Cooperative steering controls enable mobile sampling platforms to conduct synoptic, adaptive surveys of dynamicnspatiotemporal processes by appropriately regulating the space-time separation of their sampling trajectories.nSensing platforms in the air and maritime domains can be pushed off course by strong and variable environmentalndynamics. However, most existing cooperative-control algorithms are based on simple motion models that do notninclude a flowfield. Existing models that include the flowfield often include speed control to compensate for the flow.nIn this paper, we describe a constant-speed self-propelled particle model that explicitly incorporates a time-invariantnflowfield. Each vehicle is represented by a Newtonian particle subject to a gyroscopic steering control. We describenthe Lyapunov-based design of decentralized control algorithms that stabilize collective motion in a known flowfield.nIn the case of a spatially variable flow, we provide an algorithm to stabilize synchronized motion, in which all of thenparticles move in the same direction, and circular motion, in which all of the particles orbit an inertially fixed point atna constant radius. For a spatially invariant flow, we provide an algorithm to stabilize balanced motion, in which thenparticle position centroid is inertially fixed, and symmetric circular formations, in which the particle spacing aroundna circle is temporally regulated. Via the latter algorithm, we provide a method of stabilizing a circular formation innwhich the particles are evenly spaced in time and the formation is centered on a moving target. The theoretical resultsnare illustrated with two numerical examples based on applications in environmental monitoring and targetnsurveillance.
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