Distributed deployment algorithms for robotic visual sensor networks in non-convex environment

Gusrialdi, Azwirman; Zeng, Lu

doi:10.1109/icnsc.2011.5874908

Cited by 14 publications

(15 citation statements)

References 11 publications

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“…Due to the combinatorial search space size, an exact solution to (13) is challenging to obtain. However, a candidate solution can be obtained using a simple greedy algorithm and is referred to as a greedy solution.…”

Section: Greedy Algorithmmentioning

confidence: 99%

“…This total curvature measure has been used in [20] to establish better performance bounds compared to the conventional bound L C in the context of the coverage control problem in (13). Next, we propose another curvature concept to obtain even tighter performance bounds than L T .…”

Section: Total Curvaturementioning

confidence: 99%

“…Algorithm Greedy Method for Solving(13) Inputs: N, F D (Recall I := {A : A ⊆ F D , |A| ≤ N}). 2: Outputs: Greedy solution S G .…”

mentioning

confidence: 99%

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Optimal composition of heterogeneous multi-agent teams for coverage problems with performance bound guarantees

Welikala

Cassandras

2020

Automatica

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We consider the problem of determining the optimal composition of a heterogeneous multi-agent team for coverage problems by including costs associated with different agents and subject to an upper bound on the maximal allowable number of agents. We formulate a resource allocation problem without introducing additional non-convexities to the original problem. We develop a distributed Projected Gradient Ascent (PGA) algorithm to solve the optimal team composition problem. To deal with non-convexity, we initialize the algorithm using a greedy method and exploit the submodularity and curvature properties of the coverage objective function to derive novel tighter performance bound guarantees on the optimization problem solution. Numerical examples are included to validate the effectiveness of this approach in diverse mission space configurations and different heterogeneous multi-agent collections. Comparative results obtained using a commercial mixed-integer nonlinear programming problem solver demonstrate both the accuracy and computational efficiency of the distributed PGA algorithm.

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Section: Greedy Algorithmmentioning

confidence: 99%

Section: Total Curvaturementioning

confidence: 99%

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Optimal composition of heterogeneous multi-agent teams for coverage problems with performance bound guarantees

Welikala

Cassandras

2020

Automatica

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“…In order to take the collision avoidance between the sensors into account, the following potential function may be adopted [16,33] U ij (s i , s j ) = min 0,…”

Section: Collision Avoidancementioning

confidence: 99%

Exploiting the use of information to improve coverage performance of robotic sensor networks

Gusrialdi¹,

2014

IET Control Theory & Applications

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A robotic sensor network is advantageous in performing a coverage task compared to the static sensor network because of its ability to self-deploy and self-reconfigure. However, since the sensor has a limited sensing range, when mobile sensors are initially deployed, sensors located far away from the region of interest may not be able to self-deploy themselves, that is, participate in the coverage task. This results in a degradation of coverage performance by the robotic network. This paper proposes a novel algorithm in order to improve the coverage performance by the whole mobile sensor network by guaranteeing the participation of all sensors in the coverage task. The algorithm is a combination of the standard gradientbased coverage algorithm and a leader-following algorithm and is designed to maximise the joint detection probabilities of the events in the region of interest. As a strategy, first a set of leader sensors are selected based on the information which each sensor has gathered. The rest of the sensors will follow their leaders until they have sufficient information on the region of interest and then switch to the standard coverage algorithm. The proposed algorithm can be performed in a distributed manner. The results are validated through several numerical simulations.

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“…Voronoi Diagram [2] is shown to be the best cell partition for the one-tier homogeneous networks and Lloydlike Algorithms are applied to minimize the distortions. A similar node deployment problem over a non-convex environment, where the nodes can be placed everywhere other than some obstacles, are considered in [39], [40]. The sensing coverage problem in heterogeneous WSNs where sensors have different sensing ranges is discussed in [41]- [43].…”

Section: Related Work and Applicationsmentioning

confidence: 99%

A Source Coding Perspective on Node Deployment in Two-Tier Networks

Guo

Koyuncu

Jafarkhani

2018

IEEE Trans. Commun.

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Abstract-Multi-tier networks have many applications in different fields. We define a novel two-tier quantizer that can be applied to different node deployment problems including the energy conservation in two-tier wireless sensor networks (WSNs) consisting of N access points (APs) and M fusion centers (FCs). We aim at finding an optimal deployment of APs and FCs to minimize the average weighted total, or Lagrangian, of sensor and AP powers. For one fusion center, M = 1, we show that the optimal deployment of APs is simply a linear transformation of the optimal N -level quantizer for density f , and the sole FC should be located at the geometric centroid of the sensing field. We also provide the exact expression of the AP-Sensor power function and prove its convexity. For more than one fusion center, M > 1, we provide a necessary condition for the optimal deployment. Furthermore, to numerically optimize the AP and FC deployment, we propose three Lloyd-like algorithms and analyze their convergence. Simulation results show that our algorithms outperform the existing algorithms.

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Distributed deployment algorithms for robotic visual sensor networks in non-convex environment

Cited by 14 publications

References 11 publications

Optimal composition of heterogeneous multi-agent teams for coverage problems with performance bound guarantees

Optimal composition of heterogeneous multi-agent teams for coverage problems with performance bound guarantees

Exploiting the use of information to improve coverage performance of robotic sensor networks

A Source Coding Perspective on Node Deployment in Two-Tier Networks

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