Solving the multi-objective path planning problem for mobile robot using an improved NSGA-II algorithm

Abstract

Path planning is one of the most concerned problems in mobile robotics. Given the NP-hard nature of path planning problems, the non-dominated sorting genetic algorithm (NSGA-II), as one of the outstanding evolutionary algorithms with robust optimization capabilities, is a good candidate to deal with them. In this study, an improved NSGA-II (INSGA-II) is proposed to solve the multi-objective path planning problems, in which path length, path safety and path smoothness are optimized simultaneously. First, a grid-based environment model is created, and each feasible solution is encoded as a discrete ordered point set. Second, a hybrid initialization strategy is employed to enrich the diversity of initial population. Third, besides the usual selection, crossover and mutation operators, problem-specific evolutionary operators are developed. In addition, an adaptive variable neighborhood local search strategy is designed to increase the exploitation ability of the algorithm. Moreover, a hybrid global search strategy is applied to further improve the algorithm’s the exploration ability. Finally, the proposed INSGA-II is compared with six state-of-the-art algorithms on 16 instances of four representative maps. Simulation results indicate that INSGA-II can solve the multi-objective path planning problems with high efficiency.