Fruit flexible collecting trajectory planning based on manual skill imitation for grape harvesting robot

Abstract

The flexible collection is vital for the intelligent grape-picking robot. In view of the reliable operation method obtained by long-term training of human prior skill, a fruit flexible collecting trajectory planning method based on manual skill imitation for grape harvesting robot is proposed. The method involves capturing manual teaching trajectory data using a motion capture system, preprocessing the data, extracting features from multiple teaching trajectories, and forming a probability distribution through Gaussian mixture model – Gaussian mixture regression (GMM-GMR). And combined with the key point of manual operation trajectory, the general trajectory generated by GMR is segmented and further imitated by kernelized movement primitives (KMP) to obtain the reference trajectory, respectively. An optimization method for hyperparameter adaptation KMP (O-KMP) was proposed to meet the trajectory fitting effect of multiple key points. Mean square error (MSE) was used to evaluate the deviation of the trajectory from the reference trajectory. The partial optimal trajectory is selected and integrated into a single trajectory. Two experiments were conducted to investigate imitation: For the same starting and ending points task, the MSE of the trajectory generated by O-KMP decreased by 15.274% compared to the original fixed hyperparameter KMP. For different placement tasks, the MSE of the trajectory generated by the O-KMP decreased by 7.296% compared to the original KMP. Finally, the robot arm of the actuator visually presents different task states for the compliant placement of fresh grapes.