Research on an improved RRT-APF path planning algorithm for tomato harvesting manipulators

To address the problems of low path planning efficiency and excessive redundant nodes in tomato harvesting manipulators, an improved RRT-APF path planning algorithm is proposed. First, a goal-biased strategy is introduced into the standard Rapidly-exploring Random Tree (RRT) framework to reduce search randomness and improve sampling efficiency. The Artificial Potential Field (APF) method is then integrated with a greedy strategy to perform obstacle avoidance and update nodes. Finally, an improved quadratic B-spline curve is applied to optimize the planned path. In a simple obstacle environment, compared with RRT, RRT*, and APF, the proposed algorithm reduces average planning time by 75.51%, 79.31%, and 52.00%, respectively, and reduces average sampling node count by 88.51%, 85.45%, and 95.78% in 2D scenarios. In 3D scenarios, average planning time is reduced by 99.57%, 99.87%, and 85.71%, and sampling node count is reduced by 98.80%, 98.97%, and 97.23%. In a complex obstacle environment, compared with RRT and RRT*, the algorithm reduces average planning time by 29.41% and 70.73%, and sampling node count by 69.23% and 77.11% in 2D scenarios; in 3D complex environments, average planning time is reduced by 99.80% and 99.86%, and average sampling node count is reduced by 99.49% and 99.38%. Experimental results demonstrate that the improved RRT-APF algorithm has significant advantages in path planning speed and efficiency, providing reliable technical support for Cartesian obstacle avoidance of tomato harvesting manipulators.