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Author: search.filters.author.Nair, V.G.Subject: search.filters.subject.Coverage algorithms

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    GeoDesic-VPC: Spatial partitioning for multi-robot coverage problem
    (Acta Press journals@actapress.com, 2020) Nair, V.G.; Guruprasad, K.R.
    In this paper, we address a problem of area coverage using multiple cooperating robots using a “partition and cover" approach, where the area of interest is decomposed into as many cells as the robots, and each robot is assigned the task of covering a cell. While the most partitioning approaches used in the literature in the context of a robotic coverage problem may result in topologically disconnected cells in the presence of obstacles leading to incomplete coverage, we propose to use geodesic distance-based generalization of the Voronoi partition, ensuring that each cell that is allotted for a robot for coverage is a topologically connected region, and hence, achieving a complete coverage. The proposed multi-robot coverage strategy is demonstrated with simulation in MATLAB and V-rep simulator, using two single-robot coverage algorithms reported in the literature, namely boustrophedon decomposition-based coverage and spanning tree-based coverage algorithms. © 2020 SAE International. All rights reserved.
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    2D-VPC: An Efficient Coverage Algorithm for Multiple Autonomous Vehicles
    (Institute of Control, Robotics and Systems, 2021) Nair, V.G.; Guruprasad, K.R.
    In this paper, we address a problem of multi-robotic coverage, where an area of interest is covered by multiple sensors, each mounted on an autonomous vehicle such as an aerial or a ground mobile robot. The area of interest is first decomposed into grids of equal size and then partitioned into Voronoi cells. Each robot/sensor is assigned the task of covering the corresponding Voronoi cell. We propose an optimal gridding size and partitioning methodology that eliminate the coverage inefficiencies induced by the partitioning process. We carried out experiments using multiple quadcopters and mobile robots to demonstrate and validate the proposed multi-sensor coverage strategy. © 2021, ICROS, KIEE and Springer.