摘 要
路径规划问题是人工智能与自动化领域的重要研究课题，启发式搜索算法凭借其高效性与灵活性成为解决该问题的关键方法。在复杂环境下的路径规划中，传统算法面临计算量大、实时性差等挑战，启发式搜索算法通过引入启发式函数有效改善了这一状况。本研究旨在探讨启发式搜索算法在路径规划中的应用，以提高路径规划的效率和准确性。基于A*算法框架，提出了一种改进的启发式评估函数，结合环境特征自适应调整权重系数，使算法能够更好地适应不同场景需求。实验结果表明，在多种典型环境中，改进后的算法不仅显著降低了搜索节点数量，提高了搜索速度，而且生成的路径更加平滑合理，具有更高的实用价值。此外，针对动态障碍物场景，引入预测机制进一步优化了算法性能，实现了对移动障碍物的有效规避。本研究创新性地将环境特征融入启发式函数设计，并提出了动态场景下的优化策略，为启发式搜索算法在实际路径规划任务中的应用提供了新的思路和技术支持，拓展了启发式搜索算法的应用范围，提升了其在复杂环境下的适用性和鲁棒性。
关键词：路径规划；启发式搜索算法；A*算法；启发式评估函数；动态障碍物预测
Abstract
Path planning is a critical research topic in the fields of artificial intelligence and automation, where heuristic search algorithms have emerged as key methods due to their efficiency and flexibility. Traditional algorithms face challenges such as high computational demands and poor real-time performance in complex environments, whereas heuristic search algorithms improve this situation by incorporating heuristic functions. This study investigates the application of heuristic search algorithms in path planning to enhance both efficiency and accuracy. Based on the A* algorithm fr amework, an improved heuristic evaluation function is proposed, which adaptively adjusts weight coefficients according to environmental characteristics, thereby better accommodating diverse scenario requirements. Experimental results demonstrate that in various typical environments, the improved algorithm not only significantly reduces the number of search nodes and increases search speed but also generates smoother and more reasonable paths with higher practical value. Furthermore, for dynamic obstacle scenarios, a prediction mechanism is introduced to further optimize algorithm performance, achieving effective avoidance of moving obstacles. Innovatively, this research integrates environmental features into the design of heuristic functions and proposes optimization strategies for dynamic scenes, providing new insights and technical support for the application of heuristic search algorithms in practical path planning tasks. This approach expands the application scope of heuristic search algorithms and enhances their applicability and robustness in complex environments.
Keywords：Path Planning；Heuristic Search Algorithm；A* Algorithm；Heuristic Evaluation Function；Dynamic Obstacle Prediction
目 录
摘 要 I
Abstract II
引 言 1
第一章 启发式搜索算法基础 2
1.1 启发式搜索算法概述 2
1.2 算法核心原理分析 2
1.3 常见启发式函数类型 3
1.4 算法性能评估指标 3
第二章 路径规划问题建模 5
2.1 路径规划问题定义 5
2.2 环境模型构建方法 5
2.3 约束条件与优化目标 6
2.4 问题复杂度分析 6
第三章 启发式搜索在路径规划中的应用 8
3.1 经典应用场景实例 8
3.2 动态环境下的适应性 8
3.3 多目标路径规划实现 9
3.4 特殊约束条件处理 9
第四章 算法改进与优化策略 11
4.1 搜索效率提升方法 11
4.2 启发式信息优化设计 11
4.3 并行计算技术应用 12
4.4 实时性与鲁棒性增强 12
结 论 14
参考文献 15
致 谢 16
