Routing and Control for Marine Oil-Spill Cleanup with a Boom-Towing Vessel Fleet

2026-03-17

AI Summary

This work proposes the first risk-aware, scalable multi-autonomous surface vehicle (ASV) cooperative framework for containment and cleanup in multi-spill maritime oil spill scenarios, addressing the lack of extensible coordination methods for concurrent spills in real-world settings. The approach formulates the mission as a risk-weighted minimum-latency problem and integrates mixed-integer linear programming with a custom warm-start heuristic to achieve near-optimal scheduling for dozens of spill sites within minutes on standard hardware. To manage the coupled dynamics of dual ASVs towing a boom, the framework incorporates both feedback linearization and PID-based tracking controllers, which simulation results demonstrate enable high-precision path following.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Coordination and Collaboration

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Marine oil spills damage ecosystems, contaminate coastlines, and disrupt food webs, while imposing substantial economic losses on fisheries and coastal communities. Prior work has demonstrated the feasibility of containing and cleaning individual spills using a duo of autonomous surface vehicles (ASVs) equipped with a towed boom and skimmers. However, existing algorithmic approaches primarily address isolated slicks and individual ASV duos, lacking scalable methods for coordinating large robotic fleets across multiple spills representative of realistic oil-spill incidents. In this work, we propose an integrated multi-robot framework for coordinated oil-spill confinement and cleanup using autonomous ASV duos. We formulate multi-spill response as a risk-weighted minimum-latency problem, where spill-specific risk factors and service times jointly determine cumulative environmental damage. To solve this problem, we develop a hybrid optimization approach combining mixed-integer linear programming, and a tailored warm-start heuristic, enabling near-optimal routing plans for scenarios with tens of spills within minutes on commodity hardware. For physical execution, we design and analyze two tracking controllers for boom-towing ASV duos: a feedback-linearization controller with proven asymptotic stability, and a baseline PID controller. Simulation results under coupled vessel-boom dynamics demonstrate accurate path tracking for both controllers. Together, these components provide a scalable, holistic framework for rapid, risk-aware multi-robot response to large-scale oil spill disasters.

Problem

• marine oil-spill cleanup
• multi-robot coordination
• autonomous surface vehicles
• scalable response
• multi-spill scenarios

Innovation

• multi-robot coordination
• risk-weighted minimum-latency problem
• mixed-integer linear programming
• boom-towing ASV
• feedback-linearization control