GM-VPC: An Algorithm for Multi-robotCoverage of Known Spaces Using Generalized Voronoi Partition

Nair, Vishnu G and Guruprasad, K (2019) GM-VPC: An Algorithm for Multi-robotCoverage of Known Spaces Using Generalized Voronoi Partition. Robotica.

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In this paper we address the problem of coverage path planning (CPP) for multiple cooperating mobile robots. We use a ‘partition and cover’ approach using Voronoi partition to achieve natural passive cooperation between robots to avoid task duplicity. We combine two generalizations of Voronoi partition, namely geodesic-distance-based Voronoi partition and Manhattan-distance-based Voronoi partition, to address contiguity of partition in the presence of obstacles and to avoid partitionboundary-induced coverage gap. The region is divided into 2D × 2D grids, where D is the size of the robot footprint. Individual robots can use any of the single-robot CPP algorithms. We show that with the proposed Geodesic-Manhattan Voronoi-partition-based coverage (GM-VPC), a complete and non-overlapping coverage can be achieved at grid level provided that the underlying single-robot CPP algorithm has similar property. We demonstrated using two representative single-robot coverage strategies, namely Boustrophedon-decomposition-based coverage and Spanning Tree coverage, first based on so-called exact cellular decomposition and second based on approximate cellular decomposition, that the proposed partitioning scheme completely eliminates coverage gaps and coverageoverlaps. Simulation experiments using Matlab and V-rep robot simulator and experiments with Fire Bird V mobile robot are carried out to validate the proposed coverage strategy.

Item Type: Article
Uncontrolled Keywords: Coverage path planning; Multi-robotic system; Unmanned ground vehicles; Voronoi partition; Geodesic distance; Manhattan distance.
Subjects: Engineering > MIT Manipal > Aeronautical and Automobile
Engineering > MIT Manipal > Mechanical and Manufacturing
Depositing User: MIT Library
Date Deposited: 23 Jun 2020 05:32
Last Modified: 23 Jun 2020 05:32

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