Coordinated target assignment and intercept for unmanned air vehicles

Beard, Randal W.; McLain, Timothy W.; Goodrich, Michael A.; Anderson, Erik P.

doi:10.1109/tra.2002.805653

Cited by 621 publications

(165 citation statements)

References 27 publications

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“…In the recent past, considerable efforts have been devoted to the problem of how to cooperatively assign and schedule tasks that are defined over an extended geographical area (Alighanbari and How, 2008;Arslan et al, 2007;Beard et al, 2002;Moore and Passino, 2007;Smith and Bullo, 2009). In these papers, the main focus is in developing distributed algorithms that operate with knowledge about the task locations and with limited communication between robots.…”

Section: A Static and Dynamic Vehicle Routingmentioning

confidence: 99%

“…In particular, the instantaneous radius of curvature is constrained to be no less than ⇢. This model is often referred to as the Dubins vehicle, in recognition of Dubins' work in computing minimum-length paths for such model (Dubins, 1957), and is typically considered appropriate to model the kinematics of UAVs (Beard et al, 2002;Chandler et al, 2000). The UAVs are assumed to be identical, and have unlimited range.…”

Section: B Uav Routing With Motion Constraints and Unlimited Sensingmentioning

confidence: 99%

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UAV Routing and Coordination in Stochastic, Dynamic Environments

Enright¹,

Pavone

Savla

2014

Handbook of Unmanned Aerial Vehicles

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Recent years have witnessed great advancements in the science and technology for unmanned aerial vehicles (UAVs), e.g., in terms of autonomy, sensing, and networking capabilities. This chapter surveys algorithms on task assignment and scheduling for one or multiple UAVs in a dynamic environment, in which targets arrive at random locations at random times, and remain active until one of the UAVs flies to the target's location and performs an on-site task. The objective is to minimize some measure of the targets' activity, e.g., the average amount of time during which a target remains active. The chapter focuses on a technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. The main advantage of this approach is its ability to provide analytical estimates of the performance of the UAV system on a given problem, thus providing insight into how performance is affected by design and environmental parameters, such as the number of UAVs and the target distribution. In addition, the approach provides provable guarantees on the system's performance with respect to an ideal optimum. To illustrate this approach, a variety of scenarios are considered, ranging from the simplest case where one UAV moves along continuous paths and has unlimited sensing capabilities, to the case where the motion of the UAV is subject to curvature constraints, and finally to the case where the UAV has a finite sensor footprint. Finally, the problem of cooperative routing algorithms for multiple UAVs is considered, within the same queueing-theoretical framework, and with a focus on control decentralization.

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Section: A Static and Dynamic Vehicle Routingmentioning

confidence: 99%

Section: B Uav Routing With Motion Constraints and Unlimited Sensingmentioning

confidence: 99%

UAV Routing and Coordination in Stochastic, Dynamic Environments

Enright¹,

Pavone

Savla

2014

Handbook of Unmanned Aerial Vehicles

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“…Examples of such problems include multi-target intercept [1], terrain mapping [24], reconnaissance [28], and surveillance [36]. To achieve effective solutions, in general, a vehicle team needs to follow a cooperative policy.…”

Section: Introductionmentioning

confidence: 99%

“…The generation of such a policy has been the subject of a rich literature in cooperative control. A sample of the noteworthy work in this field includes a language for modeling and programming cooperative control systems [18], receding horizon control for multi-vehicle systems [9], non-communicative multi-robot coordination [21], hierarchical methods for target assignment and intercept [1], cooperative estimation for reconnaissance problems [28], mixed integer linear programming methods for cooperative control [33,13], the compilation on multi-robots in dynamics environments [22], and the compilation on cooperative control and optimization [26].…”

Section: Introductionmentioning

confidence: 99%

A decomposition approach to multi-vehicle cooperative control

Earl

D’Andrea

2007

Robotics and Autonomous Systems

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We present methods that generate cooperative strategies for multivehicle control problems using a decomposition approach. By introducing a set of tasks to be completed by the team of vehicles and a task execution method for each vehicle, we decomposed the problem into a combinatorial component and a continuous component. The continuous component of the problem is captured by task execution, and the combinatorial component is captured by task assignment. In this paper, we present a solver for task assignment that generates near-optimal assignments quickly and can be used in real-time applications. To motivate our methods, we apply them to an adversarial game between two teams of vehicles. One team is governed by simple rules and the other by our algorithms. In our study of this game we found phase transitions, showing that the task assignment problem is most difficult to solve when the capabilities of the adversaries are comparable. Finally, we implement our algorithms in a multi-level architecture with a variable replanning rate at each level to provide feedback on a dynamically changing and uncertain environment.

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“…Agents implement a consensus protocol to reach consensus, that is to (make their states) converge to a same value, called consensus-value, or group decision value [1]. Coordination of agents/vehicles is an important task in several applications including autonomous formation flight [2], [3], cooperative search of unmanned air-vehicles (UAVs) [4], [5], swarms of autonomous vehicles or robots [6], [7], multi-retailer inventory control [8], [9] and congestion/flow control in communication networks [10]. Actually, a central point in consensus problems is the connection between the graph structure, defined by the Laplacian Matrix, and delays or distortions in communication links [11].…”

Section: Introductionmentioning

confidence: 99%

Distributed Consensus in Networks of Dynamic Agents

Bauso

Giarré

Pesenti

Proceedings of the 44th IEEE Conference on Decision and Control

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Abstract-Stationary and distributed consensus protocols for a network of n dynamic agents under local information is considered. Consensus must be reached on a group decision value returned by a function of the agents' initial state values. As a main contribution we show that the agents can reach consensus if the value of such a function computed over the agents' state trajectories is time invariant. We use this basic result to introduce a protocol design rule allowing consensus on a quite general set of values. Such a set includes, e.g., any generalized mean of order p of the agents' initial states. We demonstrate that the asymptotical consensus is reached via a Lyapunov approach. Finally we perform a simulation study concerning the alignment maneuver of a team of unmanned air vehicles.

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Coordinated target assignment and intercept for unmanned air vehicles

Cited by 621 publications

References 27 publications

UAV Routing and Coordination in Stochastic, Dynamic Environments

UAV Routing and Coordination in Stochastic, Dynamic Environments

A decomposition approach to multi-vehicle cooperative control

Distributed Consensus in Networks of Dynamic Agents

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