Effective Coverage Control using Dynamic Sensor Networks with Flocking and Guaranteed Collision Avoidance

Hussein, Islam I.; Stipanović, Dušan M.

doi:10.1109/acc.2007.4282310

Cited by 27 publications

(53 citation statements)

References 7 publications

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“…1) A sensor is able to observe only one cell at a time. Extension to other sensor models that are capable of observing multiple cells at the same time (e.g., the sensor models with limited sensory range proposed in [3]- [5], [27]- [33]) is straightforward.…”

Section: B Sensor Modelmentioning

confidence: 99%

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Cost-Aware Bayesian Sequential Decision-Making for Search and Classification

Wang

Hussein

Brown

et al. 2012

IEEE Trans. Aerosp. Electron. Syst.

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A cost-aware Bayesian sequential decision-making strategy for domain search and object classification using a limited-range sensor is presented. On one hand, it is risky to allocate all available sensing resources at a single location while ignoring other regions. On the other hand, the sensor may miss-detect or miss-classify a critical object with insufficient observations. Therefore, we develop a decision-making strategy that balances the tolerable risks and the desired decision precision under limited resources.

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Section: B Sensor Modelmentioning

confidence: 99%

“…Other motion schemes, such as gradient-based, awareness-based, and information-driven control laws ( [3][4][5][27][28][29][30][31][32][33]), can be adopted without difficulty.…”

Section: B Sensor Modelmentioning

confidence: 99%

Cost-Aware Bayesian Sequential Decision-Making for Search and Classification

Wang

Hussein

Brown

et al. 2012

IEEE Trans. Aerosp. Electron. Syst.

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“…(5) and Eqn. (6) in terms of the representation of the areas A i through their extremes ( i , r i ).…”

Section: Perimeter Patrollingmentioning

confidence: 99%

“…The problem of patrolling has been and is extensively studied in the framework of mobile agents (see for example [5][9] [6] and references within) where a set of robots or other autonomous vehicles moves in the environment in order to attain the optimal area coverage in a dynamic sense and to act coordinately according to the information they gather and share. In this case, the mobile agents are typically constrained by their motion dynamics, by their sensing capabilities, and by the communication protocols.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent perimeter patrolling subject to mobility constraints

Alberton

Carli

Cenedese

et al. 2012

2012 American Control Conference (ACC)

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Abstract-In this paper we study the problem of real-time optimal distributed partitioning for perimeter patrolling in the context of multi-camera networks for surveillance. The objective is to partition a given segment into non-overlapping sub-segments, each assigned to a different camera to patrol. Each camera has both physical mobility range and limited speed, and it must patrol its assigned sub-segment by sweeping it back and forth at maximum speed. Here we first review the solution for the centralized optimal partitioning. Then we propose two different distributed control strategies to determine the extremes of the optimal patrolling areas of each camera. Both these strategies require only local communication with the neighboring cameras but adopt different communication schemes, respectively, symmetric gossip and asynchronous asymmetric broadcast. The first scheme is shown to be provably convergent to the optimal solution. Some theoretical insights are provided also for the second scheme whose effectiveness is validated through numerical simulations.

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“…Other sensor models that are capable of observing multiple cells at the same time (e.g., the sensor models with limited sensory range proposed in [1]- [3], [9]- [16]) can be used. We consider the extreme case in which the resources available are at a minimum (a single sensor as opposed to multiple cooperating ones).…”

Section: B Sensor Modelmentioning

confidence: 99%

Cost-aware Bayesian sequential decision-making for domain search and object classification

Wang

Hussein

Brown

et al. 2010

49th IEEE Conference on Decision and Control (CDC)

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Abstract-This paper focuses on the development of a cost-aware Bayesian sequential decision-making strategy for the search and classification of multiple unknown objects over a given domain using a sensor with limited sensory capability. Under such scenario, it is risky to allocate all the available sensing resources at a single location of interest, while ignoring other regions in the domain that may contain more critical objects. On the other hand, for the sake of finding and classifying more objects elsewhere, making a decision regarding object existence or its property based on insufficient observations may result in miss-detecting or missclassifying a critical object of interest. Therefore, a decisionmaking strategy that balances the desired decision accuracy and tolerable risks/costs is highly motivated. The strategy developed in this paper seeks to find and classify all unknown objects within the domain with minimum risk under limited resources.

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Effective Coverage Control using Dynamic Sensor Networks with Flocking and Guaranteed Collision Avoidance

Cited by 27 publications

References 7 publications

Cost-Aware Bayesian Sequential Decision-Making for Search and Classification

Cost-Aware Bayesian Sequential Decision-Making for Search and Classification

Multi-agent perimeter patrolling subject to mobility constraints

Cost-aware Bayesian sequential decision-making for domain search and object classification

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