A Matlab-Based Toolbox for Supervising Multi-Vehicle Autonomous Systems

Over the past decade, numerous significant contributions based on Model Predictive Control concepts have paved the way for a paradigm shift in addressing the complex task of coordinating autonomous vehicles to safely follow predefined trajectories defined as pointwise-in-time set-membership state constraints. While these techniques undoubtedly offer enhanced control performance, their initial implementation at the end-user level often presents formidable challenges, hindering their widespread adoption within the control community. For this reason, this paper introduces CoGoV, a novel Matlab-based toolbox designed to provide a streamlined and user-friendly approach for implementing Command Governor-based supervisory strategies in the context of multi-vehicle distributed motion planning challenges. Importantly, users are not required to possess specific optimization knowledge to leverage its capabilities. To illustrate the toolbox’s advantages, we present simulations demonstrating its effectiveness in supervising and coordinating a group of dynamically independent omnidirectional Unmanned Surface Vehicles (USVs) subject to Local, Obstacle Avoidance and Collision Avoidance constraints.