Robust Decentralized Navigation of Multi-Agent Systems with Collision Avoidance and Connectivity Maintenance Using Model Predictive Controllers
It addresses the challenge of safe and connected navigation for multi-agent systems under uncertainty and limited sensing, which is important for applications like drone swarms or autonomous vehicles.
This paper proposes a decentralized nonlinear model predictive control (DNMPC) framework for second-order uncertain nonlinear multi-agent systems to achieve navigation to predefined positions while guaranteeing connectivity maintenance and collision avoidance with agents, obstacles, and workspace boundaries. Simulation results validate the approach.
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.