Multirobot Forest Coverage for Weighted and Unweighted Terrain

Zheng, Xiaoming; Koenig, Sven; Kempe, David; Jain, Sonal

doi:10.1109/tro.2010.2072271

Cited by 71 publications

(42 citation statements)

References 28 publications

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“…frontier based exploration, while the followers are potentialfield driven. Similarly, in Zheng et al (2010), the authors have proposed Space-Based Potential Field technique, which focus on dispersion of robots in the environment, while avoiding overlap performance and the local minima. Recently decentralized variation of this technique (Liu and Lyons 2015) is proposed, which includes monotonic factor for coverage to avoid local minima problem.…”

Section: Related Workmentioning

confidence: 99%

A Cognitive Agent-based Model for Multi-Robot Coverage at a City Scale

Zia

Din

Shahzad

et al. 2017

Complex Adapt Syst Model

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Background In this article, we model a behavior-based strategy of autonomous coverage and exploration at the scale of a city with multiple robots. The behavioral components are motivated by Cepeda et al. (Sensors 12 (9): 12772–12797, 2012) and extended to incorporate into a generic cellular-automata based agent model. These agents are representing homogenous robots with reactive control. Deliberative approaches requires large scale map and large memory, which slowdowns the execution. Our approach is reactive and simple, that is, robots have no prior information about the environment and do not generate a route map as they traverse. However, other robots in neighborhood are detected using local sensors. Findings A city-scale map-driven simulation is designed and model’s efficiency is evaluated for different deployment possibilities. It is evidenced that even with this simple model, the agents are able to explore a significant percentage of the environment. Conclusion For a city-scale multi-robotic exploration, our simple but efficient model does not require explicit communication and data sharing (and hence representation and storage of navigated map) because possibility of encountering and influencing another agent is quite low, due to spatial dynamics of the environment.

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Section: Related Workmentioning

confidence: 99%

A Cognitive Agent-based Model for Multi-Robot Coverage at a City Scale

Zia

Din

Shahzad

et al. 2017

Complex Adapt Syst Model

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“…However, a challenging problem when using a Multi-Robotic System (MRS) is to achieve distributed coordination between different robots so that they do not collide with each other or perform repeated coverage of regions within the environment. Several multi-robot coverage (MRC) strategies have been proposed in the literature [4], [5], [6], [7], [8], [9], [10], [11]. In most of these algorithms, fundamentally two strategies are followed to ensure cooperation and avoid coverage overlap.…”

Section: Introductionmentioning

confidence: 99%

Centroidal Voronoi Partitioning using Virtual Nodes for MultiRobot Coverage

Nair¹,

Guruprasad²

2018

IJET

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This paper addresses the problem of Voronoi partitioning using Centroidal Voronoi configuration for a multi-robotic coverage strategy known as Voronoi Partition based Coverage (VPC) algorithm. In VPC, the area to be covered is divided into Voronoi cells and each robot covers the corresponding cell. We use the concept of Centroidal Voronoi Configuration (CVC) to achieve a more uniform load distribution among the robots in terms of the area covered. Instead of the robots moving physically into the CVC, we introduce a concept of virtual nodes, which are deployed into CVC. Once the Voronoi partition is created based on the virtual nods, the robots cover the corresponding Voronoi cells. A gradient based control law has been used for deployment of the virtual nodes. Simulation results are provided to demonstrate the proposed deployment and partitioning scheme.

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“…STC optimally solves the single-robot coverage problem. STC algorithms have also developed into MSTC (multirobot spanning tree coverage) ( [4], [5]) and MFC (multirobot forest coverage) algorithms [6]. Existing STC algorithms have focused on two issues: full (a.k.a complete) and non-repetitive coverage.…”

Section: Introductionmentioning

confidence: 99%