Distributed Estimation of State and Parameters in Multi-Agent Cooperative Load Manipulation

TL;DR

This paper introduces two distributed algorithms for estimating the state and parameters of a planar load manipulated by multiple agents, using rigid body kinematics, nonlinear observation theory, and consensus algorithms, with proven convergence and robustness.

Contribution

It presents novel distributed estimation methods that handle both constant and time-varying parameters in multi-agent load manipulation tasks, with theoretical analysis and practical control strategies.

Findings

Algorithms successfully estimate load parameters in simulations.

Methods are robust to communication constraints and parameter variations.

Theoretical proofs guarantee convergence and observability.

Abstract

We present two distributed methods for the estimation of the kinematic parameters, the dynamic parameters, and the kinematic state of an unknown planar body manipulated by a decentralized multi-agent system. The proposed approaches rely on the rigid body kinematics and dynamics, on nonlinear observation theory, and on consensus algorithms. The only three requirements are that each agent can exert a 2D wrench on the load, it can measure the velocity of its contact point, and that the communication graph is connected. Both theoretical nonlinear observability analysis and convergence proofs are provided. The first method assumes constant parameters while the second one can deal with time-varying parameters and can be applied in parallel to any task-oriented control law. For the cases in which a control law is not provided, we propose a distributed and safe control strategy satisfying the observability condition. The effectiveness and robustness of the estimation strategy is showcased by means of realistic MonteCarlo simulations.