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.