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Research Papers

Distributed Full-State Observers With Limited Communication and Application to Cooperative Target Localization

[+] Author and Article Information
Davide Spinello

Department of Mechanical Engineering,
University of Ottawa,
Ottawa, ON K1N 6N5, Canada
e-mail: dspinell@uottawa.ca

Daniel J. Stilwell

The Bradley Department of Electrical &
Computer Engineering,
Virginia Polytechnic Institute & State University,
Blacksburg, VA 24060
e-mail: stilwell@vt.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 11, 2013; final manuscript received November 18, 2013; published online March 4, 2014. Assoc. Editor: Sergey Nersesov.

J. Dyn. Sys., Meas., Control 136(3), 031022 (Mar 04, 2014) (14 pages) Paper No: DS-13-1115; doi: 10.1115/1.4026173 History: Received March 11, 2013; Revised November 18, 2013

We present a fully decentralized motion control algorithm for the coordination of platoons of mobile agents with highly restricted communication capabilities. In order to address very low bandwidth communication between agents and time varying communication network topologies, we utilize a distributed full-state observer onboard each agent. Agent motion and data fusion algorithms are implemented locally by each agent based on the state of the local full-state estimator. Although no separation principle exists between decentralized agent motion control and distributed data fusion in general, we introduce a gradient-based framework in which simultaneously we achieve asymptotic agreement among full-state estimators and convergence of desired agent motion.

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Figures

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Fig. 1

Schematics of the networked system with intermittent communications and symbols used to describe it. Each agent maintains a local representation of the states of all the other agents in the group. Self representations of the states correspond to true states. Symbols aij are indicators to represent the existence of a link among two nodes at a given time. Through directed communication events the agents share information in the form of a function γ of their true state.

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Fig. 2

Snapshot of the geometric configuration of a moving sensor

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Fig. 3

Trajectories of sensors estimating the position of a static target, with output equal to the sensors position

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Fig. 4

Time history of ∑i=13 cos 2βi[k] (solid line) and ∑i=13 sin 2βi[k] (dashed line)

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Fig. 5

Time history of the functions J(q∧i) in Eq. (36) for three sensors with output equal to the sensor position

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Fig. 6

Time history of the error in the estimation of the state of sensor starting at (−20 m, −15 m)

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Fig. 7

Trajectories of sensors estimating the position of a static target, with output equal to the relative distance to the target

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Fig. 8

Time histories of the functions J(q∧i) in Eq. (36) for three sensors with output equal to the relative distance to the target

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Fig. 9

Time history of the error in the output estimation of sensor starting at (−20 m, −15 m)

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Fig. 10

Time histories of the functions J(q∧i) in Eq. (36) for three sensors with output equal to the state of the sensor and circulant communication events every twenty time steps

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Fig. 11

Time histories of the errors in the estimation of the state of sensor starting at (−20 m, −15 m) with circulant communication events every twenty time steps

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Fig. 12

Time histories of the target state estimates x∧i for three mobile sensors sharing information every twenty updating time steps. The dashed line represents the true state of the target

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Fig. 13

Trajectories of sensors estimating the position of a static target, with output equal to the state of the sensor. Solid lines refer to trajectories generated by the distributed observer proposed here, and dashed lines refer to trajectories generated by the same gradient descent feedback control with complete communication network.

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