Robot Path Planning with GD-RRT-APF Fusion

This paper proposes a robot path planning algorithm based on artificial potential field combined with fast search tree (GD-RRT-APF) under the goal orientation. The algorithm adds the goal orientation heuristic in the fast search tree to reduce the random expansion of the search path, and combines the advantages of the potential field distribution around the target point to improve the obstacle avoidance and path optimality of the robot path planning. Through simulation analysis and experimental verification, and compared with the traditional RRT algorithm, the planning path is shorter, the time consumption is increased by 3.63 s, and the computational efficiency is higher. The results show that the algorithm effectively avoids collisions, reduces randomness of the traditional RRT algorithm, it can quickly generate smooth, short distance paths, and efficiently complete the path planning task.