Abstract: In view of the dynamic constraints of actual archive storage spaces, conventional motion planning algorithms are inadequate for fast online planning. Research was conducted in two directions: planning speed and online planning in dynamic spaces. First, a novel rapidly-exploring random trees (RRT) method was proposed. By using pruning and path-refinement strategies, it significantly reduced the computation of unnecessary nodes and redundant path motions. Second, an artificial potential field method was integrated with the RRT algorithm. During new-node expansion, node sampling was influenced by a Gaussian distribution whose expected value was the current resultant potential field force. This improved the expansion capability while enabling online planning for dynamic obstacles. Simulation results show that the new RRT algorithm achieves high expansion efficiency, and the hybrid planning algorithm demonstrates superior performance in dynamic planning and exploration efficiency.