Routing and Control for Marine Oil-Spill Cleanup with a Boom-Towing Vessel Fleet
Authors: Snir Carmeli, Adir Morgan, Kiril Solovey
Organizations: The authors are with the Viterbi Faculty of Electrical and Computer Engineering, Technion–Israel Institute of Technology, Haifa, Israel.
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.
Figures & tables
Fig. 1 : Visualization of coordinated oil-spill confinement and cleanup using autonomous ASV duos equipped with towed booms and skimmers, which we tackle in this work.
Fig. 2 : Illustration of the path tracking problem for a boom-towing ASV duo, along with feedback-linearization control behavior, presented in Sec. VI . A reference trajectory generated by the routing approach is converted into a sequence of tracked setpoints (blue dots and red squares) for each individual ASV, while satisfying boom constraints.
Fig. 3 : Body–fixed surge–sway frame (u,v) attached to the vessel’s center of mass (CoM).
Fig. 4 : Visualization for the boom dynamics. Body–fixed surge–sway frame (t,n) attached to the link’s center of mass (CoM); inertial axes point East ( e1 ) and North ( e2 ). The control inputs are the forces and torques applied by nearby linked bodies.
Fig. 5 : Final objective values for 25, 50, and 100 spills instances across varying numbers of agents. Shaded gray regions indicate lower bounds.
Fig. 6 : RMSE maps for cross-track (top) and heading errors (bottom) as functions of curvature radius and velocity for both vessels using feedback linearization control.
Fig. 7 : Lead controller in the normalized variant: the controller lies in the feedback while its static gain sits in the main branch along with the gain KP . The reference signal is a piecewise constant signal, filtered with a low-pass filter to avoid discontinuous jumps in the control signal.
Collaborative Robotics And Intelligent Systems Institute, Oregon State University, Corvallis, OR, USA · MIT Marine Autonomy Lab, MIT, Cambridge, MA, USA