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The development of information technology in the Internet of Things has made logistics robots face problems such as local extreme traps and algorithm convergence in current motion path planning. In addition, traditional motion path planning methods are difficult to meet the complex and changing logistics environment. Therefore, it is urgent to explore active and effective motion planning methods. Based on this, the study utilizes the grey wolf optimization algorithm for global path planning and mixed path analysis, and introduces collaborative quantum and improved artificial potential fields for improvement, achieving the update of the convergence factor of the algorithm and the execution of crossover strategies. The simulation results of logistics robots show that the algorithm exhibits good convergence in the test function, and the reduction rate of search path length in a single obstacle fruit is about 5%, with an average cost consumption of 23.65. It can detect dynamic obstacles well and effectively escape local minimum traps. And its running time in dynamic environments has been shortened by 46.37%, with good optimization and obstacle avoidance performance. The path planning algorithm proposed in the study can effectively provide reference ideas and value for the development of the logistics industry and automated scheduling.