首页|Findings from Northwest A&F University Yields New Findings on Agric ultural Robots (Fruit Flexible Collecting Trajectory Planning Based On Manual Sk ill Imitation for Grape Harvesting Robot)
Findings from Northwest A&F University Yields New Findings on Agric ultural Robots (Fruit Flexible Collecting Trajectory Planning Based On Manual Sk ill Imitation for Grape Harvesting Robot)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Agriculture-Agricultural Robots have been published. According to news reporting originatin g from Shaanxi, People's Republic of China, by NewsRx correspondents, research s tated, "The flexible collection is vital for the intelligent grape-picking robot . In view of the reliable operation method obtained by long-term training of hum an prior skill, a fruit flexible collecting trajectory planning method based on manual skill imitation for grape harvesting robot is proposed." Funders for this research include Beijing Municipal Science & Tech nology Commission, BAAFS Youth Research Foundation. Our news editors obtained a quote from the research from Northwest A& F University, "The method involves capturing manual teaching trajectory data usi ng a motion capture system, preprocessing the data, extracting features from mul tiple teaching trajectories, and forming a probability distribution through Gaus sian mixture model-Gaussian mixture regression (GMM-GMR). And combined with th e 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 hyper parameter adaptation KMP (O-KMP) was proposed to meet the trajectory fitting eff ect of multiple key points. Mean square error (MSE) was used to evaluate the dev iation of the trajectory from the reference trajectory. The partial optimal traj ectory is selected and integrated into a single trajectory. Two experiments were conducted to investigate imitation: For the same starting and ending points tas k, 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."
ShaanxiPeople's Republic of ChinaAsiaAgricultural RobotsAgricultureEmerging TechnologiesMachine LearningRo botRoboticsNorthwest A&F University
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