Omnidirectional Robot Path Planning Based on Improved RRT Algorithm
The fast expanding random tree(RRT)algorithm takes a long time and length to generate the path in the global path planning,and lacks the target guidance in the sampling.In this paper,a target biased sampling strategy is proposed,which promotes random sampling to target points by introducing a virtual force field composed of initial points,end points,obstacles and random points.For the influence of the distance and proximity of obstacles,the threshold of the influence of obstacles was set,the adaptive coefficient is introduced,the angle constraint was added in the expansion step,and different expansion steps were selected according to the angle of the target point.Aiming at the path length redundancy of the traditional RRT algorithm in the global path planning,the redundant node elimina-tion strategy was introduced to shorten the path length.Finally,the greedy strategy was added to reduce the number of unnecessary sampling nodes.Firstly,the simulation was carried out in matlab2018a,and then the experiment was car-ried out in the actual environment.The experimental results show that the path search time,the number of inflection points and the path distance are significantly improved.
NETL
NSTL
万方数据
维普
