A Unified Framework of In-Situ Calibration and Synchronous Identification for Industrial Robots Using Composite Sensing

Previous current-based dynamic identification methods suffer from coupled motor-side uncertainties, imprecise joint drive gains, and current noises. The combined identification methods integrate the bedplate wrench and motor current to derive the decoupled link-side and motor-side dynamic parameters. However, the in-situ calibration demand for the bedplate sensor and drive gain complicates the identification process. Further, asynchronous calibration and identification accumulate and amplify errors. Therefore, this article proposes a unified framework of synchronous calibration and identification for industrial robots. First, the bedplate sensor calibration and dynamic identification are formulated into a unified optimization model and solved. Next, the drive gains and joint dynamic parameters are extracted under the same optimization structure. Then, a unified framework is founded to seamlessly integrate the in-situ calibration, synchronous identification, physical feasibility constraints, composite sensing fusion, and nonlinear friction estimation. Finally, the excitation trajectory for combined identification is delicately designed and executed. The high identification precision is evaluated through cross-validation experiments by the prediction performance of bedplate wrench and joint torque by up to 97.581% and 93.318%, respectively. The suggested framework outperforms other advanced methods and decreases parameter standard deviations. Note to Practitioners-Accurate robot dynamics are crucial for high-performance model-based control techniques. This research aims to address two issues that interfere with combined dynamic identification results: the coupled motor-side uncertainties induced by current signals and the accumulated errors caused by asynchronous sensing calibration and robot dynamic identification. Thus, this article proposes a unified framework of in-situ calibration and synchronous identification for industrial robots using composite sensing, which can bring many benefits to practitioners. Firstly, the proposed framework extracts the robot dynamic and sensing device parameters within one excitation trajectory execution, which significantly refines the results and simplifies the experimental process by the integrated algorithm structure. Secondly, the suggested optimization paradigm of synchronous calibration and identification can be implemented for different types of industrial robots in various scenarios easily. Thirdly, the calibrated device parameters and measurement covariance matrices contribute to the further applications of robot force perception and control.