首页|New Robotics Data Have Been Reported by Researchers at Shanghai Jiao Tong University (A Calibration and Compensation Method for an Industrial Robot With High Accuracy Harmonic Reducers)
New Robotics Data Have Been Reported by Researchers at Shanghai Jiao Tong University (A Calibration and Compensation Method for an Industrial Robot With High Accuracy Harmonic Reducers)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Robotics. According to news originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Industrial serial robots need high stiffness to keep absolute pose accuracy and meet the requirements in practical applications. However, the weak stiffness feature of robot joints and the payloads affected on robot end-effector, which will also increase the pose error of robot." Financial support for this research came from National Key Research and Development Program for Robotics Serialized Harmonic Reducer Fatigue Performance Analysis and Prediction and Life Enhancement Technology Research. Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, "Especially, the existing calibration methods often consider under no-payload condition without discussing the payload state. In this paper, we report a new industrial serial robot composed by a new harmonic reducer: Model-Y, based on high accuracy and high stiffness, and a kinematic parameter calibration algorithm which is based on a harmonic reducer force-deformation model. To decrease the accuracy effects of payload, an iterative calibration method for kinematic parameters with payload situation was proposed. Simulation and experiments are conducted to verify the effectiveness of the proposed calibration method using the self-developed industrial serial robot. The results show a remarkably improved accuracy in absolute position and orientation with the robot's payload range. The position mean error has 70% decreased to 0.1 mm and the orientation mean error diminished to less than 0.01 degrees after calibration with compensation. Additionally, online linear and circular tests are carried out to evaluate the position error of the robot during large-scale spatial and low-speed continuous movement."
ShanghaiPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsShanghai Jiao Tong University