A 2D voronoi-based random tree for path planning in complicated 3D environments

Abstract

Path planning in complicated 3D environments with narrow passages and rooms is a challenging problem, which is usually time consuming for geometric searching methods or incomplete for sampling-based methods. Focusing on these issues, this paper presents a new algorithm named 2D Voronoi-based Random Tree which combines the completeness of the voronoi diagram based 2D path searching methods and the efficiency of sampling based path planning methods. In this method, 2D voronoi diagram is created and used to guide the growth of random trees in complicated 3D environments. In each iteration of random trees growth, a new node on voronoi edges is selected by moving forward to the target with a fix step length along edges. And then, the 3D particles are distributed locally around this node and be selected according to a cost function for the random trees growth. By doing so, this method can find a valid path in complicated 3D environments with narrow passages and rooms while improving its efficiency and completeness. To demonstrate its effectiveness, efficiency and robustness, the proposed method is examined and compared with RRT algorithm in various practical complex 3D environments.