Multivehicle Control in a Strong Flowfield with Application to Hurricane Sampling

Abstract

A major obstacle to path-planning and formation-control algorithms in multi-vehicle systems are strong flows in which the flow speed is greater than the vehicle speed relative to the flow. This challenge is especially pertinent in the application of unmanned aircraft used for collecting targeted observations in a hurricane. 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. This paper presents results for motion coordination in a strong, known flowfield using a selfpropelled particle model of vehicle motion in which each particle moves at constant speed relative to the flow. A new formulation of the particle model with respect to a rotating reference frame is provided along with a set of conditions on the flowfield that ensures trajectory feasibility. Results from the Lyapunov-based design of decentralized control algorithms are presented for circular, folium, and spirograph trajectories, which are selected for their potential use as hurricane sampling trajectories. The theoretical results are illustrated using numerical simulations in an idealized hurricane model. Nomenclature N Number of particles in the system k Particle index k = 1,. .. , N r k/O Position of particle k with respect to O

Non-Gaussian Estimation of a Potential Flow by an Actuated Lagrangian Sensor Steered to Separating Boundaries by Augmented Observability

“…A model for a self-propelled vehicle with speed ρ in the complex plane C without a flow field present is [36] 182ż = ρe…”

Convergência e Circulação de Curvas Variantes no Tempo com um Conjunto de Quadrotores

“…Particularmente em missões de segurança, os veículos aéreos superam os veículos terrestres em vários aspectos, já que possuem maior manobrabilidade e podem trabalhar no espaço tridimensional. Algumas dessas tarefas podem ser executadas fazendo com que um ou vários robôs em cooperação convirjam e circulem uma curva variante no tempo, como no caso de patrulhamento de perímetros (Pimenta et al, 2013;Hsieh et al, 2008), seguimento de alvos móveis (Briñón-Arranz et al, 2019) e monitoramento ambiental (DeVries and Paley, 2012).…”

Active Singularities for Multivehicle Motion Planning in an N-Vortex System

“…However, the movement of these eddies could not be correlated with local wind shear stress [18]. Understanding transport within these flow structures is possible but requires in situ observations over a large spatiotemporal volume collected by fleets of autonomous vehicles [3], [6], [8], [17]. Underwater gliders, which are steered, buoyancy-driven, long-endurance vehicles, and drifters, which are depth-controlled underwater platforms that passively drift, are deployed to reduce uncertainties in estimates of ocean processes [14], [16], [20] and to sample and track oceanographic features [2], [18].…”

“…Frew et al [4] construct Lyapunov-based guidance vector fields for specifying sampling trajectories that can be tracked by the onboard controller of an autonomous (aerial) vehicle. Further, focusing measurements on targeted areas [6] and stabilizing multivehicle formations have been shown to help maximize information collection [3] when sampling geophysical flows that can be modeled using reduced-order methods such as a point-vortex model.…”

“…Significant hardware and sensor improvements [19] as well as algorithmic performance guarantees [9], [10] have further encouraged interest. However, there are open challenges about how mobile sensor platforms can most effectively sample and interact with strong, circulating flows [3], [10], [11].…”