Corey Schumacher

Complexity in UAV cooperative control

This paper addresses complexity and coupling issues in cooperative decision and control of distributed autonomous unmanned aerial vehicle (UAV) teams. In particular, the recent results obtained by the inhouse research team are presented. Hierarchical decomposition is implemented where team vehicles are allocated to sub-teams using the set partition theory. Results are presented for single assignment and multiple assignments using the 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 a radically improved team performance.

TASK ALLOCATION FOR WIDE AREA SEARCH MUNITIONS VIA NETWORK FLOW OPTIMIZATION

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 programming problem for optimal resource allocation. Periodically re-solving this optimization problem results in coordinated action by the search munitions. Simulation results are presented for a swarm of eight vehicles searching an area containing five potential targets. Simulation results indicate the task allocation methodology presented in this paper results in an efficient use of available resources.

Task allocation for wide area search munitions

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 model 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.

Micro UAV Path Planning for Reconnaissance in Wind

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 MAVs 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 MultiUAV2.

Adaptive control of UAVs in close-coupled formation flight

This paper studies the control of multiple unmanned aerial vehicles (UAVs) flying in a close-coupled formation for the purposes of drag reduction. A controller design methodology for use in the trail vehicle in a two-UAV formation is presented. The LQR outer-loop tracks relative position commands and generates body-axis rate commands for the inner loop. The adaptive dynamic inversion inner-loop tracks these input commands using only minimal knowledge of the aircraft dynamics. The controller is tested in a two-vehicle formation flight simulation. Excellent command tracking and performance are achieved without use of specific knowledge of the formation flight effects. Simulation results demonstrate that the proposed controller design enables the trail UAV to maneuver in the lead UAVs wake, and to hold a desired position in the vortex.

Task Allocation for Wide Area Search Munitions via Iterative Network Flow

Abstract : 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.

Time-dependent cooperative assignment

The problem of assigning multiple agents to time-dependent cooperative tasks is addressed using a mixed-integer linear program. A time-dependent cooperative task is a task requiring multiple agents to perform separate sub tasks simultaneously or within some predetermined margin where agent availability to perform a subtask is limited to specific intervals in time. By separating the underlying calculation of agent availability and cost from the mechanism of assignment, a method to solve complex cooperative assignment problems can be formulated. A cooperative UAV target tracking/target prosecution scenario is presented to illustrate the assignment method.

UAV Task Assignment with Timing Constraints

Abstract : 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. We assume that target field information is communicated between all elements of the swarm. We generate a tour of optimal assignments for each vehicle using a Mixed Integer Linear Program, or MILP format. MILP can assign tasks that look infeasible, due to timing, by adding time to a UAVs path, and vehicle paths are then recalculated to match the required arrival times. The MILP formulation with variable arrival times provides an optimal solution to multiple-assignment problems for groups of UAVs with coupled tasks involving timing and task order constraints.

Missile Autopilot Designs Using H8 Control with Gain Scheduling and Dynamic Inversion

Twononlinearcontrollerdesignsarepresentedforabank-to-turn,air-to-airmissile.Thee rstcontrollerisagainscheduled H1 design, and the second is a nonlinear dynamic inversion design using a two-timescale separation. We carried out a number of time- and frequency-domain analysis procedures on the resulting designs and tested their performance on a nonlinear simulation of the missile. We compared the controller designs for nominal performance, robustness to uncertainties in the aerodynamic coefe cients, and sensitivity to measurement noise. The dynamic inversion controller was found to be signie cantly less robust to aerodynamic uncertainty. Using a π-analysis test on a linearization of the closed-loop dynamics with the dynamic inversion controller, we were able to e nd a destabilizing aerodynamic uncertainty for the full nonlinear system.

Optimization of air vehicles operations using mixed-integer linear programming

A scenario where multiple air vehicles are required to prosecute geographically dispersed targets is considered. Furthermore, multiple tasks are to be successively performed on each target, that is, the targets must be classified, attacked, and verified as destroyed. The optimal, for example, minimum time, performance of these tasks requires cooperation among the vehicles such that critical timing constraints are satisfied, that is, a target must be classified before it can be attacked, and an air vehicle is sent to a target area to verify its destruction only after the target has been attacked. In this paper, the optimal task assignment/scheduling problem is posed as a mixed-integer linear program (MILP). The solution of the MILP assigns all tasks to the vehicles and performs the scheduling in an optimal manner, including staged departure times. Coupled tasks involving timing and task order constraints are automatically addressed. When the air vehicles have sufficient endurance, the existence of a solution is guaranteed.