Path planning for mobile robots using Morphological Dilation Voronoi Diagram Roadmap algorithm

Abstract

This paper presents Morphological Dilation Voronoi Diagram Roadmap (MVDRM) algorithm to address unsafe path computation accompanied by high time and space computation complexity problems of roadmap path planning methods in complex environments for mobile robots. Morphological dilation was employed to inflate the obstacles in the environment of the robot before computing the path to ensure safe path computation for the robots. To reduce time and space complexities in computing the path, a scale factor is introduced to provide sparse and uniform distribution of sample nodes in the environment of the robot for the computation of the roadmap. The purpose of this technique is to reduce the sample nodes and computation time to provide fast-path computation to aid robots to make quick decisions to avoid collision with obstacles. Simulation results using maps of different complexities demonstrate better performance of the proposed method compared to Probabilistic Roadmap (PRM) and conventional VD methods in terms of safe path computation as well as time and space computation complexities. Results indicate that the proposed method is 1.69 and 7.03 times faster than conventional VD and PRM methods, respectively. The proposed method is 88.32 times faster than VD and 1.08 times faster than PRM. The path computation success rate is 96.3% better than the PRM method. MVDRM is a promising roadmap path planning method for computing a safe and quick path for autonomous vehicles.