用于海洋油污清理的拖曳浮标船队路由与控制
Routing and Control for Marine Oil-Spill Cleanup with a Boom-Towing Vessel Fleet
AI总结：
本文提出一种多机器人框架，用于协调多个自主水面车辆对多处油污进行 containment 和清理，通过混合整数线性规划和 warm-start 策略实现近最优路由计划，设计了拖曳浮标船队的反馈线性化控制器和 PID 控制器，模拟结果验证了路径跟踪的准确性。
摘要：
海洋油污破坏生态系统、污染海岸线并扰乱食物链，同时对渔业和沿海社区造成重大经济损失。先前工作展示了使用配备拖曳浮标和刮水器的自主水面车辆（ASVs）对单个油污进行 containment 和清理的可行性。然而，现有算法主要针对孤立油污和单个 ASV 配对，缺乏可扩展的方法来协调多个机器人舰队应对多个油污事件。本文提出了一种集成的多机器人框架，用于协调使用自主 ASV 配对的油污 containment 和清理。我们将多油污响应建模为一个风险加权最小延迟问题，其中油污特定的风险因素和服务时间共同决定累积环境损害。为了解决这个问题，我们开发了一种混合优化方法，结合混合整数线性规划和定制的 warm-start 策略，使在商用硬件上几分钟内即可生成针对数十次油污的近最优路由计划。对于物理执行，我们设计并分析了两种拖曳浮标 ASV 配对的跟踪控制器：具有已证明渐近稳定性的反馈线性化控制器和基准 PID 控制器。在耦合船体-浮标动力学下的模拟结果表明，两种控制器都能实现准确的路径跟踪。这些组件共同提供了一种可扩展、全面的框架，用于快速、风险感知的多机器人响应大型油污灾害。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
