Aimed at the problems that robot path planning problems being solved by the standard gray wolf algorithm, initial parameters are strong in dependence, lack of variety, and liable to sink into local extreme value, a Logistic-Tent based Grey Wolf Optimizer (LTGWO) is proposed. Firstly, by an elite method, the Logistic-Tent composite chaotic mapping is in combination with inverse learning to improve the population distribution. And then, by introducing the sigmoid function, the factor of concentration and balances between global exploration and local exploitation is adjusted, while the improved control parameters are fitted even more in line with the actual hunting behavior. Lastly, Proportional weights being in company with the changes of adaptable value are added to enhance the search ability of individual gray wolves. A population culling strategy is adopted to eliminate the individuals with poor fitness, promoting evolution. Three different groups of raster maps are selected for the experiments,and the experimental results show that the average path length and the standard deviation of the path length generated by the LTGWO algorithm are better than the comparison algorithm.