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ABSTRACT: 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<sub>A</sub>V2.
American Control Conference, 2007. ACC '07; 08/2007
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ABSTRACT: This paper develops a method of assignment and path allocation that incorporates a priori collision avoidance and operator workload reduction in assigning multiple tasks to cooperative unmanned aerial vehicles (UAV). The problem is posed as a combinatorial optimization problem. A branch and bound tree search algorithm is implemented for a satisficing solution using a cost function that integrates distance travelled, proximity to other UAVs, and target visitation times. The results demonstrate that the assigned path is near optimal with respect to distance travelled, significantly increases the expected proximity distance to other UAVs, and significantly increases the difference between visitation times of targets. The algorithm runs in less than a tenth of a second allowing on the fly replanning.
American Control Conference, 2007. ACC '07; 08/2007
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ABSTRACT: This paper describes a branch and bound optimization algorithm for assigning cooperating homogeneous uninhabited aerial vehicles to multiple tasks. The combinatorial optimization problem is posed in the form of a decision tree, the structure of which enforces the required group coordination and precedence for cooperatively performing the multiple tasks. For path planning a Dubin's car model is used so that the vehicles' dynamic constraint, of minimum turning radius, is taken into account. The proposed optimization algorithm is initialized by a best-first search and candidate optimal solutions serve as a monotonically decreasing upper bound for the assignment cost. Euclidean distances are used for estimating the path length encoded in branches of the tree that have not yet been evaluated by the computationally intensive Dubin's optimization subroutine. This provides a lower bound for the cost of unevaluated assignments. We apply these upper and lower bounding procedures iteratively on active subsets within the feasible set, enabling efficient pruning of the solution tree. Using Monte Carlo simulations the performance of the search algorithm is analyzed for two different cost functions. It is shown that the algorithm's convergence rate is dependent on the optimized cost function of the cooperative mission
American Control Conference, 2006; 07/2006
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ABSTRACT: A multiple task assignment problem for cooperating uninhabited aerial vehicles is posed as a combinatorial optimization problem. A genetic algorithm for assigning the multiple agents to perform multiple tasks on multiple targets is proposed. The algorithm allows efficiently solving this NP-hard problem that has prohibitive computational complexity for classical combinatorial optimization methods. It also allows taking into account the unique requirements of the scenario such as task precedence and coordination, timing constraints, and flyable trajectories. The performance of the algorithm is compared to that of deterministic branch and bound search and stochastic random search methods. Monte Carlo simulations demonstrate the viability of the genetic algorithm, providing good feasible solutions quickly. Moreover, it converges near to the optimal solution considerably faster than the other methods for some test cases. This makes real-time implementation for high dimensional problems feasible.
American Control Conference, 2005. Proceedings of the 2005; 07/2005
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ABSTRACT: This paper describes the MultiUAV2 simulation and how it has been applied to cooperative control of autonomous uninhabited air vehicles (UAV). MultiUAV2 is a simulation based on SIMULINK, Matlab, and C++ that is capable of simulating multiple UAVs which cooperate to accomplish tactical missions. First there is a discussion of cooperative control of UAVs and then the background of the MultiUAV2 simulation. Next, the simulated mission is explained, including how users can introduce new missions. Next, there are descriptions of the major elements of MultiUAV2, which are: targets/threats, vehicle dynamics, sensors, communications and cooperative assignment algorithms. In the final section, an example of the simulation event flow is presented.
American Control Conference, 2005. Proceedings of the 2005; 07/2005
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ABSTRACT: A novel decision-estimation architecture for a team of agents cooperating under communication imperfections is presented. The scenario of interest is that of a group of uninhabited aerial vehicles performing cooperative task assignment under communication delays. In the proposed architecture, each UAV in the group runs multiple filters in parallel on: its own states, teammates' states, and its states as viewed by teammates. The estimation of team members' states allows each UAV to synchronize the transmitted cost of performing known tasks, obtained from the different group members, to a common time base. It also enables estimating the expected cost for teammates to prosecute new tasks. Thus, the group performance, under communication imperfections, can be improved. For the estimation, two different algorithms are proposed. The first is communication efficient in which asynchronous information updates are sent to the network by individual members based on the value of the information to the rest of the group. Taking into account that the plan and plant of each UAV are known to the rest of the group, improves the overall estimation process. Moreover, it allows proposing another, computationally efficient, estimation algorithm utilizing synchronous information updates.
American Control Conference, 2005. Proceedings of the 2005; 07/2005
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ABSTRACT: We investigate the effects of target arrival rate on the communication and mission performance of cooperatively controlled uninhabited aerial vehicles with task allocation performed by iterative network flow. Specifically, we quantify the effect of arrival rate on observed statistics of communication and mission performance. The statistics of interest are peak communication data rate, execution defects. The effects are seen in a series of vehicle-target scenarios simulated in the US Air Force Research Laboratory's MultiUAV environment.
Decision and Control, 2004. CDC. 43rd IEEE Conference on; 01/2005
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ABSTRACT: This paper describes an algorithm that generates vehicle task assignments for autonomous uninhabited air vehicles in cooperative missions. The algorithm uses a state-space best-first search of a tree that incorporates all of the constraints of the assignment problem. Using this algorithm a feasible solution is generated immediately, that monotonically improves and eventually converges to the optimal solution. Using Monte Carlo simulations the performance of the search algorithm is analyzed and compared to the desirable assignment algorithm attributes. It is shown that the proposed deterministic search method can be implemented for given run times, providing good feasible solutions.
Decision and Control, 2004. CDC. 43rd IEEE Conference on; 01/2005
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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 iteratively and only updating the assigned task with the shortest estimated time-of-arrival (ETA) in each iteration. Periodically resolving 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 guarantee computation of feasible paths. Simulation results are presented for a swarm of eight vehicles searching an area containing multiple potential targets.
American Control Conference, 2003. Proceedings of the 2003; 07/2003
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ABSTRACT: This paper describes MultiUAV, a simulation that is capable of simulating multiple unmanned aerospace vehicles which cooperate to accomplish a predefined mission. The simulation was constructed using the Mathwork's Simulink simulation software. Construction of the simulation satisfied the need for a simulation environment that researchers can use to implement and analyze cooperative control algorithms. The simulation is implemented in a hierarchical manner with inter-vehicle communication explicitly modeled. During construction of MultiUAV, issues concerning memory usage and functional encapsulation were addressed. MultiUAV includes plotting tools and links to an external program for post-simulation analysis. Each of the vehicle simulations include six-degree-of-freedom dynamics and embedded flight software. The embedded flight software consists of a collection of managers (agents) that control situational awareness and responses of the vehicles. Managers included in the simulation are: tactical maneuvering, sensor, target, cooperation, route and weapons.
Simulation Conference, 2002. Proceedings of the Winter; 01/2003
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ABSTRACT: This paper describes MultiUAV, a simulation that is capable of simulating multiple unmanned aerospace vehicles which cooperate to accomplish a predefined mission. The simulation was constructed using the Mathwork's Simulink simulation software. Construction of the simulation satisfied the need for a simulation environment that researchers can use to implement and analyze cooperative control algorithms. The simulation is implemented in a hierarchical manner with inter-vehicle communication explicitly modeled. During construction of MultiUAV, issues concerning memory usage and functional encapsulation were addressed. MultiUAV includes plotting tools and links to an external program for post-simulation analysis. Each of the vehicle simulations include six-degree-of-freedom dynamics and embedded flight software. The embedded flight software consists of a collection of managers (agents) that control situational awareness and responses of the vehicles. Managers included in the simulation are: Tactical Maneuvering, Sensor, Target, Cooperation, Route and Weapons.
