Research on 3D Obstacle Avoidance Path Planning for Apple Picking Robotic Arm

Abstract

To address the challenges of obstacle avoidance and low efficiency by robotic arms during apple picking, this paper proposes an improved informed-RRT* motion planning algorithm to improve path planning performance. By integrating the artificial potential field method, the unique location points within the planning space are identified. Segmenting the path planning further accelerates the path searching efficiency. The algorithm includes target bias and cosine offset strategies, along with bidirectional planning to improve planning efficiency, enhancing the purposefulness of informed-RRT* sampling. Spatially expanding sampling by dynamic step size enhances the robustness of path indexing in complex obstacle environments. The algorithm is compared with RRT* and in-formation-RRT* in several scenarios. The experimental results show that compared to RRT* and IRRT*, the average path cost of the optimization algorithm is reduced by 31.565 mm and 14.935 mm and the average search time is reduced by 7.18 s and 4.33 s. In the complex two-dimensional simulation experiment, compared to RRT* and IRRT*, the average path cost of the optimization algorithm is reduced by 362.4 mm and 343.5 mm and the average search time is reduced by 5.49 s and 1.54 s. In the 3D simulation, compared to RRT* and IRRT*, the average path cost is reduced by 1110.17 mm and 469.97 mm and the average search time is reduced by 37.82 s and 11.26 s. The optimization algorithm effectively reduces the total length of the picking path and the path planning time. The research results provide a reference for apple picking robots to perform collision-free picking tasks.