Generalized non-autonomous metric optimization for area coverage problems with mobile autonomous agents

Miah, Suruz; Panah, Arian Y.; Fallah, Mostafa M. H.; Spinello, Davide

doi:10.1016/j.automatica.2017.02.044

Cited by 34 publications

(9 citation statements)

References 21 publications

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“…Literature Review: The problem addressed herein lies under the category of deployment problems. Some examples include target tracking [1] and area coverage [2], [3], [4], [5]. The objective of the deployment problem is to distribute the agents over a region in which a density function which is either static [2] or time-varying [3], [4] describes the relative importance of each subset of the region of interest.…”

Section: Introductionmentioning

confidence: 99%

“…Some examples include target tracking [1] and area coverage [2], [3], [4], [5]. The objective of the deployment problem is to distribute the agents over a region in which a density function which is either static [2] or time-varying [3], [4] describes the relative importance of each subset of the region of interest. A key problem in the deployment of multi-agent networks is how the agents maneuver while avoiding collisions among themselves and with obstacles in a cluttered domain of interest.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Coverage Control of Multi-Agent Networks with Guaranteed Collision Avoidance in Cluttered Environments

Abdulghafoor¹,

Bakolas²

2021

Preprint

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We propose a distributed control algorithm for a multi-agent network whose agents deploy over a cluttered region in accordance with a time-varying coverage density function while avoiding collisions with all obstacles they encounter. Our algorithm is built on a two-level characterization of the network. The first level treats the multi-agent network as a whole based on the distribution of the locations of its agents over the spatial domain. In the second level, the network is described in terms of the individual positions of its agents. The aim of the multiagent network is to attain a spatial distribution that resembles that of a reference coverage density function (high-level problem) by means of local (microscopic) interactions of its agents (lowlevel problem). In addition, as the agents deploy, they must avoid collisions with all the obstacles in the region at all times. Our approach utilizes a modified version of Voronoi tessellations which are comprised of what we refer to as Obstacle-Aware Voronoi Cells (OAVC) in order to enable coverage control while ensuring obstacle avoidance. We consider two control problems. The first problem which we refer to as the high-level coverage control problem corresponds to an interpolation problem in the class of Gaussian mixtures (no collision avoidance requirement), which we solve analytically. The second problem which we refer to as the low-level coverage control problem corresponds to a distributed control problem (collision avoidance requirement is now enforced at all times) which is solved by utilizing Lloyd's algorithm together with the modified Voronoi tessellation (OAVC) and a time-varying coverage density function which corresponds to the solution of the high-level coverage control problem. Finally, simulation results for coverage in a cluttered environment are provided to demonstrate the efficacy of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Coverage Control of Multi-Agent Networks with Guaranteed Collision Avoidance in Cluttered Environments

Abdulghafoor¹,

Bakolas²

2021

Preprint

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“…Coverage control research also attempts to maximize area coverage using dynamic density functions. The focus of research in this area has been on motion coordination algorithms of mobile robots to optimize for a generalized time-varying density metric; often, with the use of Voronoi tessellations [35][36][37][38]. Although the density functions considered could represent target motion, as in our work, their solutions are not applicable as they typically assume mobile sensors, in contrast to our static sensors (i.e., sensors that cannot be relocated or reoriented once deployed).…”

Section: Introductionmentioning

confidence: 99%

Directional-Sensor Network Deployment Planning for Mobile-Target Search

et al. 2020

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In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

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“…Ivic et al [1] present an ergodicity‐based area coverage control algorithm achieving given coverage density by agents, and the problem is solved by designing stationary heat equation for the potential field. Miah et al [2] solve non‐autonomous area coverage metric optimisation problems with time‐varying risk densities by designing a decentralised multi‐agent control law in 2‐dimensional space, where each agent computes its Voronoi partition and uses sensory data to infer the targets' states. Mavrommati et al [3] develop a receding‐horizon ergodic control approach to solve real‐time motion plan problems for area coverage and target localisation.…”

Section: Introductionmentioning

confidence: 99%

Cooperative multi‐agent sweep coverage control for unknown areas of irregular shape

Shi

Qin

Liu

2018

IET Control Theory & Applications

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Generalized non-autonomous metric optimization for area coverage problems with mobile autonomous agents

Cited by 34 publications

References 21 publications

Distributed Coverage Control of Multi-Agent Networks with Guaranteed Collision Avoidance in Cluttered Environments

Distributed Coverage Control of Multi-Agent Networks with Guaranteed Collision Avoidance in Cluttered Environments

Directional-Sensor Network Deployment Planning for Mobile-Target Search

Cooperative multi‐agent sweep coverage control for unknown areas of irregular shape

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