Path Planning of Mobile Robots using the Artificial Potential Field Method and the Harris Hawks Metaheuristic Algorithm

Path planning of mobile robots is one of the important issues in the field of robotics. Also, optimizing the path length and crossing the local minima traps are the basic and up-to-date challenges in this field. One of the important methods in path planning of these robots is the artificial potential field method. Because it is based on simple mathematical calculations. One of the most important disadvantages of this method is getting trapped in local minima situations. One approach for solving the problem of local minima is to use optimization methods to find suitable coefficients (attractive and repulsive) and step length that can solve local minima and optimize the path length. The Harris Hawks algorithm is a powerful and new meta-heuristic algorithm in the field of optimization that is based on population and inspired by the life of Harris Hawks in nature. This algorithm has been able to prove its superiority over similar optimization methods in finding the optimal solution, faster convergence, lower computational time and not trapping in local minima. This method has not been used in the path planning of mobile robots. In order to eliminate the disadvantages of the artificial potential field method and to optimize the path length, the Harris Hawks algorithm has been used in this paper. The simulation results showed that the combination of the artificial potential field method and the Harris Hawks algorithm can solve the local minima problem and optimize the path length, increase the path efficiency and reduce the convergence time.