复杂环境中基于人工势场优化算法的最优路径规划

本文提出一种基于人工势场优化的路径规划方法.把人工势场的路径规划结果作为先验知识，对蚁群算法进行初始化，提高了蚁群算法的优化效率;另一方面，机器人的路径也同时得到优化，克服了人工势场法的局部极小问题．仿真实验结果表明，该方法在复杂环境中能有效地实现最优路径规划；并提供了一种把传统规划方法和统计优化相结合、提高规划效率的可行思路．

OPTIMAL PATH PLANNING IN COMPLEX ENVIRONMENTS BASED ON OPTIMIZATION OF ARTIFICIAL POTENTIAL FIELD

A path planning method based on artificial potential field optimization is proposed. The ant algorithm is initialized by the planning result of the artificial potential field method as the prior knowledge, which improves the algorithm's efficiency. On the other hand, the path obtained by the artificial potential field method is optimized by the ant algorithm, which overcomes the local minima problem in the artificial potential field method. Results of computer simulation experiment indicate that the method can implement optimal path planning in complex environments. In order to improve the planning efficiency a feasible idea of combining traditional planning methods with statistic optimization is also presented.