Autonomous Oil Spill Response Through Liquid Neural Trajectory Modeling and Coordinated Marine Robotics

TL;DR

This paper presents an integrated system combining liquid neural networks and marine robotics for real-time oil spill prediction and response, significantly improving accuracy and coordination over existing methods.

Contribution

It introduces a novel framework that fuses Liquid Time-Constant Neural Networks with multi-agent marine robotics for enhanced oil spill trajectory forecasting and mitigation.

Findings

Achieved 0.96 spatial accuracy in spill prediction, surpassing LSTM by 23%.

Demonstrated scalable, decentralized decision-making with swarm robotics.

Validated effectiveness using Deepwater Horizon spill data.

Abstract

Marine oil spills pose grave environmental and economic risks, threatening marine ecosystems, coastlines, and dependent industries. Predicting and managing oil spill trajectories is highly complex, due to the interplay of physical, chemical, and environmental factors such as wind, currents, and temperature, which makes timely and effective response challenging. Accurate real-time trajectory forecasting and coordinated mitigation are vital for minimizing the impact of these disasters. This study introduces an integrated framework combining a multi-agent swarm robotics system built on the MOOS-IvP platform with Liquid Time-Constant Neural Networks (LTCNs). The proposed system fuses adaptive machine learning with autonomous marine robotics, enabling real-time prediction, dynamic tracking, and rapid response to evolving oil spills. By leveraging LTCNs--well-suited for modeling complex, time-dependent processes--the framework achieves real-time, high-accuracy forecasts of spill movement. Swarm intelligence enables decentralized, scalable, and resilient decision-making among robot agents, enhancing collective monitoring and containment efforts. Our approach was validated using data from the Deepwater Horizon spill, where the LTC-RK4 model achieved 0.96 spatial accuracy, surpassing LSTM approaches by 23%. The integration of advanced neural modeling with autonomous, coordinated robotics demonstrates substantial improvements in prediction precision, flexibility, and operational scalability. Ultimately, this research advances the state-of-the-art for sustainable, autonomous oil spill management and environmental protection by enhancing both trajectory prediction and response coordination.