Graft-RRT: An efficient method to find and converge global paths through intermediate points

Abstract

In this paper, an improved algorithm for rapidly exploring random trees (RRT) is proposed. This method is called Graft-RRT. the main advantages of this method are the low cost of the explored global path and the fast exploration speed. Based on the idea of bi-directional random exploration tree (B-RRT), the algorithm generates a third random tree, Graft-Tree, by finite random sampling of the feasible region between the starting point and the ending point, and then extends the starting point random tree and the ending point random tree. When the global path is obtained, the global path cost is reduced by compressing the effective cost on the path several times. The simulation results show that the global path cost is significantly lower than B-RRT and RRT*, and the time consumed is lower than RRT and RRT* in the same complex and simple environment.