A Generalized Voronoi Diagram-Based Efficient Heuristic Path Planning Method for RRTs in Mobile Robots

Abstract

The rapidly exploring random tree and its variants (RRTs) have been widely adopted as the motion planning algorithms for mobile robots. However, the trap space problem, such as mazes and S-shaped corridors, hinders their planning efficiency. In this article, we present a generalized Voronoi diagram (GVD)-based heuristic path planning algorithm to generate a heuristic path, guide the sampling process of RRTs, and further improve the motion planning efficiency of RRTs. Different from other heuristic algorithms that only work in certain environments or depend on specified parameter setting, the proposed algorithm can automatically identify the environment feature and provide a reasonable heuristic path. First, the given environment is initialized with a lightweight feature extraction from the GVD, which guarantees that any state in the free space can be connected to the feature graph without any collision. Second, to remove the redundancy of feature nodes, a feature matrix is proposed to represent connections among feature nodes and a corresponding feature node fusion technique is utilized to delete the redundant nodes. Third, based on the GVD feature matrix, a heuristic path planning algorithm is presented. This heuristic path is then used to guide the sampling process of RRTs and achieve real-time motion planning. The proposed GVD feature matrix can be also utilized to improve the efficiency of the replanning. Through a series of simulation studies and real-world implementations, it is confirmed that the proposed algorithm achieves better performance in heuristic path planning, feature extraction of free space, and real-time motion planning.