Hybrid metaheuristic approach for robot path planning in static environment

Abstract

General Path Planning (GPP) is a challenging problem in the field of mobile robotics due to its complexity. The robots must selected their path from the starting point to the target point with the lowest possible distance, in the least possible time, and with the fewest possible turns and movements. The aim of this research is to achieve best path planning of a mobile robot using the hybrid algorithm. This paper proposed heuristic algorithms for determining the optimal pathway of the robot in a static environment. These algorithms are the Particle Swarming Optimization (PSO), the Ant Colony Optimization (ACO), and the hybrid approach of ACO&PSO. They used to obtain the perfect path for the robot as well as to avoid hitting obstacles that it encounters through its path. Initially, each of the two algorithms is implemented separately in a static environment, and then the hybrid one is implemented. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The results obtained for the hybrid algorithm were better than the PSO and ACO algorithms.