摘要
随着智能机器人技术的快速发展，路径规划与避障算法作为其核心功能之一，对提升机器人自主导航能力具有重要意义本研究以提高机器人在复杂动态环境中的路径规划效率和避障性能为目标，深入探讨了基于人工智能的路径规划与避障算法设计首先，结合传统搜索算法与深度强化学习方法，提出了一种融合全局与局部信息的混合路径规划策略，该策略能够在保证路径最优性的同时显著降低计算复杂度其次，针对动态障碍物场景，引入预测模型优化避障算法，通过实时感知与预测障碍物运动轨迹，实现了更高效的动态避障此外，本研究还开发了一种自适应参数调整机制，使算法能够根据环境特征自动调节关键参数实验结果表明，所提出的算法在多种复杂环境中均表现出优异的性能，不仅路径规划时间较传统方法缩短约30%，且避障成功率提升至98%以上总体而言，本研究为智能机器人在未知动态环境中的自主导航提供了新的解决方案，其创新点在于将深度学习与传统算法有机结合，并通过自适应机制增强了算法的鲁棒性和泛化能力，为未来相关领域的研究奠定了坚实基础
关键词：路径规划；避障算法；深度强化学习；动态环境；自适应参数调整机制
Abstract
With the rapid development of intelligent robot technology, path planning and obstacle avoidance algorithms, as one of the core functions, play a significant role in enhancing the autonomous navigation capability of robots. This study aims to improve the efficiency of path planning and obstacle avoidance performance of robots in complex dynamic environments. By integrating artificial intelligence-based approaches, this research explores the design of advanced path planning and obstacle avoidance algorithms. Firstly, a hybrid path planning strategy that combines traditional search algorithms with deep reinforcement learning is proposed, which integrates global and local information. This strategy not only ensures the optimality of the path but also significantly reduces computational complexity. Secondly, for dynamic obstacle scenarios, a prediction model is introduced to optimize the obstacle avoidance algorithm. By real-time perception and prediction of obstacle motion trajectories, more efficient dynamic obstacle avoidance is achieved. Additionally, an adaptive parameter adjustment mechanism is developed, enabling the algorithm to automatically regulate key parameters according to environmental characteristics. Experimental results demonstrate that the proposed algorithm exhibits superior performance in various complex environments, reducing path planning time by approximately 30% compared to traditional methods while increasing obstacle avoidance success rates to over 98%. Overall, this study provides a novel solution for autonomous navigation of intelligent robots in unknown dynamic environments. Its innovation lies in the organic combination of deep learning with traditional algorithms, as well as the enhancement of robustness and generalization capabilities through an adaptive mechanism, thus laying a solid foundation for future research in related fields.
Keywords：Path Planning; Obstacle Avoidance Algorithm; Deep Reinforcement Learning; Dynamic Environment; Adaptive Parameter Adjustment Mechanism
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能机器人路径规划的研究背景 1
(二) 国内外研究现状与发展趋势 1
(三) 本文研究方法与技术路线 2
二、路径规划算法基础理论 2
(一) 路径规划的基本概念与分类 2
(二) 常见路径规划算法综述 3
(三) 算法性能评价指标体系 3
(四) 路径规划算法的优化方向 4
三、避障算法设计与实现 4
(一) 障碍物检测与建模方法 4
(二) 动态避障算法的核心机制 5
(三) 静态环境下的避障策略分析 5
(四) 避障算法的实际应用案例 6
四、路径规划与避障算法集成研究 6
(一) 路径规划与避障的协同机制 6
(二) 复杂环境下的算法融合方案 7
(三) 实验设计与结果分析 8
(四) 算法改进与未来发展方向 8
结 论 10
参考文献 11
