PATH PLANNING RESEARCH ON GRAPE PICKING ROBOTIC ARM BASED ON IMPROVED RRT ALGORITHM

Abstract

The robot's operation in a grape orchard environment is often disrupted by obstacles such as vines and leaves, resulting in low fruit picking efficiency. To achieve stable obstacle avoidance, an improved RRT algorithm based on global adaptive step size and target-biased sampling was developed. First, the kinematic equations of the grape-picking robotic arm were established using the PoE method, and both forward and inverse kinematics calculations were performed to determine the robot's workspace. Then, to address the issues of lack of target orientation and other shortcomings in the traditional RRT algorithm when planning collision-free paths, dynamic updating and global adaptive step size strategies were proposed. Simulation experiments conducted using MATLAB software demonstrated that our improved RRT algorithm, compared to the RRT, RRT_informed, and RRT_star algorithms, offered advantages in terms of lower planning time, fewer sampling points, and shorter path lengths in both 2D and 3D map scenarios. Finally, grape-picking experiments were conducted in both a laboratory setting and a real orchard. The results demonstrated that the average path planning time using the proposed algorithm was shorter compared to baseline algorithms, effectively validating the efficiency and practicality of the algorithm.