论文标题
一种新型的基于知识的遗传算法，用于在复杂环境中用于机器人路径计划
A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments
论文作者
论文摘要
在本文中，提出了一种基于知识的新型遗传算法，用于在非结构化复杂环境中移动机器人的路径规划，其中提出了五个特定于问题的操作员以进行有效的机器人路径计划。提出的遗传算法将机器人路径计划的领域知识纳入其专业操作员，其中一些也结合了局部搜索技术。提出了一种独特而简单的表示机器人路径的表示，并开发了一种简单但有效的路径评估方法，可以准确检测到碰撞，并很好地反映了机器人路径的质量。所提出的算法能够在静态和动态复杂环境中找到近乎最佳的机器人路径。通过模拟研究证明了所提出算法的有效性和效率。通过比较研究证明了专业遗传算子在解决机器人路径计划问题的拟议遗传算法中的不可替代的作用。
In this paper, a novel knowledge-based genetic algorithm for path planning of a mobile robot in unstructured complex environments is proposed, where five problem-specific operators are developed for efficient robot path planning. The proposed genetic algorithm incorporates the domain knowledge of robot path planning into its specialized operators, some of which also combine a local search technique. A unique and simple representation of the robot path is proposed and a simple but effective path evaluation method is developed, where the collisions can be accurately detected and the quality of a robot path is well reflected. The proposed algorithm is capable of finding a near-optimal robot path in both static and dynamic complex environments. The effectiveness and efficiency of the proposed algorithm are demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed genetic algorithm for solving the robot path planning problem is demonstrated through a comparison study.
