Abstract: In view of problems such as unreasonable path planning, slow planning speed and poor real -time performance when mine mobile robots use traditional dynamic window algorithm to plan path in complex environment, a dynamic window algorithm of mine mobile robot based on membrane computing and particle swarm optimization was proposed. The traditional dynamic window algorithm is optimized by using randomness of particle swarm optimization and distributed parallel computing ability of membrane computing. In the dynamic window algorithm, the velocity limit space of mine mobile robot is transformed into coordinate space, and the velocity coordinate of the mine mobile robot is regarded as particle position. The speed sampling mode is changed from uniform equal sampling to random sampling, the sample particles are evenly distributed to each basic membrane. The exchange between membranes and the renewal mechanism of particles in membrane are used to evaluate the renewal of particles. The optimal speed is output continuously. The path planning of the mine mobile robot is based on the optimal output speed in continuous time interval. The simulation results show that the algorithm optimizes the speed limit region of mine mobile robot by membrane computing and particle swarm optimization algorithm, and improves the randomness of speed sampling and the rationality of planning path. Compared with the traditional dynamic window algorithm, the proposed algorithm can not only reduce the number of planning steps and the evaluation times of each step, but also shorten the planning path length by 7% -10% and the planning time by 9% -32%, and can adapt to the special environment with U -shaped obstacles.