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Research Papers

Cloud-Supported Coverage Control for Persistent Surveillance Missions

[+] Author and Article Information
Jeffrey R. Peters

Department of Mechanical Engineering,
University of California,
Santa Barbara, CA 93106
e-mail: jrpeters@engr.ucsb.edu

Sean J. Wang

Department of Mechanical Engineering,
University of California,
Santa Barbara, CA 93106
e-mail: seanwang@umail.ucsb.edu

Amit Surana

Systems Department,
United Technologies Research Center,
East Hartford, CT 06118
e-mail: suranaa@utrc.utc.com

Francesco Bullo

Department of Mechanical Engineering,
University of California,
Santa Barbara, CA 93106
e-mail: bullo@engr.ucsb.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 25, 2016; final manuscript received January 18, 2017; published online May 24, 2017. Assoc. Editor: Jongeun Choi.

J. Dyn. Sys., Meas., Control 139(8), 081011 (May 24, 2017) (12 pages) Paper No: DS-16-1514; doi: 10.1115/1.4035874 History: Received October 25, 2016; Revised January 18, 2017

A cloud-supported coverage control scheme is proposed for multi-agent, persistent surveillance missions. This approach decouples assignment from motion planning operations in a modular framework. Coverage assignments and surveillance parameters are managed on the cloud and transmitted to mobile agents via unplanned and asynchronous exchanges. These updates promote load-balancing, while also allowing effective pairing with typical path planners. Namely, when paired with a planner satisfying mild assumptions, the scheme ensures that (i) coverage regions remain connected and collectively cover the environment, (ii) regions may go uncovered only over bounded intervals, (iii) collisions (sensing overlaps) are avoided, and (iv) for time-invariant event likelihoods, a Pareto optimal configuration is produced in finite time. The scheme is illustrated in simulated missions.

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References

Figures

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Fig. 1

When complete or pairwise coverage updates are impossible, two updates are required to move from the left-most to the right-most configuration. The left region is updated first, introducing a collision (redundant sensing) risk.

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Fig. 2

Illustration of the proposed strategy. The partitioning component (executed on the cloud) manages coverage regions and introduces logic to prevent collisions, while the trajectory planning component (executed on-board each agent) governs agent motion.

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Fig. 3

A four-agent example mission over a static Gaussian likelihood. Each agent's position, past trajectory, and active coverage region are shown with the shaded triangle, line, and squares, respectively.

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Fig. 4

The maximum amount of time that any subregion went uncovered in each of the 50 simulation runs (left), and the value of the cost H as a function of time, averaged over the same 50 runs (right)

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Fig. 5

Comparison between the (time-invariant) event likelihood Φ (left), and the proportion of time that some agent occupied each subregion after significant time has passed (10,000 units) (right)

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Fig. 6

Simplified example illustrating how Algorithm 1 manipulates timing parameters to prevent agent collisions

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Fig. 7

Comparison of coverage cost between Ref. [12] and Algorithm 1. Coverage costs are calculated with H min (see Ref. [12], Sec. II-C) on the left and with H (Sec. 4.2) on the right.

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Fig. 8

Coverage regions after the likelihood switches (see Fig. 9)

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Fig. 9

The initial and final likelihood Φ(⋅, t)

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Fig. 10

Evolution of the cost H using a piecewise-constant likelihood with 12 random switches (indicated by the stars) (left), and the average percent decrease in H following each switch (right)

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