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This project developed an operator error model for improving the accuracy of the operator task of discriminating objects in multiple UAV videos on the Vigilant Spirit Control Station (VSCS) ground station. This error model is idiographic in the sense that it is tailored for a specific individual. The operator error model is used in a stochastic controller which decides, based on the model uncertainty and the operator input of target/non-target, whether to revisit the object to obtain additional information or to continue to the next object. The stochastic controller computes the probability of a target given the available evidence and the reliability of the evidence as per the operator error model. Over 120 operator-in-the-loop tests were conducted to obtain sufficient data to be able to estimate the false alarm (FA) and missed detection (MD) rates over four variables (altitude, aspect angle, dwell time, workload). For a 50% target density scenario where the operator discrimination task is fairly difficult, the integrated human behavior model and stochastic controller (IHBMSC) was shown to reduce the FA rate from 35% to 5%. Also, the IHBMSC performance was found to be quite robust for lower target densities (5%) and different operators., Prepared in collaboration with the Supervisory Control and Cognition Branch, Warfighter Interface Division, Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH, the Air Force Institute of Technology, Wright-Patterson Air Force Base, OH, and InfoSciTex Corporation, Dayton, OH.

A very important sub-problem in the task assignment problem for unmanned air vehicles (UAVs) is the evaluation of costs for the state transitions of a directed graph. Usually a Dubins vehicle flying in the absence of wind is considered in the computation of costs. However, when a prevailing wind vector field is considered, the costs take on very different values and the task assignment problem can have very different solutions. In this paper, we consider the problem of constructing minimum time trajectories for a Dubins vehicle in the presence of a time varying wind vector field. We present results on the existence and uniqueness of minimum-time solutions for a Dubins vehicle flying in a general time-varying wind vector field under some technical conditions. These results extend the conclusions of the well-known Dubins theorem. We also propose an algorithm for obtaining the minimum-time solution for an UAV and prove its convergence. We also present the results of numerical experiments that show that the importance of considering wind vector fields while planning the tour for an UAV., Prepared in collaboration with Texas Tech Univ., Lubbock, TX. Submitted for publication in the AIAA Journal of Guidance, Control, and Dynamics.

For a team of unmanned aerial vehicles (UAVs) to deceive a radar network by generating a phantom track requires a high degree of cooperation due to: 1) dynamic constraints imposed mainly by the UAVs and 2) strong coupling caused by the phantom. We first review three works that have addressed this problem, namely two one-step look-ahead algorithms and an optimal solution with full time horizon; original simulations of each are included. Second, since the one-step algorithms do not satisfy the UAV and/or phantom constraints in many cases, we design a new algorithm that does, as shown by simulation. Finally, we add a multi-step look-ahead for the UAV constraints and modify the algorithm so that an operator can dynamically add objective waypoints for the phantom., Proceedings of the 2007 American Control Conference.

This paper addresses complexity and coupling issues in cooperative decision and control of distributed autonomous UAV teams. In particular, the recent results obtained by the in-house research team are presented. Hierarchical decomposition is implemented where team vehicles are allocated to subteams using set partition theory. Results are presented for single assignment and multiple assignment using network flow and auction algorithms. Simulation results are presented for wide area search munitions where complexity and coupling are incrementally addressed in the decision system, yielding radically improved team performance., The original document contains color images. Conference paper published in the Proceedings of the American Control Conference, 8-10 May 2002. Prepared in collaboration with Texas A&M University, Brigham Young University, AFIT/ENG, Cornell University, Veridian Inc, and North Dakota State University.

This paper presents a path planning and path-elongation method for wide area search munitions, a type of small Unmanned Air Vehicle. Variable-length paths are necessary to allow sufficient flexibility for efficient task assignment meeting timing constraints for UAVs. Five specific cases are developed that identify the best method of elongation based only on the initial position and heading of the vehicle with respect to the target. The cases include one linear direct method of path elongation and four cases which use an iterative approach for the nonlinear path elongation. The iterative methods are similar to a Newton-Raphson search over a function for a specific value (a path of the desired length). The function searched are the path length vs. the delay used, are monotonically increasing, and are very well behaved. The result is fast convergence to a small window around the desired value (path length). Multiple aspects of the path elongation program are presented. First, the problem setup is reviewed which includes the correction to the heading algorithm and the definition of the different cases and their associated elongation methods. The process of the iterative method is described and the results of the individual methods are presented. Results are shown for elongated paths for each of the five cases, and simulation results are shown using the path elongation algorithms in a task assignment problem., Presented at the AIAA Guidance Navigation and Control Conference, Austin, TX, August 11-14, 2003. This material is declared a work of the U.S, Government and is not subject to copyright protection in the United States.

A communication model is incorporated into the MultiUAV simulation package for wide area search munitions. This model is used to study the effect of communication delays on the performance of an iteractive network flow optimization model that results in a sequence of linear programs for the optimal allocation of consecutive assignments. Fixed communication delays are considered. The resulting delayed system performance is compared to the performance of a system with a perfect communications model for a set of vehicle-target scenarios to determine the effect on attack and verify task completion., Presented at the AIAA Guidance Navigation and Control Conference, Austin, TX, August 11-14, 2003.

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 estimated time of arrival (ETA) in each iteration. Periodically re-solving the overall optimization problem as new targets are discovered results in coordinated action by the search munitions. Variable path lengths are used to improve overall performance and prevent infeasibility. Simulation results are presented for a swarm of eight vehicles searching an area containing multiple potential targets., Presented at the American Control Conference held in Denver, CO on 4-6 Jun 2003. Prepared in cooperation with Veridian, Inc., Wright-Patterson AFB, OH and the Dept. of Mechanical Engineering, Brigham Young Univ., Provo, Utah.

This paper addresses the problem of task allocation for wide area search munitions. The munitions are required to search for, classify, attack, and perform battle damage assessment on 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. Periodically re-solving this optimization problem results in coordinated action by the search munitions. The network optimization modes can be initialized such that multiple vehicles can be assigned to service a single target. Memory of previous task assignments is included in the task benefit calculations to reduce churning due to frequent reassignments. Simulation results are presented for a swarm of eight vehicles searching an area containing three potential targets. All targets are quickly classified, attacked, and verified as destroyed.

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 interactively 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., Prepared in collaboration with Veridian Inc., Wright-Patterson AFB, OH.

This documents contains the proceedings of Qualitative Feedback Theory (QFT) Symposium at Hope Hotel, WPAFB, OH on 2-4 August 1992. Included are QFT tutorials, and session papers on application to a wide variety of physical systems. The sessions include (1) Theory and Design, (2) Aerospace, and (3) Industrial applications. QFT is a Multiple-Input Multiple Output (MIMO) frequency based synthesis technique that explicitly employs quantitative performance standards and plant uncertainty during controller design., Prepared in collaboration with Purdue Univ., West Lafayette, IN. School of Mechanical Engineering.