot platform to verify the applicability and robustness of the proposed algorithm in different scenarios. The results show that the algorithm can realize efficient path planning in the dynamic obstacle environment, significantly reduce the collision probability and shorten the path length, and have strong generalization ability.
Keyword:Intelligent Robot Deep Reinforcement Learning Path Planning
目 录
1绪论 1
1.1强化学习与路径规划的研究背景 1
1.2智能机器人路径规划的意义与价值 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2强化学习基础理论与算法分析 2
2.1强化学习的基本概念与框架 2
2.2常见强化学习算法及其特点 3
2.3强化学习在动态环境中的适应性 3
2.4强化学习算法的优化策略 4
2.5理论基础对路径规划的指导作用 4
3强化学习在路径规划中的应用挑战 4
3.1动态环境下的路径规划问题 5
3.2高维状态空间的处理方法 5
3.3实时性要求与计算效率的权衡 6
3.4不确定性因素对路径规划的影响 6
3.5强化学习模型的收敛性分析 7
4强化学习驱动的路径规划方案设计 7
4.1路径规划问题的形式化描述 7
4.2基于强化学习的路径规划算法设计 8
4.3多目标路径规划的实现方法 8
4.4实验验证与性能评估 8
4.5方案改进与未来发展方向 9
结论 9
参考文献 11
致谢 12
