复杂环境下改进APF的机器人路径规划

针对移动机器人在有大型障碍物和运动空间相对狭窄的复杂环境中，人工势场法（APF）容易出现反复震荡、路径规划时间较长以及大型障碍物附近避障困难的问题，提出了在结合边缘探测法的APF路径规划基础上，加入自适应动态步长调整算法来克服APF的上述缺陷，实现移动机器人在复杂环境下的平滑路径规划，在确保路径近似最优的同时提高APF算法的收敛速度和路经规划的避障性能。实验结果证明了上述方法的有效性。

Path planning for mobile robot based on improved Artificial Potential Field method incomplex environment

When the obstacles are large, or the complex environment space is relatively narrow, Artificial Potential Field method （APF） is prone to appear repeated shocks, long time planning and obstacle avoidance of difficulty nearby the large obstacles. This paper presents an adaptive dynamic step length adjustment method based on the APF path planning which is combined with the edge detection method to overcome the proposed defects of APF, achieving mobile robot smooth path planning in the complex environment. Hence it can not only improve APF algorithm convergence speed and the safety of path planning, but at the same time ensure the approximate optimum path. Experiments are carried out by simulation to verify the effectiveness of the afore- mentioned methods.