Analysis of static-dynamic global path planning for the rosa roxburghii picking robot

Abstract In the context of the Rosa Roxburghii picking robot operating in the Rosa Roxburghii park, path planning represents a critical challenge and a key area of study. To address this, a globally optimized A* algorithm has been developed, which integrates A* (A-star) with the dynamic window approach (DWA) for local optimization. The process begins by incorporating the robot’s positional information into the traditional evaluation function, along with establishing a weight function that balances both cost and heuristic factors. Following this, the conventional eight search directions are reduced to seven, enhancing the efficiency of the path search. To increase the smoothness of the path, unnecessary turning points are eliminated while retaining essential ones for node optimization. A safety domain value is also introduced to further minimize extraneous turns, ensuring comprehensive global path optimization. For effective realtime dynamic obstacle avoidance. The combination of the two algorithms enables online path planning, resulting in a smooth trajectory based on the globally optimal path. Simulation outcomes demonstrate that this approach significantly reduces path length, average turning angle, and execution time, with its effectiveness validated through additional simulation experiments.