Targeting the mobility assistance needs of people with walking difficulties in environments dense with obstacles like museums and exhibition halls, this study proposes an autonomous navigation path planning method. The method combines a global A* algorithm with a local dynamic window approach DWA. First, an improved A* algorithm designs an optimized heuristic function and a path smoothing strategy. This improved algorithm generates the global optimal path. Second, the study employs an adaptive weighting DWA algorithm to handle dynamic environments. This DWA variant adds a global path evaluation function and a braking distance evaluation function to the original DWA. These additions significantly enhance safety and motion continuity within complex, narrow passages. To verify effectiveness, the study constructs a test platform simulating a museum environment in a laboratory setting. Evaluation utilizes metrics including trajectory smoothness, collision rate and number of traversed nodes. Experimental results demonstrate that compared to traditional A* and original DWA algorithms, this method achieves global path optimality. It also increases the dynamic obstacle avoidance success rate. Furthermore, the method reduces trajectory jitter amplitude. This approach provides reliable technical support for the safe movement of special groups in constrained indoor environments.