改进人工势场与TAS-RRT融合优化算法

Hybrid optimization algorithm of improved artificial potential field and TAS-RRT

针对人工势场法易陷入局部极小值的缺陷,提出旋转速度矢量角以精确定位逃离点,并将TAS-RRT算法与人工势场算法结合进行动态路径规划。采用人工势场法进行避障规划,当陷入局部最小值时,使用基于速度矢量角度差引导的快速随机扩展树算法调节扩张速度,自适应地寻找逃离点,对RRT算法的采样策略和局部规划器进行改进,使搜索过程快速跳出局部极小值,当采样点的旋转速度矢量角满足条件时,切换人工势场进行规划。仿真实验表明,TAS-RRT算法引导路径快速渐进逃离点,与人工势场结合进行运动规划,能适应环境的变化,控制精度和处理速度得到大大提高。

To solve the problem of artificial potential field algorithm(APF)being liable to fall into local minimum,this paper presents a strategy based on angle of rotating speed vectors to accurately locate the position of jump point.Combined with improved APF,the RRT based on transition of angle different of rotating speed vectors(TAS-RRT)can be used to path planning dynamicly.Firstly,artificial potential field algorithm is used in obstacle avoidance motion planning.When falling into local minimum,the sampling strategy of the basic RRT is improved to adaptively seek the target point and the connection mode of the local planner is adjusted to change exploration rate of the tree,so that the search process can be made to jump out of the attractive areas of local minimum point quickly.In addition,APF will be applied again when the the sampling point meets the stop condition of angle of rotating speed vectors.The simulation experiments show that the control precision and velocity of the motion planning are enhanced with TAS-RRT guiding the sampling nodes to the target one gradually and quickly.Besides that,the method can be applied to motion planning of different obstacle environment.