首页|New Robotics Findings from Chinese Academy of Sciences Reported (A Highly Powerf ul Calibration Method for Robotic Smoothing System Calibration Via Using Adaptiv e Residual Extended Kalman Filter)
New Robotics Findings from Chinese Academy of Sciences Reported (A Highly Powerf ul Calibration Method for Robotic Smoothing System Calibration Via Using Adaptiv e Residual Extended Kalman Filter)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Robotics is now availab le. According to news reporting originating in Chengdu, People's Republic of Chi na, by NewsRx journalists, research stated, "Achieving high absolute positioning accuracy is crucial for obtaining aspheric optical components with remarkable s urface quality using a robotic smoothing system. Robot kinematic calibration is an effective means of improving absolute positioning accuracy." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news reporters obtained a quote from the research from the Chinese Academy o f Sciences, "The calibration algorithms that use gradient direction have been sh own to significantly improve computational efficiency compared to other calibrat ion methods. However, these algorithms usually suffer from gradient degradation or vanishing after several iterations. In particular, the extended Kalman filter depends on the initial covariance matrix, which must be continually adjusted to reasonable values using artificial means. To address this challenge, an adaptiv e residual extended Kalman filter is proposed for robot kinematic calibration. T his method involves using the residual generated from the current iteration to a void gradient degradation or vanishing in the next iteration. An improved butter fly optimization algorithm is also used to adapt the system covariance matrix, t he covariance matrix of system noises, and the covariance matrix of measurement noises of the extended Kalman filter to improve the identification accuracy. Fin ally, the proposed method's feasibility is demonstrated through sufficient calib ration experiments. The method improved the RMSE positioning accuracy from 0.932 8 to 0.4786 mm, a 48.69 % increase from before calibration. The sm oothing compensation experimental results show that the proposed method achieves optical components with excellent surface quality."
ChengduPeople's Republic of ChinaAsi aEmerging TechnologiesMachine LearningRobotRoboticsRobotsChinese Aca demy of Sciences
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