# A Passivity-Based Distributed Reference Governor for Constrained Robotic Networks

## TL;DR

This paper introduces a passivity-based distributed reference governor for constrained robotic networks, utilizing a novel optimization scheme and consensus estimator to ensure convergence and effectiveness through theoretical proof and practical validation.

## Contribution

It presents a new passivity-based distributed optimization approach for reference governors in robotic networks, with proven convergence and demonstrated effectiveness.

## Key findings

- Convergence of state estimates to the optimal solution is proven.
- The scheme is effective in both simulations and experiments.
- The method ensures coordinated constraint management in robotic networks.

## Abstract

This paper focuses on a passivity-based distributed reference governor (RG) applied to a pre-stabilized mobile robotic network. The novelty of this paper lies in the method used to solve the RG problem, where a passivity-based distributed optimization scheme is proposed. In particular, the gradient descent method minimizes the global objective function while the dual ascent method maximizes the Hamiltonian. To make the agents converge to the agreed optimal solution, a proportional-integral consensus estimator is used. This paper proves the convergence of the state estimates of the RG to the optimal solution through passivity arguments, considering the physical system static. Then, the effectiveness of the scheme considering the dynamics of the physical system is demonstrated through simulations and experiments.