一种新基于混沌优化算法的机器人路径规划方法

提出了一种基于混沌优化算法的机器人路径规划方法,即混沌人工势场法，该方法能够在动态环境下实时、有效地产生避碰局部最优路径,避免了传统人工势场法容易陷入局部最优和在比较靠近的两个障碍物之间找不到通道的缺陷.仿真试验表明:提出的方法具有较强的路径规划能力，克服了传统人工势场法的缺点,具有较强的实用性.

Path Planning Method for Robot Based on Chaotic Optimization Algorithm

This paper presents a path planning method based on chaotic optimization algorithm for robot, which is named chaotic artificial potential field method (CAPFM). The algorithm can generate an optimal local path for obstacle avoidance more efficiently in real time in the dynamic environments, prevent local optimum, and overcome the problem that there is no passage between closely spaced obstacles caused by directly applying the conventional artificial potential field method. The simulation results demonstrate that the proposed method performs path planning very well, overcomes the drawbacks of the conventional artificial potential field methods, and has good practicality.