Drifting Speed of Lagrangian Fronts and Oil Spill Dispersal at the Ocean Surface

Due to its dire impacts on marine life, public health, and socio-economic services, oil spills require an immediate response. Effective action starts with good knowledge of the ocean dynamics and circulation, from which Lagrangian methods derive key information on the dispersal pathways present in the contaminated region. However, precise assessments of the capacity of Lagrangian methods in real contamination cases remain rare and limited to large slicks spanning several hundreds of km. Here we address this knowledge gap and consider two medium-scale (tens of km wide) events of oil in contrasting conditions: an offshore case (East China Sea, 2018) and a recent near-coastal one (East Mediterranean, 2021). Our comparison between oil slicks and Lagrangian diagnostics derived from near-real-time velocity fields shows that the calculation of Lagrangian fronts is, in general, more robust to errors in the velocity fields and more informative on the dispersion pathways than the direct advection of a numerical tracer. The inclusion of the effect of wind is also found to be essential, being capable of suddenly breaking Lagrangian transport barriers. Finally, we show that a usually neglected Lagrangian quantity, the Lyapunov vector, can be exploited to predict the front drifting speed, and in turn, its future location over a few days, on the basis of near-real-time information alone. These results may be of special relevance in the context of next-generation altimetry missions that are expected to provide highly resolved and precise near-real-time velocity fields for both open ocean and coastal regions.

Design of Standoff Cooperative Target-Tracking Guidance Laws for Autonomous Unmanned Aerial Vehicles

This paper investigates two guidance laws of standoff cooperative tracking static and moving of multiple autonomous unmanned aerial vehicles for targets from the perspective of the control system design. In the scheme of the proposed guidance laws, one vehicle is chosen as leader and others as followers. The leader only needs the measurement of the target, and the followers only measure the leader and its neighbors in the communication topology network. By using the proposed guidance laws, it is guaranteed that all vehicles can track a static or moving target with an evenly spaced formation of circle. Considering the coupling of tracking and cooperation, the stability analysis is performed by constructing two relatively independent subsystems based on Lyapunov theory, and the corresponding rigorous proofs of stability are given. By comparing with the Lyapunov vector field guidance law, the simulation results verify the effectiveness and superiority of the proposed guidance laws.

Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance

Cooperative standoff tracking of moving targets is an important application of fixed-wing unmanned aerial vehicles (UAVs). To cope with the problem of long convergence time and unstable tracking in cooperative target tracking, traditional Lyapunov vector field guidance (LVFG) is modified. The guidance parameter c is discussed, and the gradient descent method is utilized to develop the optimal guidance parameter search algorithm. As for tracking moving targets, an interacting multiple model-based unscented Kalman filter (IMM-UKF) estimator is built for predicting the target state, and the result is used for correcting the guidance law. Meanwhile, a speed-based controller is developed for faster convergence to the desired intervehicle phase, and the stability of the controller is proved using the Lyapunov stability theory. Numerical simulation results indicate the proposed guidance converges faster to the standoff circle without intersecting the orbit. The state estimator reduces the estimate error and the intervehicle phase converges faster to the desired phase than the traditional control method. Furthermore, extensive hardware-in-the-loop simulations are carried out to verify the feasibility of the algorithm.