Hybrid optimization algorithm of improved artificial potential field and TAS-RRT
Xiaohui Xu, Zhang Jinlong · DOAJ (DOAJ: Directory of Open Access Journals) · 2018
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.