A Passivity-Based Distributed Reference Governor for Constrained Robotic Networks
This work addresses coordination and constraint satisfaction in mobile robotic networks, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of distributed reference governor (RG) for constrained robotic networks by proposing a passivity-based distributed optimization scheme, proving convergence to the optimal solution and demonstrating effectiveness through simulations and experiments.
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.