A motion planning method for winter jujube harvesting robotic arm based on optimized Informed-RRT* algorithm

Abstract

Winter jujube is a fruit that is rich in nutritional value and has a delicious taste. Since the ripe winter jujubes are tender and easy to be damaged, during the picking process the mechanical arm needs to maintain smooth and stable movement to avoid the impact of vibration or oscillation on the picking task. However, existing motion planning algorithms may not guarantee the smoothness and stability of the mechanical arm's movement. The research discovered a motion planning method based on the optimized Informed-RRT* algorithm. By adding target bias, adaptive step size, and pruning strategies, the optimization of search paths was achieved, reducing unnecessary movement of the mechanical arm during operation. This can ensure high picking success rate and low damage rate. Through comparative experiments, the optimized Informed-RRT* algorithm has good performance in the two-dimensional and three-dimensional spaces. Within the specified time, the planned paths by the optimized Informed-RRT* algorithm are shorter and smoother, significantly improving the efficiency of the mechanical arm. Additionally, this research deploys the optimized Informed-RRT* algorithm to the Robot Operating System (ROS) and conducts three-dimensional modeling of the picking environment through Moveit! to obtain real-time environmental information and obstacle detection. This allows for effective avoidance of obstacles while ensuring the optimal path. To ensure the safety of the mechanical arm's movement, this research monitors the position changes of each joint in real-time. The results indicated that during the movement process, the angular velocity and angular acceleration of the mechanical arm exhibit smooth and continuous trends, demonstrating good dynamic stability and control performance during movement and further proving the effectiveness of the optimized Informed-RRT* algorithm.