When the robot is running in a complex environment with densely distributed obstacles, the DWA (dynamic window approach) algorithm is prone to obstacle avoidance failure or unreasonable planning. In this regard, an improved DWA planning algorithm based on MOPSO(multi-objective particle swarm optimization) was proposed. Based on the establishment of multi obstacle environment coverage model, a method was put forward for judging obstacle-dense areas in complex environments. And the original DWA algorithm was improved by optimizing the sub-evaluation functions. On these basis of the improved MOPSO algorithm, the adaptive change of DWA weight coefficients were transformed into a multi-objective optimization problem. According to the requirements of path planning, the safety distance and speed can be set as the optimization goals, moreover, the corresponding multi-objective optimization model was established. The results of a series of simulations show that this method enables the robot to effectively pass through the dense area of obstacles while taking account of the safety and speed of operation, and has better path planning effect.