Distributed collaboration with limited communication using mission state estimates

Godwin, Mark F.; Spry, S.; Hedrick, J. Karl

doi:10.1109/acc.2006.1656520

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…In [8], a taxonomy of task allocation problems is given, dividing problems into groups based on, among other things, the number of tasks a robot can execute, and the number of robots required for a task. In papers such as [9]- [11], advanced heuristic methods are developed, and their effectiveness is demonstrated through simulation or real world implementation. In [12] the auction algorithm is adapted to solve a task allocation problem in the presence of communication delays.…”

Section: Introductionmentioning

confidence: 99%

Monotonic Target Assignment for Robotic Networks

Smith

Bullo

2009

IEEE Trans. Automat. Contr.

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Abstract-Consider an equal number of mobile robotic agents and distinct target locations dispersed in an environment. Each agent has a limited communication range and either: 1) knowledge of every target position or 2) a finite-range sensor capable of acquiring target positions and no a priori knowledge of target positions. In this paper we study the following target assignment problem: design a distributed algorithm with which the agents divide the targets among themselves and, simultaneously, move to their unique target. We evaluate an algorithm's performance by characterizing its worst-case asymptotic time to complete the target assignment; that is the task completion time as the number of agents (and targets) increases, and the size of the environment scales to accommodate them. We introduce the intuitive class of monotonic algorithms, and give a lower bound on its worst-case completion time. We design and analyze two algorithms within this class: the ETSP ASSGMT algorithm which works under assumption 1), and the GRID ASSGMT algorithm which works under either assumption 1) or 2). In "sparse environments," where communication is infrequent, the ETSP ASSGMT algorithm is within a constant factor of the optimal monotonic algorithm for worst-case initial conditions. In "dense environments," where communication is more prevalent, the GRID ASSGMT algorithm is within a constant factor of the optimal monotonic algorithm for worst-case initial conditions. In addition we characterize the performance of the GRID ASSGMT algorithm for uniformly distributed targets and agents, and for the case when there are more agents than targets.

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Section: Introductionmentioning

confidence: 99%

Monotonic Target Assignment for Robotic Networks

Smith

Bullo

2009

IEEE Trans. Automat. Contr.

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“…The information structure for distributed task allocation resides in the Mission State Estimate (MSE) [16]. MSE is wrapped on a message and is broadcast periodically by each UAV.…”

Section: E Collaborative Control and Sensingmentioning

confidence: 99%

The C3UV testbed for collaborative control and information acquisition using UAVs

Pereira

Hedrick

Sengupta

2013

2013 American Control Conference

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Abstract-In this paper we introduce the Center for Collaborative Control of Unmanned Vehicles (C3UV) testbed for collaborative information acquisition.The C3UV testbed has been used for demonstrating a wide range of information-oriented applications executed by collaborative teams of Unmanned Aerial Vehicles (UAVs).This paper presents the C3UV testbed from an architectural stand-point. The testbed includes a estimation and control architecture and a software architecture. The estimation and control architecture is a set of components that can be composed to perform specific missions. The software architecture supports the execution of estimation and control components and implements the Collaborative Sensing Language -a language for highlevel specification of mission-level controllers for mobile sensor networks with ad-hoc resource pool and dynamic network topology.We show the use of different layers of the architecture using examples from our field experiments and demonstrations. Our heterogeneous teams of UAVs perform several types of missions such as environmental monitoring, pedestrian search and tracking, and river mapping.

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“…Partition E into n regions and assign a vehicle to each region. 1 foreach region-vehicle pair do 2 As tasks arrive in the region, form sets of size M .…”

Section: The Tsp Partitioning Policymentioning

confidence: 99%

“…Partition E into n/k regions and assign one agent of each 1 type to each region. foreach region do 2 Form a queue for each of the K task types. Divide agents into teams to form required task types.…”

Section: Scheduled Task-specific Team Policymentioning

confidence: 99%

“…In [1] a taxonomy of task allocation problems is given, dividing problems into groups based on criteria such as the number of tasks a robot can execute, and the number of robots required for a task. In papers such as [2], [3], advanced heuristic methods are developed, and their effectiveness is demonstrated through extensive simulation or real world implementation.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic multi-agent team forming: Asymptotic results on throughput versus delay

Smith

Bullo

2008

2008 American Control Conference

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Abstract-In this paper we focus on problems in which tasks (demands for service) arrive in an environment sequentially over time. A task is completed when a robot (or team of robots) provides the required service, and the goal is to minimize the expected delay between a task's arrival, and its completion. We develop a general framework in which these problems can be described, and propose a set of scaling laws for studying the relationship between the number of robots, the expected task delay, and the task arrival rate. We describe two existing problems in our framework, namely the dynamic traveling repairperson problem, and the dynamic pickup delivery problem, and present their asymptotic performance. We then introduce the dynamic team forming problem, in which tasks require services that can be provided only through complex teams of heterogeneous robots. We determine a lower bound on the problem's achievable performance, and propose three policies for solving the problem. We show that for each policy, there is a broad class of tasks for which the policy's performance is within a constant factor the optimal.

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Distributed collaboration with limited communication using mission state estimates

Cited by 19 publications

References 10 publications

Monotonic Target Assignment for Robotic Networks

Monotonic Target Assignment for Robotic Networks

The C3UV testbed for collaborative control and information acquisition using UAVs

Dynamic multi-agent team forming: Asymptotic results on throughput versus delay

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