Autonomous marine platforms are becoming essential tools for ocean science and operations because they can collect observations over large areas and long durations while reducing the cost, risk, and logistical burden of ship-based campaigns. They are now used for environmental monitoring, adaptive sampling, ocean forecasting, upper-ocean and air–sea observations, infrastructure inspection, and other persistent observing tasks.

However, there is increasing value in operating these vehicles as coordinated teams rather than as isolated platforms. Multi-vehicle systems can sample larger regions, observe evolving phenomena at multiple locations simultaneously, and adapt more effectively to dynamic environmental features. To realize these advantages, vehicles should remain sufficiently spread over the region of interest so that different parts of the environment are sampled and measurements do not cluster inefficiently in only a small portion of the domain. It can also be beneficial for the team to preserve an organized group structure, since maintaining prescribed relative positions, shapes, or coverage properties allows the vehicles to function as a coordinated sensing array for feature tracking and cooperative sampling.

In this work, we study the coordinated control of multiple autonomous marine vehicles in dynamic flow environments using the MIT-MSEAS general partial differential equations for reachability and optimal planning.