Aiming at the problems of mobile robot path planning, such as prone to fall into local optimum, slow convergence speed and high cost of searching paths, an improved grey wolf optimizer was proposed for mobile robot path planning. A twodimensional space model of obstacle avoidance path planning related to mobile robots was established, the linear convergence factor of the grey wolf optimizer was transformed into the nonlinear convergence factor, and the grey wolf optimizer algorithm was combined with particle swarm optimizer to give Omega wolf consciousness; collaborative quantization was added to optimize the grey wolf population. 4 types of international test function were used to prove that the improved algorithm had better convergence accuracy and stability. The improved algorithm was applied to the mobile robot path planning, and compared with the particle swarm optimization algorithm and the original gray wolf optimizer. The simulation results verify the effectiveness of the proposed algorithm.