Abstract: A method of global path planning for mobile robot in static environment is proposed based on immune genetic algorithm.First the neural network model of environmental information is constructed in the working space for robot,also the relationship between the collision-free path and the neural network output is established based on this model,then the collision-free demand and the path optimization demand are fused to a simple fitness function for immune genetic algorithm.Antibody selective probability is expressed as a fusion function based on antibody vector distance and antibody density,the antibody diversity and maturation convergence are ensured synchronously.Finally the simulation results show that the performance is improved by contrast with genetic algorithm,also simulation results demonstrate that the algorithm for global path planning is feasible and valid.