An Optimal Control Approach to Decentralized Energy-Efficient Coverage Problems

Moarref, Miad; Rodrigues, Luís

doi:10.3182/20140824-6-za-1003.01625

Cited by 22 publications

(30 citation statements)

References 12 publications

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“…Many recent research works focus on driving the MAS into a Centroidal Voronoi Configuration (CVC) in which the position of each agent coincides with the center of mass of its associated Voronoi cell 1 Cortes et al (2002), Kwok and Martinez (2010), Yan and Mostofi (2012), Song et al (2014). Moarref and Rodrigues (2014) proposes an optimal decentralized control to deal with energy-efficient constraints. The main results are developed for continuous-time systems, while a discrete-time version is developed by Nguyen et al (2016b).…”

Section: Introductionmentioning

confidence: 99%

Optimization-based control for Multi-Agent deployment via dynamic Voronoi partition

2017

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This paper presents a novel decentralized control law for the Voronoi-based deployment of a Multi-Agent dynamical system. At each time instant, a bounded convex polyhedral working region is partitioned using a Voronoi algorithm providing the agents with non-overlapping functioning zones. The agents' deployment objective is to drive the entire system into a stable static configuration which corresponds to a stationary maximal coverage. This goal is achieved by using local stabilizing feedback control ensuring the convergence of each agent towards a centroid of its associated functioning zone. The proposed approach considers the Chebyshev center as centroidal point of each Voronoi cell.

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Section: Introductionmentioning

confidence: 99%

Optimization-based control for Multi-Agent deployment via dynamic Voronoi partition

2017

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“…The continuous sensor relocation in MWSNs is an interesting future work. dynamics, velocity, and/or second order dynamics, acceleration [8], [9], [16]. However, in the discrete time system, sensors only communicate with their neighbors at some discrete time instances, and their relocation is divided into multiple steps [12].…”

Section: A Problem Formulationmentioning

confidence: 99%

“…It is mainly used when there is no specific target. The widely used CVT model (see more details in Section II) can be presented as a quantizer with the sensing uncertainty as its distortion [1]- [12], [16]- [20].…”

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confidence: 99%

Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range

Guo¹,

Jafarkhani²

2019

IEEE Trans. Wireless Commun.

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We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy consumption, and connectivity, have attracted plenty of attention, but the interaction of these factors is not well studied. To take all the three factors into consideration, we model the sensor deployment problem as a constrained source coding problem. Our goal is to find an optimal sensor deployment (or relocation) to optimize the sensing quality with a limited communication range and a specific network lifetime constraint. We derive necessary conditions for the optimal sensor deployment in both homogeneous and heterogeneous MWSNs. According to our derivation, some sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these necessary conditions, we design both centralized and distributed algorithms to provide a flexible and explicit trade-off between sensing uncertainty and network lifetime. The proposed algorithms are successfully extended to more applications, such as area coverage and target coverage, via properly selected density functions. Simulation results show that our algorithms outperform the existing relocation algorithms.

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“…There are various works mentioned in the control literature employing dynamic Voronoi partition [8] as a conventional mathematical tool to approach a stationary configuration over a given bounded region. Many recent research works focus on driving the MAS into a Centroidal Voronoi Configuration (CVC) in which the position of each agent coincides with the centroid (center of mass) of its associated Voronoi cell 1 [9], [3], [10], [11]. The M.T.…”

Section: Introductionmentioning

confidence: 99%

“…Other results [13] use Mixed-Integer Programming to minimize the energy consumption. In the notable recent work [10], the coverage task is formulated as an optimal control problem, which considers energy constraints in the control law computation. The authors show that the Lloyd's algorithm-based local control may waste too much energy and they propose an improved energy-efficient optimal control.…”

Section: Introductionmentioning

confidence: 99%

Discretized optimal control approach for dynamic multi-agent decentralized coverage

Nguyen¹,

Rodrigues²,

Maniu³

et al. 2016

2016 IEEE International Symposium on Intelligent Control (ISIC)

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This paper presents a novel discrete-time decentralized control law for the Voronoi-based self-deployment of a Multi-Agent dynamical system. The basic control objective is to let the agents deploy into a bounded convex polyhedral region and maximize the coverage quality by computing locally the control action for each agent. The Voronoi tessellation algorithm is employed to partition dynamically the deployed region and to allocate each agent to a corresponding bounded functioning zone at each time instant. The control synthesis is then locally computed based on an optimal formulation framework related to the Lloyd's algorithm but according to the discrete-time agent's dynamics equation. The performance of the discretized optimal solution will be demonstrated via an illustrative example.

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An Optimal Control Approach to Decentralized Energy-Efficient Coverage Problems

Cited by 22 publications

References 12 publications

Optimization-based control for Multi-Agent deployment via dynamic Voronoi partition

Optimization-based control for Multi-Agent deployment via dynamic Voronoi partition

Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range

Discretized optimal control approach for dynamic multi-agent decentralized coverage

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