Anytime synchronized-biased-greedy rapidly-exploring random tree path planning in two dimensional complex environments

摘要

A new synchronized biased-greedy RRT is proposed which leverages the strengths of the biased and greedy RRTs. It combines the advantage of the biased RRT that grows trees towards the goal location, with the ability of the greedy RRT that makes trees traverse the environment in a single iteration. The proposed method achieves performance improvements compared to other RRT variants, not only in computational time but also in the quality of the path. Two enhancements are made to the initial path to relax the sub-optimality of the RRT path; first a path pruning algorithm is executed to eliminate redundant nodes and an anytime strategy is adapted to continuously enhance the quality of the path within the deliberation time.