Obstacle Avoidance Path Planning Based on Target Heuristic and Repair Genetic Algorithms

Luo Jun-Qi, Wei-Che Chien
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引用次数: 2

Abstract

Using genetic algorithm (GA) to optimize the mobile robot path planning has the disadvantages of low initial population generation efficiency and low initial population quality, especially under large size and complex environment model in grids. In order to overcome this problem, a novel methodology contains a target heuristic operator and a reparation operator is proposed in this paper. In this study, the maps consist of the obstacles areas and the feasible areas are decomposed by grids. These two operators are integrated into the GA and applied to acquire the collision-free shortest path in a static two-dimension environment. The experimental data show that the methodology can decrease significantly the random search time for generating the initial population and improve the quality of the initial population generation. Results suggested that the proposed requires a shorter amount of time and possesses a better global searching performance, compared with the conventional methods.
基于目标启发式和修复遗传算法的避障路径规划
采用遗传算法优化移动机器人路径规划存在初始种群生成效率低、初始种群质量低等缺点,特别是在大尺寸、复杂的网格环境模型下。为了克服这一问题,本文提出了一种包含目标启发式算子和修复算子的新方法。在本研究中,地图由障碍物区域组成,可行区域通过网格分解。将这两种算子集成到遗传算法中,用于获取静态二维环境下的无碰撞最短路径。实验数据表明,该方法可以显著减少生成初始种群的随机搜索时间,提高初始种群生成的质量。结果表明,与传统方法相比,该方法的搜索时间更短,具有更好的全局搜索性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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