Multi-objective optimization for solving mobile robot path planning problem employing hybridization of algorithms

Abstract

Mobile Robot Path Planning problem (MRPPP) is the most prominent research area employed in different real-time environments. In this article, a hybrid strategy is employed and validated with different environments for the path planning of mobile robots. The Artificial Potential Field (APF) algorithm and Multi-objective Genetic Algorithm (MOGA) are hybridized to solve an MRPPP. In the proposed hybrid methodology, the implementation is accomplished in three phases. To generate the initial population, an Artificial Potential Field (APF) algorithm is applied to discover all viable routes in an environment between the start and destination points. The collision-free paths are generated by determining the artificial force produced by obstacles and the target. The population-based evolutionary algorithm is employed to derive an optimal solution path from the initial population consisting of candidate paths. The selection operator is used to select the qualified subpopulation to the next generation to converge into the optimal solution. The genetic operators such as two-point crossover and mid-point mutation are exercised which are specific to the path planning problem.