Obstacle Crossing Path Planning for a Wheel-Legged Robot Using an Improved A* Algorithm

In response to the challenges of obstacle avoidance and terrain negotiation encountered by wheel-legged robots in static environments with complex obstacles, this study introduces an enhanced A* path planning algorithm that incorporates a jump-point search strategy, a dynamically weighted heuristic strategy, and a continuous jumping constraint mechanism to facilitate efficient obstacle traversal. The algorithm extends the traditional 8-neighborhood rule to support jumping in the horizontal, vertical, and diagonal directions. A dynamic, weighted heuristic is introduced to adaptively adjust heuristic weights, guide the search direction, improve efficiency, and reduce detours. Redundant point removal and Bézier curve smoothing were employed to enhance path smoothness, whereas the continuous jumping constraint limited the jump frequency and improved motion stability. The results validate that-relative to the standard A* algorithm, which achieves a 73.7% reduction in path nodes (from 54 to 16)-85% fewer search nodes (from 542 to 78) and a planning time of 0.0032 s were achieved while also enhancing performance in crossing complex structures. This enhances the capability of wheel-legged robots to perform real-time path planning in structurally complex yet static environments, thereby improving their autonomous navigation efficiency.