Target-biased informed trees: sampling-based method for optimal motion planning in complex environments

Abstract Aiming at the problem that progressively optimized Rapidly-exploring Random Trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional complex environments. In this paper we present Target-biased Informed (TBIT*), an improved RRT* path planning based on target-biased sampling strategy heuristic optimization strategy. The adopts combined target bias phase finding initial to guide random tree grow rapidly toward direction, thereby reducing generation nodes improving algorithm; after is searched, used optimize instead optimizing tree, can benefit from useless calculations, improve capability algorithm. experimental results show proposed article changes randomness certain extent, environments have been significantly improved, indicating feasible efficient.