摘要
随着机器人技术的快速发展，路径规划作为机器人自主导航的核心问题，其优化效率与精度成为研究热点。传统路径规划算法在复杂环境下面临计算效率低、易陷入局部最优等挑战，而遗传算法因其全局搜索能力和鲁棒性，为解决上述问题提供了新思路。本研究旨在基于遗传算法设计一种高效的机器人路径规划优化方法，以提升路径规划的适应性和可靠性。具体而言，通过引入自适应交叉与变异策略改进遗传算法性能，并结合启发式距离评估函数对种群初始化进行优化，从而加速收敛过程并提高解的质量。实验结果表明，该方法能够在多种复杂场景中生成平滑、安全且长度较短的路径，相较于传统算法表现出更优的全局搜索能力和更高的计算效率。此外，本研究提出的自适应参数调整机制有效缓解了遗传算法中参数敏感的问题，显著提升了算法的稳定性和适用范围。综上所述，本研究不仅为机器人路径规划提供了一种创新性的解决方案，还为进一步探索智能优化算法在机器人领域的应用奠定了理论基础。
关键词：机器人路径规划；遗传算法；自适应优化；全局搜索能力；启发式距离评估函数
Abstract
With the rapid development of robotics technology, path planning, as the core issue of robot autonomous navigation, has become a research hotspot in terms of optimizing its efficiency and accuracy. Traditional path planning algorithms face challenges such as low computational efficiency and susceptibility to local optima in complex environments, whereas genetic algorithms, with their global search capability and robustness, offer a novel approach to addressing these issues. This study aims to design an efficient optimization method for robot path planning based on genetic algorithms to enhance the adaptability and reliability of path planning. Specifically, the performance of the genetic algorithm is improved by incorporating adaptive crossover and mutation strategies, while the initialization of the population is optimized through a heuristic distance evaluation function, thereby accelerating the convergence process and improving solution quality. Experimental results demonstrate that this method can generate smooth, safe, and relatively short paths in various complex scenarios, exhibiting superior global search capabilities and higher computational efficiency compared to traditional algorithms. Moreover, the adaptive parameter adjustment mechanism proposed in this study effectively alleviates the parameter sensitivity issue in genetic algorithms, significantly enhancing their stability and applicability. In summary, this research not only provides an innovative solution for robot path planning but also lays a theoretical foundation for further exploring the application of intelligent optimization algorithms in the field of robotics.
Keywords：Robot Path Planning; Genetic Algorithm; Adaptive Optimization; Global Search Ability; Heuristic Distance Evaluation Function
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、遗传算法基础及其应用 2
(一) 遗传算法的基本原理 2
(二) 遗传算法在路径规划中的优势 3
(三) 遗传算法的关键参数设置 3
三、机器人路径规划优化模型构建 4
(一) 路径规划问题的数学描述 4
(二) 基于遗传算法的优化模型设计 5
(三) 模型适应度函数的定义与改进 5
四、实验设计与结果分析 6
(一) 实验环境与参数配置 6
(二) 优化效果评估指标体系 6
(三) 实验结果对比与分析 7
结 论 8
参考文献 9
