Navigation of Multiple Robots in Formative Manner in an Unknown Environment using Artificial Potential Field Based Path Planning Algorithm

Abstract

This paper proposes a new algorithm for multiple robots that can navigate from source to goal position in a coordinated and formative manner in an optimum path and time, efficiently avoiding static and dynamic obstacles. The proposed algorithm is applied here to track surveillance for two and four robots from starting point to the goal point in an obstacle environment without collision. This algorithm is inspired by the concept of the Artificial Potential Field (APF) method and behavior-based approximate reasoning. An exhaustive search approach is used to avoid the randomly appeared obstacles, approximately estimate positions of obstacles, thus reaching the surveillance point and then tracking the allotted path for the robots. The robots are governed to move in real-time, maintaining a formation between them to track the assigned path cooperatively. The performance of this algorithm is validated in a real-time and simulation environment using MATLAB/Simulink. It is observed from the results of performance measuring parameters path length and travel time that the proposed algorithm outperforms than existing A*, RRT, and GA algorithm.