A Parallel Algorithm Combining Improved-Connect-RRT and JPS with Closed-operation
Qianyi Zhang, Lei Zhou, Yingli Zhao, Rui Cao, Jingtai Liu · 2020
How to plan a path in big and complex environments has always been a key issue. In this paper, we proposed Global Sampling and Local Search Trees(GSLST), a parallel planning algorithm. It uses Connect-RRT to grow two global sampling trees in the entire map while using Closed-Operation and Jump Point Search(JPS) to extract narrow paths and create Local Dynamic Link Trees. GSLST shows both the fastness of the sampling-based algorithm for planning in a wide range of environment and the completeness of the search based algorithm for planning in complex environment with narrow paths. The simulations demonstrate that GSLST is faster than that of sampling-based and search-based algorithms in big complex environments.