Calibrating Kinematic Parameters Using Force Sensors скачать в хорошем качестве

Calibrating Kinematic Parameters Using Force Sensors

Kwon, Jaewoon, Keunjun Choi, and Frank C. Park. "Kinodynamic Model Identification: A Unified Geometric Approach." IEEE Transactions on Robotics (2021). Published in T-RO, 2021. Also presented at IROS 2021. [Abstract] A robot's dynamic model depends on both the kinematic and mass-inertial parameters of a robot. Robot model identification therefore typically begins with kinematic identification; the mass-inertial parameters are then identified with the identified kinematic parameters used in the dynamic model. In this article we show that poorly identified kinematic parameters can lead to an uncorrectable bias in the dynamic model, leading to errors in the mass-inertial parameters that are many times that of kinematic parameter errors. We instead argue that a unified kinodynamic identification leads to more accurate identification of both the kinematic and mass-inertial parameters. A linearly weighted kinodynamic objective function is proposed, in which the weight can be interpreted from a maximum likelihood perspective as the relative accuracy of the kinematic sensors vis-à-vis the dynamic sensors. Recursive algorithms for computing exact analytic gradients of the kinodynamic objective function are newly derived, leading to robust and fast-converging identification algorithms. Extensive numerical and hardware experiments demonstrate the advantages of our unified kinodynamic identification procedure. ----- © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.