Probabilistic Roadmap and Artificial Bee Colony Algorithm Cooperation For Path Planning

Abstract

Path planning for autonomous exploration is a promising topic due to latest development on robot systems. Finding the safest and the shortest path which promise minimum energy consumption is the key problem for path planning. In this study we developed a new approach of hybrid path planning for emerge call back procedure during autonomous exploration task. In this situation the robot used for autonomous exploration is called back to the start point immediately. The path planning algorithm should find the best path accordingly to energy, road smoothness, road safety, path length and task duration constraints. During exploration we can use both global and local path planning methods but when call back procedure operated, because of we have already an explored map and a path, use of heuristic optimization methods is more reasonable in order to tune optimal path. In this work, a new hybrid algorithm with cooperation of Artificial Bee Colony algorithm and Probabilistic Roadmap algorithm is proposed. The proposed algorithm uses an object function for optimum path which involves shortest, safest and more smooth path. The Proposed algorithm has tested on Matlab with Robotic System Toolbox software, satisfactory results has been achieved.