Unmanned Aircraft Systems (UAS) may prove invaluable in their ability to collect targeted observations within a hurricane, at low altitudes in which manned flights are too dangerous. The data collected from multiple aircraft are expected to yield improved analyses and predictions of the track, structure and intensity of a tropical cyclone. However, a major obstacle to path-planning and formation-control algorithms in multi-vehicle systems are strong flows in which the ambient flow speed is greater than the vehicle speed relative to the flow. The presence of such a flowfield may inhibit a vehicle from making forward progress relative to a ground-fixed frame, thus limiting the directions in which it can travel and making some trajectories infeasible.

This talk will discuss the design, feasibility analysis, and implementation of candidate sampling formations applied to an idealized time-invariant hurricane model in which the vortex is allowed to translate in a plane. Using a self-propelled particle model in which each particle moves at constant speed relative to the flow, motion coordination results are presented. We present the particle model with respect to inertial and rotating reference frames and provide for each case a set of conditions on the flowfield that ensure trajectory feasibility. Lyapunov-based design is utilized to generate decentralized control algorithms for circular, folium, and spirograph trajectories, which are selected for their potential use as hurricane sampling trajectories. Circular formations remain largely feasible and can provide targeted sampling at a specific radius. Folium trajectories are selected to closely emulate current sampling trajectories of manned aircraft and spirograph trajectories can be manipulated to concentrate higher sampling densities in specific azimuthally symmetric regions of the idealized vortex.