This paper presents a novel approach for identifying the dynamic parameters of a 6 DoF serial manipulator characterized by coupling and springs, which is a common mechanics for industrial robots. The proposed method consists of two steps: at first, a static identification process for estimating the masses and centers of gravity (CoGs) of the links is performed; then, a dynamic identification process for determining the inertias, motor inertias, and frictions is executed. In the dynamic identification process, a trajectory is used to generate the required dynamic response of the system, and a regression matrix is employed to combine the identified parameters. Finally, a constrained optimization method is utilized to extract the parameters. The proposed method has been validated through simulations and experiments, showing high accuracy and reliability. This research contributes to the advancement of robot modeling and control, and has potential applications in various industrial fields.
Parameter Identification of a 6-DoF Serial Manipulator with Coupled Joints and Load-Assisting Springs for Industrial Applications / Nini, Matteo; Ferraguti, Federica; Ragaglia, Matteo; Bertuletti, Mattia; Di Napoli, Simone; Fantuzzi, Cesare. - (2024), pp. 1-8. (Intervento presentato al convegno 2024 7th Iberian Robotics Conference (ROBOT) tenutosi a Madrid, Spain nel 06/11/2024) [10.1109/robot61475.2024.10796943].
Parameter Identification of a 6-DoF Serial Manipulator with Coupled Joints and Load-Assisting Springs for Industrial Applications
Nini, Matteo
;Ferraguti, Federica;Bertuletti, Mattia;Fantuzzi, Cesare
2024
Abstract
This paper presents a novel approach for identifying the dynamic parameters of a 6 DoF serial manipulator characterized by coupling and springs, which is a common mechanics for industrial robots. The proposed method consists of two steps: at first, a static identification process for estimating the masses and centers of gravity (CoGs) of the links is performed; then, a dynamic identification process for determining the inertias, motor inertias, and frictions is executed. In the dynamic identification process, a trajectory is used to generate the required dynamic response of the system, and a regression matrix is employed to combine the identified parameters. Finally, a constrained optimization method is utilized to extract the parameters. The proposed method has been validated through simulations and experiments, showing high accuracy and reliability. This research contributes to the advancement of robot modeling and control, and has potential applications in various industrial fields.Pubblicazioni consigliate
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