Decentralized and Recursive Identification for Cooperative Manipulation of Unknown Rigid Body with Local Measurements

TL;DR

This paper introduces a novel decentralized recursive method for online identification of unknown parameters in 3D rigid body cooperative manipulation, enabling adaptive control based on local measurements.

Contribution

It presents the first fully decentralized, recursive approach for identifying kinematic and dynamic parameters in 3D cooperative manipulation using linear observation models.

Findings

Effective in online parameter identification

Applicable to both 2D and 3D manipulation

Demonstrated through numerical simulations

Abstract

This paper proposes a fully decentralized and recursive approach to online identification of unknown kinematic and dynamic parameters for cooperative manipulation of a rigid body based on commonly used local measurements. To the best of our knowledge, this is the first paper addressing the identification problem for 3D rigid body cooperative manipulation, though the approach proposed here applies to the 2D case as well. In this work, we derive truly linear observation models for kinematic and dynamic unknowns whose state-dependent uncertainties can be exactly evaluated. Dynamic consensus in different coordinates and a filter for dual quaternion are developed with which the identification problem can be solved in a distributed way. It can be seen that in our approach all unknowns to be identified are time-invariant constants. Finally, we provide numerical simulation results to illustrate the efficacy of our approach indicating that it can be used for online identification and adaptive control of rigid body cooperative manipulation.