A decomposition strategy for optimal coordination of unmanned air vehicles

McLain, Timothy W.; Chandler, P.; Pachter, Meir

doi:10.1109/acc.2000.878919

Cited by 62 publications

(24 citation statements)

References 6 publications

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“…; l n Þ and fJ i ðl i ; l Ài Þ; i 2 Ng in Eq. (31), the designed problem is consistent with a potential game as defined in Definition 5, and thus, the equilibrium for fJ i ðl i ; l Ài Þ; i 2 Ng is consistent with the global objective. Therefore, the cost function for the strategy l i of agent i can be simplified as…”

Section: Rendezvous Problem For Uavsmentioning

confidence: 87%

Consensus based on learning game theory with a UAV rendezvous application

Lin

Liu

2015

Chinese Journal of Aeronautics

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Multi-agent cooperation problems are becoming more and more attractive in both civilian and military applications. In multi-agent cooperation problems, different network topologies will decide different manners of cooperation between agents. A centralized system will directly control the operation of each agent with information flow from a single centre, while in a distributed system, agents operate separately under certain communication protocols. In this paper, a systematic distributed optimization approach will be established based on a learning game algorithm. The convergence of the algorithm will be proven under the game theory framework. Two typical consensus problems will be analyzed with the proposed algorithm. The contributions of this work are threefold. First, the designed algorithm inherits the properties in learning game theory for problem simplification and proof of convergence. Second, the behaviour of learning endows the algorithm with robustness and autonomy. Third, with the proposed algorithm, the consensus problems will be analyzed from a novel perspective.

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Section: Rendezvous Problem For Uavsmentioning

confidence: 87%

Consensus based on learning game theory with a UAV rendezvous application

Lin

Liu

2015

Chinese Journal of Aeronautics

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“…Utilize the improved Voronoi method with the targets assignment [13] and decomposition strategy for UCAVs cooperative path planning described in [2] to construct the cooperative path for each UCAV.…”

Section: Coordination Planning Optimization Algorithm For Ucavsmentioning

confidence: 99%

“…One of the primary challenges is to assign the UCAVs to the known missions/targets and to plan paths for all UCAVs such that the overall mission completion time is minimized and the UCAVs are exposed to as little threat as possible autonomously, particularly, in dynamic, poorly known or hazardous environments. New methods in awareness, planning and execution are required to coordinate these vehicles in real-time to ensure maximal efficiency of the team activities [2,3] . These algorithms must be fast to enable rapid re-planning in a dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Multiple-Target Attack of Multiple UCAVs Using Dynamic Awareness Based on DBN

Hou

Hou²

2008

2008 Second International Symposium on Intelligent Information Technology Application

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With new-arriving and updated fusion data, a dynamic awareness model against threats for UCAVs(Unmanned Combat Aerial Vehicles) based on Dynamic Bayesian Network (DBN) is constructed. Using parameters' change in relative DBN's transition, this dynamic awareness model leads to infer the main conditions that can be changed timely in multi-targets attack optimization within complicated aerospace surroundings. In the light of DBN's learning and reasoning algorithms, a coordination planning optimization algorithm for UCAVs is advanced. By means of this DBN awareness model, attack targets reassignment and cooperative path re-planning for UCAVs may be attained in accordance with given missions and guidelines. Simulations demonstrate that, with the dynamic awareness based on DBN, this autonomous planning method is valid and lays the foundations of a series of complicated missions that may be achieved successfully and autonomously for UCAVs.

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“…However, uneven probability of target distribution, dynamics in the information about target location, and the role of communication between vehicles are not addressed. In [4], the authors address the important problem of coordinating a rendezvous operation between UAVs. The problem is posed as an optimization problem that maximizes the survival of the UAV team.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, such operations have applications in military operations where interaction between components of large and complex systems are very common [2]. One specific application that has recently emerged due to advances in computing, wireless communications, and vehicular technologies, is the task of large scale deployment of multiple UAVs that can gather information about a dynamic threat/target environment, evade threats, and coordinate strikes against targets [3][4][5]. In this context we address the problem of a team of UAVs searching for targets in a search space.…”

Section: Introductionmentioning

confidence: 99%

Search using multiple UAVs with flight time constraints

Sujit

Ghose

2004

IEEE Trans. Aerosp. Electron. Syst.

121

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We consider a large scale system consisting of multiple unmanned aerial vehicles (UAVs) performing a search and surveillance task, based on the uncertainty map of an unknown region. The search algorithm is based on the k-shortest path algorithm that maximizes the effectiveness of the search in terms of searching through the maximum uncertainty region, given a constraint on the endurance time of the UAV and on the location of the base station from which the UAVs operate. These constraints set apart this class of problems from the usual search and surveillance problems. We compare the performance of this algorithm with a random search and a greedy strategy search. We also implement the algorithm for the case of multiple UAVs searching an unknown region. The cases of delayed and partial information are also considered. Simulation results that demonstrate the efficacy of the technique are also presented.

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A decomposition strategy for optimal coordination of unmanned air vehicles

Cited by 62 publications

References 6 publications

Consensus based on learning game theory with a UAV rendezvous application

Consensus based on learning game theory with a UAV rendezvous application

Coordinated Multiple-Target Attack of Multiple UCAVs Using Dynamic Awareness Based on DBN

Search using multiple UAVs with flight time constraints

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