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.
Autonomous Surface Vehicles (ASVs) have been increasingly deployed to perform different kinds of maritime tasks in recent years, which motivates the need for a reliable task allocation policy that can adapt to heterogeneous tasks and maritime disturbances. In this work, we formulate a multi-robot task allocation problem in which a team of ASVs with heterogeneous capabilities needs to complete tasks of different types and appearance times under unknown stochastic spatio-temporal uncertainties from maritime disturbances. We propose a decentralized Reinforcement Learning (RL) based policy network that is trained to be robust towards uncertainty-induced observation deviations, and is able to perform individual decision making that leads to efficient execution behaviors toward heterogeneous task requirements. We evaluate the proposed policy and four baseline methods in simulated experiments with varied proportions of different tasks types, numbers of tasks, robot species and robots per species. Experiment results show that our proposed policy achieves the lowest makespan and can be deployed to address scenarios with 100 robots and 400 tasks.
Xi Lin, Yaxuan Li, Paul Szenher +1
Department of Mechanical Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030, USA
Heterogeneous USV cooperative pursuit in constrained port waterways requires evader interception under navigation, traffic, and role constraints. This paper proposes OGR-MARL, an option-guided residual multi-agent reinforcement learning framework that is decoupled from a specific MARL algorithm. OGR-MARL integrates shared evader belief, role-conditioned option targets, adaptive rule penalties, and residual policy learning, allowing different MARL algorithms to learn corrective actions on top of rule-guided behaviors rather than exploring constrained port environments from scratch. We instantiate OGR-MARL with representative continuous-control MARL backbones, including MADDPG, MATD3, MAPPO, and MASAC, yielding OGR-MADDPG, OGR-MATD3, OGR-MAPPO, and OGR-MASAC. Experiments in an abstract Xiazhimen port-waterway scenario show that the OGR-MASAC instantiation achieves a 75.0% capture rate, promising mission-effective rule compliance, and the best heterogeneous coordination among the tested methods. Without retraining, zero-shot transfer to a QGIS/AIS-informed Xiazhimen map achieves promising results, demonstrating the generalization potential of OGR-MARL in more complex port scenarios.
Collaborative robots are well-suited to maritime missions that benefit from coordination, such as the exploration of unknown reef structures, inspection of subsea infrastructure, or search-and-rescue operations. These missions typically provide sparse feedback signals for measuring progress and require adherence to safety and regulatory norms, turning a mission into a multi-objective optimization problem. Coevolutionary algorithms can process these sparse feedback signals to generate coordinated behaviors, and in some cases extend behaviors to multiple objectives. However, incorporating high-level team objectives with low-level compliance considerations on the fly to balance norm adherence with team performance remains elusive. This paper introduces a multi-objective framework that blends coevolved behaviors with compliance behaviors to achieve a balance between maximizing team progress and minimizing norm violations. The key insight is to decouple learning from compliance since operational norms are prescribed rather than discovered. We demonstrate that our framework achieves high team performance while avoiding collisions on a collaborative swimmer rescue mission with up to 8 vehicles in a hardware deployment, and 12 vehicles in simulation. The key contribution of this paper is Marine Multi-Objective Compliance-Integrated Coevolution (MMOCIC), a framework that blends team-wide optimization with established norms for real-world deployments of learning-based coordination.
Everardo Gonzalez, Tyler M. Paine, Manuel Agraz Vallejo +3
Collaborative Robotics And Intelligent Systems Institute, Oregon State University, Corvallis, OR, USA · MIT Marine Autonomy Lab, MIT, Cambridge, MA, USA