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When using the basic genetic algorithm for path planning of mobile robots, the problem is that the insertion repair of the path cannot guarantee the feasibility of the solution, and the algorithm is easy to fall into local convergence. In response to the above problems, the use of cell genetic algorithm enhances the versatility of path planning environment modeling, and adds a path smoothing factor to the algorithm fitness function, thereby improving the path of the cell genetic algorithm.Simulation experiments show that compared with the basic genetic algorithm, the length of the robot's driving path is reduced, and the sum of the absolute values of the corners is reduced. A short and smooth path is obtained, and the driving efficiency and stability of the mobile robot are improved. Due to the good implicit migration mechanism of the algorithm, the diversity of the group is maintained during local optimization, which overcomes the premature phenomenon of the algorithm to a certain extent and effectively solves the problem of mobile robot path planning.