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Human activities are the principal contributors to oil pollution in marine ecosystems, thereby causing severe ecological damage. The high volume of vessel traffic operating in these areas contributes to the rapid contamination of the marine ecosystem, leading to frequent oil spill events, particularly near ports where congestion is prevalent. Addressing this issue today necessitates the involvement of numerous skilled personnel committed to the task. This team undertakes the repetitive and tedious work of surveying the area, detecting spills, and employing various techniques to address each oil slick. The emergence of Unmanned Surface Vehicle (USV) technology has introduced a promising alternative capable of alleviating the process of continuous monitoring and cleaning operations in proximal shoreline areas. This paper addresses the problem of USV cleaning operations near the port. The proposed method synthesizes a hierarchical architecture that integrates traditional global path planning for multi-destination oil spills, along with coverage path planning based on reinforcement learning, to adapt to dynamically changing oil spills. This combined architecture results in a comprehensive solution, allowing navigation within the port&#x2019;s vicinity to address each occurrence of oil pollution. To evaluate the effectiveness of this approach, we conducted an elaborate simulation designed to replicate port activities. The findings of this paper indicate a significant reduction in pollution levels due to USV operation and underscore the ability to acquire complex policies for dynamic coverage planning through the use of a reinforcement learning framework.

USV Port Oil Spill Cleanup using Hybrid Multi-Destination RL-CPP