The problem addressed in this paper is the control of a Micro Unmanned Aerial Vehicle (MAV) for the purpose of obtaining video footage of a set of known ground targets with preferred azimuthal viewing angles, using fixed onboard cameras. Control is exercised only through the selection of waypoints, without modification of the MAV's pre-existing autopilot and waypoint following capability. Specifically, we investigate problems and potential solutions of performing this task in the presence of a known constant wind. Simulations are provided in the presence of randomly perturbed wind, based on the Air Force Research Laboratory equipment and the high fidelity simulator MultiU A V2. I. INTRODUCTIONThe problem addressed in this paper is the control of a Micro Unmanned Aerial Vehicle (MAV) for the purpose of obtaining video footage of a set of known ground targets with preferred azimuthal viewing angles, using onboard body-frame fixed cameras. Control is exercised only through the selection of waypoints, without modification of the preexisting autopilot and waypoint following capability. Specifically, we investigate problems and potential solutions of performing this task in the presence of a known constant wind field.Algorithms for flight path guidance and synchronous target observations, in the presence of wind, have been studied in several works [11], [12], [10] by the development of guidance laws that accommodate actuated cameras. The path planner we develop generates a waypoint sequence with the primary objective of giving rise to a trajectory such that each target is viewed by one of the onboard cameras from the preferred viewing angle. As a secondary priority, the path planner should minimize the total flight time of the resulting trajectory in order that intelligence is gathered in a timely manner. Moreover it is our aim to design a path planner which reflects the underlying behavior of the onboard

In this paper we consider the following problem. A number of Uninhabited Aerial Vehicles (UAVs), modeled as vehicles moving at constant speed along paths of bounded curvature, must visit stochastically-generated targets in a convex, compact region of the plane. Targets are generated according to a spatio-temporal Poisson process, uniformly in the region. It is desired to minimize the expected waiting time between the appearance of a target, and the time it is visited. We present partially centralized algorithms for UAV routing, assigning regions of responsibility to each vehicle, and compare their performance with respect to asymptotic performance bounds, in the light and heavy load limits. Simulation results are presented and discussed.

Consider a routing problem for a team of vehicles in the plane: target points appear randomly over time in a bounded environment and must be visited by one of the vehicles. It is desired to minimize the expected system time for the targets, i.e., the expected time elapsed between the appearance of a target point, and the instant it is visited. In this paper, such a routing problem is considered for a team of Uninhabited Aerial Vehicles (UAVs), modeled as vehicles moving with constant forward speed along paths of bounded curvature. Three algorithms are presented, each designed for a distinct set of operating conditions. Each is proven to provide a system time within a constant factor of the optimal when operating under the appropriate conditions. It is shown that the optimal routing policy depends on problem parameters such as the workload per vehicle and the vehicle density in the environment. Finally, there is discussion of a phase transition between two of the policies as the problem parameters are varied. In particular, for the case in which targets appear sporadically, a dimensionless parameter is identified which completely captures this phase transition and an estimate of the critical value of the parameter is provided. Special Euclidean group in two dimensions (i.e., the group of rigid planar motions) t time, s T the steady-state system time, s v vehicle speed, ms −1

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