用多艘无人船编队高效清理多个海上漏油点，兼顾风险与响应速度。
Routing and Control for Marine Oil-Spill Cleanup with a Boom-Towing Vessel Fleet
- 将多漏油点清理建模为风险加权的最短延迟问题，统筹环境危害与作业时间。
- 可在分钟级生成数十个油污点的近优航行路线，适配普通硬件。
- 设计两种稳定跟踪控制器，实现在耦合动力学下精准拖曳围油栏。
海洋漏油会破坏生态系统、污染海岸线并扰乱食物链，给渔业和沿海社区带来巨大经济损失。以往研究已证明，由两艘自主水面艇（ASV）组成的编队配合拖曳围油栏和收油机可有效处理单个油污事件。然而，现有算法主要针对孤立油污或单一编队，缺乏对大规模机器人舰队协同应对多个油污事件的可扩展方法。本文提出一种集成式多机器人框架，用于协调多艘ASV编队完成海上漏油封堵与清理。将多油污响应问题建模为风险加权最小延迟问题，其中油污特定风险因素与服务时间共同决定累积环境损害。为此，开发了一种混合优化方法，结合混合整数线性规划与定制化的热启动启发式算法，在商品级硬件上实现分钟级求解，适用于包含数十个油污点的场景。在物理执行层面，设计并分析了两种针对拖曳围油栏的ASV编队的跟踪控制器：一种基于反馈线性化的控制器具有渐近稳定性，另一种为基准的PID控制器。在耦合船-围油栏动力学下的仿真结果表明，两种控制器均能实现精确路径跟踪。上述组件共同构成一个可扩展、全流程的快速、风险感知多机器人应急响应框架。
原文摘要 · Abstract (English)
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.
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