Affiliations: [a] Research Scholar, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, U.P., India
| [b] Department of Electronics and Communication Engineering, Noida Institute of Engineering, & Technology, Greater Noida, U.P., India
| [c] Department of Computer Science & Engineering, GNIOT Group of Institutions, Greater Noida, U.P., India
Correspondence:
[*]
Corresponding author. Laxman Singh, Assoc. Professor, Electronics & Communication Engineering, NIET: Noida Institute of Engineering and Technology, E-mail: laxman.mehlawat2@gmail.com.
Abstract: Path planning for robots plays a vital role to seek the most feasible path due to power requirement, environmental factors and other limitations. The path planning for the autonomous robots is tedious task as the robot needs to locate a suitable path to move between the source and destination points with multifaceted nature. In this paper, we introduced a new technique named modified grey wolf optimization (MGWO) algorithm to solve the path planning problem for multi-robots. MGWO is modified version of conventional grey wolf optimization (GWO) that belongs to the category of metaheuristic algorithms. This has gained wide popularity for an optimization of different parameters in the discrete search space to solve various problems. The prime goal of the proposed methodology is to determine the optimal path while maintaining a sufficient distance from other objects and moving robots. In MGWO method, omega wolves are treated equally as those of delta wolves in exploration process that helps in escalating the convergence speed and minimizing the execution time. The simulation results show that MGWO gives satisfactory performance than other state of art methods for path planning of multiple mobile robots. The performance of the proposed method is compared with the standard evolutionary algorithms viz., Particle Swarm Optimization (PSO), Intelligent BAT Algorithm (IBA), Grey Wolf Optimization (GWO), and Variable Weight Grey Wolf Optimization (VW-GWO) and yielded better results than all of these.
Keywords: Meta-heuristic, particle swarm optimization, intelligent BAT algorithm, grey wolf optimization (GWO), modified grey wolf optimization (MGWO), variable weight grey wolf optimization (VW-GWO)
