Decentralized Control of Uncertain Multi-Agent Systems with Connectivity Maintenance and Collision Avoidance

TL;DR

This paper presents a decentralized control framework using nonlinear model predictive controllers to ensure connectivity, collision avoidance, and goal achievement for uncertain multi-agent systems in bounded environments.

Contribution

It introduces a novel decentralized control scheme that maintains connectivity and avoids collisions in uncertain multi-agent systems using DNMPC.

Findings

Connectivity is preserved under disturbances.

Collision avoidance is guaranteed with limited sensing.

Simulation validates the control framework.

Abstract

This paper addresses the problem of navigation control of a general class of uncertain nonlinear multi-agent systems in a bounded workspace of $\mathbb{R}^n$ with static obstacles. In particular, we propose a decentralized control protocol such that each agent reaches a predefined position at the workspace, while using only local information based on a limited sensing radius. The proposed scheme guarantees that the initially connected agents remain always connected. In addition, by introducing certain distance constraints, we guarantee inter-agent collision avoidance, as well as, collision avoidance with the obstacles and the boundary of the workspace. The proposed controllers employ a class of Decentralized Nonlinear Model Predictive Controllers (DNMPC) under the presence of disturbances and uncertainties. Finally, simulation results verify the validity of the proposed framework.