Robust Decentralized Navigation of Multi-Agent Systems with Collision Avoidance and Connectivity Maintenance Using Model Predictive Controllers

TL;DR

This paper presents a decentralized control approach using nonlinear model predictive controllers for multi-agent systems to achieve collision-free navigation and maintain connectivity in a bounded 3D workspace with obstacles.

Contribution

It introduces a novel decentralized control protocol that guarantees connectivity and collision avoidance for uncertain nonlinear multi-agent systems using DNMPC.

Findings

Agents successfully reach target positions while maintaining connectivity.

The control scheme effectively avoids collisions with obstacles and workspace boundaries.

Simulations confirm the robustness and effectiveness of the proposed method.

Abstract

This paper addresses the problem of navigation control of a general class of 2nd order uncertain nonlinear multi-agent systems in a bounded workspace, which is a subset of $R^3$ , with static obstacles. In particular, we propose a decentralized control protocol such that each agent reaches a predefined position at the workspace, while using 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.