Abstract: To further improve the efficiency of intelligent substation inspection robots,this paper proposes an improved grey wolf optimizer for inspection robot path planning,aiming at the problem that the traditional grey wolf optimizer is prone to local optima and has a low convergence efficiency.Based on the impact of different parameters of the grey wolf optimizer on the algorithm performance,a simulation analysis is conducted on the performance of the optimizer in path planning for intelligent substation inspect robots based on the convergence function and population weight parameters respectively after the mixed improvement of the optimizer is conducted.The simulation results show that the mixed improved grey wolf optimizer not only shortens the optimized path of the intelligent substation inspection robot but also improves its path planning efficiency.