This article describes and validates a data-driven cooperative feedback control system for mobile sensors. The system can be used to guide resource constrained mobile sensors through a dynamically changing environment in order to obtain a path that results in data collection at important locations in the domain. A simulated chemical puff is used as a test application where small, resource constrained aerial vehicles provide mobile sensing capabilities. A fluid-based control scheme is used to guide the mobile sensors through the domain to collect data on the puff. The data is then used to update and improve a model of the puff concentration. Simulations are provided, demonstrating the decrease in error between the simulated and actual puffs over time. Additionally, the effect of using different numbers of mobile sensors as well as different schemes for guiding the mobile sensors is investigated. The feasibility of the technique using real sensors is demonstrated experimentally using a single UAV tracking a simulated puff.

Using a multi-vehicle control scheme based on smoothed particle hydrodynamics, a simulated swarm of unmanned underwater vehicles is guided along a pre-computed optimal trajectory between two points in an open domain under the influence of a strong background flow. The pre-computed trajectory is optimal in terms of fuel usage for a single vehicle. If the gradient of the velocity field is small compared to the total swarm radius, guiding a swarm of vehicles along this trajectory gives nearly optimal trajectories for all vehicles in the swarm without requiring additional costly optimization computations. We provide a bound on the maximum energy cost for vehicles in the swarm and also provide a more realistic estimate of the maximum energy cost. We also determine that the energy cost scales as N3/2for swarms with large numbers of vehicles, N. The algorithm and fuel cost bounds are verified in simulations of unmanned underwater vehicles moving across a double gyre system on the scale of a small ocean basin.

A control algorithm based on Smoothed Particle Hydrodynamics (SPH) is used to guide a simulated swarm of unmanned vehicles along a pre-computed optimal trajectory. By using a fluid based control method, collision avoidance is automatically incorporated into the control. Given an initial position and goal location, it is computationally impractical to compute the optimal trajectories for a large swarm of vehicles. To overcome this, the optimal trajectory is computed for a single vehicle and the swarm is guided along this trajectory using fluid based control, in this case SPH. Importantly, this method avoids computing a potential field to guide the swarm and is easily adaptable to changing environments. Additionally, this method is intended to be used in the presence of a background flow through which the vehicles must travel. This control algorithm is found to be quite robust in the test case examined here and adding additional vehicles to the swarm has only a small impact on fuel consumption per vehicle.