人工势场法局部极小值优化方法研究

Study on Local Minimum Optimization Method of Artificial Potential Field Method

摘要: 人工势场法是目前路径规划常用的算法之一，但其存在的局部最小值问题会导致智能移动机器人无法到达目标点。本文针对人工势场法局部最小值优化的三种算法展开研究。文章选择了算法运行平均时间和成功到达目标点的成功率作为研究对象，通过设置三种优化算法在不同障碍物下运行，观察不同设置情景下算法运行时间和成功率的变化，得到这三种优化算法的各自适合的障碍物环境。文章为这三种优化算法的应用提供了参考依据。

Abstract: Artificial potential field method is one of the commonly used algorithms for path planning, but its local minimum problem will cause the intelligent mobile robot to fail to reach the target point. In this paper, three algorithms for local minimum optimization of artificial potential field method are studied. In this paper, the average running time of the algorithm and the success rate of successfully reaching the target point are selected as the research objects. By setting three optimization algorithms to run under different obstacles, the changes of the running time and success rate of these algorithms under different setting scenarios are observed, and the obstacle environment suitable for each of the three optimization algorithms is obtained. This paper provides a reference for the application of these three optimization algorithms.