Experimental Demonstration of Multiple Robot Cooperative Target Intercept

McLain, Timothy W.; Beard, Randal W.; Kelsey, Jed M.

doi:10.2514/6.2002-4678

Cited by 22 publications

(12 citation statements)

References 12 publications

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“…A utility function is designed so that locally interacting vehicles of identical type meet a global performances criterion. In [1] and [9], vehicles are assigned to known target locations, and the subproblems addressed in these works include target assignment, path planning, and trajectory following. Our focus is on building teams, and previous works that have addressed this problem (i.e., coalition formation) either use a centralized approach by deploying an auctioneer agent or incorporate game-theoretic methods, where a vehicle's utility often requires information about all other vehicles in the network (for a representative sample of coalition-formation algorithms, see [10][11][12][13][14][15]).…”

Section: Introductionmentioning

confidence: 99%

Multilevel Coalition Formation Strategy for Suppression of Enemy Air Defenses Missions

Haque

Egerstedt

Rahmani

2013

Journal of Aerospace Information Systems

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This paper investigates the problem of how to form coalitions in teams of heterogeneous vehicles. In particular, a coordination strategy is designed for unmanned vehicles to autonomously carry out the suppression of enemy air defenses (SEAD) mission, which would benefit from a heterogeneous network of unmanned vehicles to search and destroy threats in an unexplored area. Inspiration for this work is drawn from natural systems, and we consider the alliance-forming behavior of bottlenose dolphins as a guiding example. A two-phased approach is taken to develop the bioinspired strategy. First, in the context of multi-agent systems, a mathematical model is produced that expressively captures the alliance-forming behavior. Next, this model is tailored to the suppression of enemy air defenses mission: the target application. Advantages of using this bioinspired approach are discussed and simulations are provided to demonstrate its operation. Nomenclature A= coordinate set of areas to be explored by high-altitude unmanned aerial vehicles A 0 = coordinate set of areas within A to be explored by medium-altitude unmanned aerial vehicles C = fc 1 ; : : : ; c N C g, set of all combat unmanned aerial vehicles GV; E = graph on vertex set V, with edge set E ⊂ V × V = fh 1 ; : : : ; h N H g, set of all high-altitude unmanned aerial vehicles I = ordered set of all intelligence, surveillance, and reconnaissance teams; I ∈ I J 1 = J 1 ∈ J 1 , ordered set of all combat pairs J 2 = J 2 ∈ J 2 , ordered set of all combat triplets M = fm 1 ; : : : ; m N M g, set of all medium-altitude unmanned aerial vehicles N i = neighborhood set of vehicle i (those who can communicate with i) r = transition rule from one subgraph to another subgraph S 1 = ordered set of all higher-order teams of size 4 S 2 = ordered set of all higher-order teams of size 5 t = time, s Σ = vertex label set Φ = set of transition rules on subgraphs

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Section: Introductionmentioning

confidence: 99%

Multilevel Coalition Formation Strategy for Suppression of Enemy Air Defenses Missions

Haque

Egerstedt

Rahmani

2013

Journal of Aerospace Information Systems

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“…Several off-line methodologies for the MTSP in multitarget interception by multiple pursuers have been reported in the literature [11][12][13][14][15][16][17][18][19]. These methods perform pursuertarget assignments only once, at the start of travel, or when a pursuer has detected a nearby target.…”

Section: Introductionmentioning

confidence: 99%

“…These methods perform pursuertarget assignments only once, at the start of travel, or when a pursuer has detected a nearby target. While this one-time determination of pursuer-target pairings can be applicable to scenarios in which targets are static [13,16,19] or where the targets' trajectories can be defined a priori [18], they would not be suitable for targets with unknown trajectories and high maneuverability as the optimality of the initial pairings would not hold. In [20], an algorithm was presented for online re-assignment of unmanned aerial vehicles containing and intercepting evading targets using a differential games theory framework.…”

Section: Introductionmentioning

confidence: 99%

On-line task allocation for the robotic interception of multiple targets in dynamic settings

Sheridan

Kosicki

Liu³

et al. 2010

2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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This paper presents a novel on-line optimal task-allocation methodology for the interception of a group of mobile targets by a team of robotic pursuers. The mobile targets are highly maneuverable. A rollinghorizon concept is used to increase the depth of the online search for the optimal solution. Guidance theory is utilized to navigate each pursuer independently toward its assigned target. Extensive simulations and experiments have confirmed the proposed generic

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“…In [7] a scheme based on hierarchical decomposition is presented to allocate a sub-team of UAVs to a particular task. In [5] the authors present an algorithm such that the UAVs reach the target at the same instant of time by avoiding pop up threats whose locations are known a-priori. A coordinated search is made through a vornoi diagram.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents a strategy that is a variant of [5] in that the UAVs can be coordinated to arrive at a desired target location or respective target locations within a specified temporal upper bound. However unlike [5] the locations of the targets are not known a-priori.…”

Section: Introductionmentioning

confidence: 99%

Parametric control of multiple unmanned air vehicles over an unknown hostile territory

Krishna

Hexmoor

Pasupuleti

et al.

International Conference on Integration of Knowledge Intensive Multi-Agent Systems, 2005.

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A methodology for real-time control of unmanned air vehicles (UAV) in the absence of a-priori knowledge of hostile territory is presented. The control methodology generates a sequence of waypoints to be pursued by the UV until the goal is reached. The controller computes the waypoints every time new information is obtained regarding the presence or absence of a hostile agent at a particular grid of the territory. The waypoints are the result of an A* search. The cost function of the search is a weighted combination of dangers arising due to probability of being hit by theattacks of hostile terrestrial agents on the ground and the danger of UAV grounded as a result of its prolonged stay over the hostile territory. The multi-agency in the system is due to the broadcast of newly observed information by an UAV to its remaining counterparts. The sequence of waypoints defines the path of the UAV to its goal. By varying the corresponding weights the paths can be altered to obtain a particular performance criterion. Simulation results are presented for various parametric combinations to validate the methodology.

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Experimental Demonstration of Multiple Robot Cooperative Target Intercept

Cited by 22 publications

References 12 publications

Multilevel Coalition Formation Strategy for Suppression of Enemy Air Defenses Missions

Multilevel Coalition Formation Strategy for Suppression of Enemy Air Defenses Missions

On-line task allocation for the robotic interception of multiple targets in dynamic settings

Parametric control of multiple unmanned air vehicles over an unknown hostile territory

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