Sampling-Based Optimal Motion Planning With Smart Exploration and Exploitation

Abstract

In this article, a novel sampling framework with smart exploration and exploitation (sampling-SEE) is proposed for sampling-based optimal motion planning problem. Exploration represents uniform sampling in the configuration space to find a path with better performance than the previous one. Exploitation denotes biased sampling in the surrounding region of the current path to rapidly improve this path. Sampling-SEE fully utilizes the current path cost and location information to decrease the space for exploration and increase the capability for exploitation. Additionally, a biasing ratio is introduced to dynamically adjust the sampling proportion for the two procedures. Sampling-SEE can be easily integrated into the single or batch sampling-based optimal anytime planners to improve the sampling efficiency and accelerate the convergence. Meanwhile, the property of asymptotic optimality remains unchanged. Simulation experiments are carried out in various environments to verify the effectiveness of the proposed framework.