Decentralized Declustering of Multiple Underactuated Autonomous Surface Vehicles: Managing Robot Swarms in the Field

Abstract

The task of deploying a large number of autonomous vehicles is challenging, risky and often overlooked in the literature. These vehicles are typically deployed from a single location, and their underactuated nature, close proximity, and susceptibility to external disturbances make it difficult to achieve a mission-ready configuration without collisions. In this paper, we address the problem of transitioning a set of underactuated Autonomous Surface Vehicles (ASVs) from arbitrary and inconvenient initial conditions, to a deconflicted set of deployed vehicles. We propose a decentralized and scalable method that assigns the vehicles to their target positions, generates optimal paths given minimum turning radii and assures collision avoidance between the vehicles. Performance is verified through simulation and extensive field trials. Results demonstrate that our approach improves the time to decluster with 58% compared to the current manual method. By improving efficiency and robustness while eliminating human involvement, this work streamlines ASV fleet deployments, enabling more effective multi-agent field operations.