The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTESPresented at the AIAA Guidance Navigation and Control Conference, Monterey, CA, August 5-8, 2002. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. ABSTRACT (Maximum 200 Words)This paper addresses the problem of task allocation for wide area search munitions. The munitions are required to search for, classify, attack, and verify the destruction of potential targets. It is assumed that target field information is communicated between all elements of the swarm. A network flow optimization model is used to develop a linear program for optimal resource allocation. This method can be used to generate a tour of several assignments to be performed consecutively, by running the assignment iteratively and only updating the assigned task with the shortest ETA in each iteration. Periodically re-solving the overall optimization problem results in coordinated action by the search munitions. Simulation results are presented for a swarm of eight vehicles searching an area containing three potential targets. All targets are quickly serviced without using up an excessive amount of potential search time. AbstractThis paper addresses the problem of task allocation for wide area search munitions. The munitions are required to search for, classify, attack, and verify the destruction of potential targets. It is assumed that target field information is communicated between all elements of the swarm. A network flow optimization model is used to develop a linear program for optimal resource allocation. This method can be used to generate a "tour" of several assignments to be performed consecutively, by running the assignment iteratively and only updating the assigned task with the shortest ETA in each iteration. Periodically re-solving the overall optimization problem results in coordinated action by the search munitions. Simulation results are presented for a swarm of eight vehicles searching an area containing three potential targets. All targets are quickly serviced without using up an excessive amount of potential search time.
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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
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