Discretized optimal control approach for dynamic multi-agent decentralized coverage

Nguyen, Minh Tri; Rodrigues, Luís; Maniu, Cristina Stoica; Olaru, Sorin

doi:10.1109/isic.2016.7579984

Cited by 20 publications

(17 citation statements)

References 17 publications

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“…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). The main difficulty is related to the computation of the center of mass.…”

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%

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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“…The optimal UAV deployment is then defined by the minimum average communication power (distortion) to serve GTs distributed by a density function λ in Ω with a minimum given data rate R b . Hereby, each GT will select the UAV which requires the smallest communication power, resulting in so called generalized Voronoi (quantization) regions of Ω, as used in [1]- [3], [5]- [9]. We also assume that the communication between all users and UAVs is orthogonal, i.e., separated in frequency or time (slotted protocols).…”

Section: System Modelmentioning

confidence: 99%

Quantizers with Parameterized Distortion Measures

Guo

Walk

Jafarkhani

2019

2019 Data Compression Conference (DCC)

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In many quantization problems, the distortion function is given by the Euclidean metric to measure the distance of a source sample to any given reproduction point of the quantizer. We will in this work regard distortion functions, which are additively and multiplicatively weighted for each reproduction point resulting in a heterogeneous quantization problem, as used for example in deployment problems of sensor networks. Whereas, normally in such problems, the average distortion is minimized for given weights (parameters), we will optimize the quantization problem over all weights, i.e., we tune or control the distortion functions in our favor. For a uniform source distribution in one-dimension, we derive the unique minimizer, given as the uniform scalar quantizer with an optimal common weight. By numerical simulations, we demonstrate that this result extends to two-dimensions where asymptotically the parameter optimized quantizer is the hexagonal lattice with common weights. As an application, we will determine the optimal deployment of unmanned aerial vehicles (UAVs) to provide a wireless communication to ground terminals under a minimal communication power cost. Here, the optimal weights relate to the optimal flight heights of the UAVs.

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Discretized optimal control approach for dynamic multi-agent decentralized coverage

Cited by 20 publications

References 17 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

Quantizers with Parameterized Distortion Measures

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