Adaptive End-effector Pose Control for Tomato Harvesting Robots

This paper investigates the development of a tomato- harvesting robot operating on a smart farm and primarily studies the reachable pose of tomatoes in the non-dexterous workspace of manipulator. The end-effector can only reach the tomatoes with reachable poses when the tomatoes is within the non-dexterous workspace. If the grasping posture is not reachable, it will lead to grasping failure. An adaptive end-effector pose control method based on a genetic algorithm(GA) is proposed to find a reachable pose. The inverse kinematic solution based on analysis method of the manipulator is analyzed and the objective function of whether the manipulator has a solution or not is obtained. The grasping pose is set as an individual owing to the position of the tomatoes is fixed and the grasping pose is variable. The GA is used to solve until a pose that can make the inverse kinematics have a solution is generated. This pose is the reachable grasping pose of the tomato at this position. The quintic interpolation polynomial is used to plan the trajectory to avoid damage to tomatoes owing to fast approaching speed and a distance based background filtering method is proposed. Experiments were performed to verify the effectiveness of the proposed method. The radius of the workspace of the UR3e manipulator with the end-effector increased from 550 to 800 mm and the grasping rang expanded by 208%. The harvesting success rate using the adaptive end- effector pose control method and trajectory planning method was 88%. The cycle of harvesting a tomato was 20s. The experimental results indicated that the proposed tomato-recognition and end- effector pose control method are feasible and effective