Path Planning of Mobile Robot Based on TGWO Algorithm

The traditional grey wolf algorithm achieves low convergence efficiency and tends to get struck in local extremum in solving path planning problems for mobile robots. As a result, this paper proposes an improved grey wolf algorithm(Tent-initialized grey wolf optimization, TGWO)based on the population initialization of Tent chaotic mapping, Firstly, the population initialization method based on Tent chaotic mapping is adopted to enrich the diversity of the population, which can improve the convergence speed. Secondly, the improvement strategy based on exponential convergence factor is proposed to better fit the search process of the grey wolf, and the global exploration and local exploitation capabilities of the algorithm are balanced by improving the control parameter H. Finally, the dynamic weight factor and the fitness scale coefficient are integrated to update the individual positions of the grey wolves, so as to improve the independent searching ability of the individual, and prevent the algorithm from getting trapped into local optimum. To verify the effectiveness of the proposed algorithm, experiments are carried out by comparing the TGWO, traditional GWO and three improved typical algorithms for global path planning simulation using eight standard test functions and three sets of grid environments with different complexities. The results are as follows: 1)For both unimodal and multi-modal functions, the convergence performance and optimization accuracy of TGWO algorithm are better than others; 2)Under the simulation scenes, compared with the traditional GWO, each improved strategy proposed for TGWO algorithm effectively improves the performance of path optimization; 3)TGWO are superior to other algorithms in terms of the 4 indicators including the average path length, standard deviation of path lengths, average number of iterations and average time for optimization. All these verify the superiority and robustness of TGWO algorithm in path optimization. © 2022 Xi'an Jiaotong University. All rights reserved.