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

Published online by Cambridge University Press:  12 July 2019

Vishnu G. Nair Open the ORCID record for Vishnu G. Nair [Opens in a new window]  and
K. R. Guruprasad
Vishnu G. Nair
Affiliation:
Department of Aeronautical and Automobile Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104India. E-mail: vishnu.nair@manipal.edu
K. R. Guruprasad*
Affiliation:
Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, 575025, India
*
*Corresponding author. E-mail: kgrprao@gmail.com
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Summary

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 partition-boundary-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 coverage overlaps. 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.

Keywords

Coverage path planningMulti-robotic systemUnmanned ground vehiclesVoronoi partitionGeodesic distanceManhattan distance
Type
Articles
Information
Robotica , Volume 38 , Issue 5 , May 2020 , pp. 845 - 860
Copyright
Copyright © Cambridge University Press 2019

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References

Weiss-Cohen, M., Sirotin, I. and Rave, E., “Lawn Mowing System for Known Areas,” 2008 International Conference on Computational Intelligence for Modelling Control Automation, Vienna, Austria (2008) pp. 539–544.CrossRefGoogle Scholar
Keith, L. D. and Harrison, R. R., “Sweep strategies for a sensory-driven, behavior-based vacuum cleaning agent,” AAAI 1993 Fall Symposium Series (1993) pp. 1–6.Google Scholar
Ma, J., Liu, H. and Huang, W., “Sensor-based complete coverage path planning in dynamic environment for cleaning robot,” CAAI Trans. Intell. Technol. 3(1), 65–72 (2018).Google Scholar
Dasgupta, P., Munoz-Melendez, A. and Guruprasad, K. R., “Multirobot Terrain Coverage and Task Allocation for Autonomous Detection of Landmines,” Proceedings of SPIE 8359, Sensors, and Command, Control, Communications, and Intelligence (C3I), Maryland, United States (2012).Google Scholar
Howie, C., “Coverage for robotics–a survey of recent results,” Ann. Math. Art. Intell. 31(1–4), 113–126 (2001).Google Scholar
Galceran, E. and Carreras, M., “A survey on coverage path planning for robotics,” Robot. Auto. Syst. 61(12), 1258–1276 (2013).CrossRefGoogle Scholar
Jager, M. and Nebel, B., “Dynamic Decentralized Area Partitioning for Cooperating Cleaning Robots,” Proceedings of IEEE International Conference on Robotics and Automation, Washington, DC, vol. 4 (2002) pp. 3577–3582.Google Scholar
Rankin, E. S., Rekleitis, I., New, A. P. and Choset, H., “Efficient boustrophedon multi-robot coverage: an algorithmic approach,” Ann. Math. Art. Intell. 52, 109–142 (2008).Google Scholar
Hazon, N. and Kaminka, G. A., “On redundancy, efficiency, and robustness in coverage for multiple robots,” Robot. Auto. Syst. 56, 1102–1114 (2008).CrossRefGoogle Scholar
Agmon, N., Hazon, N. and Kaminka, G. A., “The giving tree: Constructing trees for efficient offline and online multi-robot coverage,” Ann. Math. Art. Intell. 52(2–4), 43–168 (2009).Google Scholar
Zheng, X., Koenig, S., Kempe, D. and Jain, S., “Multirobot forest coverage for weighted and unweighted terrain,” IEEE Trans. Robot . 26(6), 1018–1031 (2010).CrossRefGoogle Scholar
Wilson, Z., Whipple, T. and Dasgupta, P., “Multi-robot Coverage with Dynamic Coverage Information Compression,” Proceedings of 8th International Conference on Informatics in Control, Automation and Robotics, Noordwijkerhout, The Netherlands (2011) pp. 236–241.Google Scholar
Michel, D. and McIsaac, K., “New Path Planning Scheme for Complete Coverage of Mapped Areas by Single and Multiple Robots,” In:Proceedings of International Conference on Mechatronics and Automation (ICMA), Chengdu, China (IEEE, 2012) pp. 1233–1240.Google Scholar
Macharet, D., Azpurua, H., Freitas, G. and Campos, M., “Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys,” Robotica 36(8), 1144–1166 (2018).Google Scholar
Min, T. W. and Yin, H. K., “A Decentralized Approach for Cooperative Sweeping by Multiple Mobile Robots,” Proceedings of International Conference on Intelligent Robots and Systems, Victoria, BC, Canada (1998) pp. 380–385.Google Scholar
Hert, S. and Lumelsky, V., “Polygon area decomposition for multiplerobot workspace division,” Int. J. Comput. Geo. Appl. 8, 437–466 (1998).CrossRefGoogle Scholar
Guruprasad, K. R., Wilson, Z. and Dasgupta, P., “Complete Coverage of an Initially Unknown Environment by Multiple Robots Using Voronoi Partition,” Proceedings of Advances in Control and Optimization of Dynamical Systems (ACODS), Bangalore, India (2012).Google Scholar
Bash, B. A. and Desnoyers, P. J., “Exact Distributed Voronoi Cell Computation in Sensor Networks,” Proceedings of the Sixth IEEE/ACM Conference On Information Processing in Sensor Networks, Cambridge, MA (2007) pp. 236–243.Google Scholar
Guruprasad, K. R. and Dasgupta, P., “Distributed Voronoi Partitioning forMulti-robot Systems with Limited Range Sensors,” Proceedings of IEEE/RSJ International Conference on Robotics and Intelligent Systems, Vilamoura, Portugal (2012) pp. 3546–3552.Google Scholar
Choset, H., “Coverage of known spaces: The boustrophedon cellular decomposition,” Auto. Robot. 9(3), 247–253 (2000).CrossRefGoogle Scholar
Butler, Z. J., Rizzi, A. A. and Hollis, R. L., “Contact Sensor-Based Coverage of Rectilinear Environments,” Proceedings of the IEEE International Symposium on Intelligent Control Systems and Semiotics, Cambridge, MA (1999) pp. 266–271.Google Scholar
Latombe, J. C., Robot Motion Planning (Kluwer Academic Publishers, Dordrecht, The Netherlands, 1991).CrossRefGoogle Scholar
Gabriely, Y. and Rimon, E., “Spanning-tree based coverage of continuous areas by a mobile robot,” Ann. Math. Art. Intell. 31(1–4), 77–98 (2001).CrossRefGoogle Scholar
Gabriely, Y. and Rimon, E., “Competitive on-line coverage of grid environments by amobile robot,” Comput. Geo. 24(3), 197–224 (2003).CrossRefGoogle Scholar
Gonzalez, E., Alvarez, O., Diaz, Y., Parra, C. and Bustacara, C., “BSA: A Complete Coverage Algorithm,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005. ICRA 2005, Barcelona, Spain (IEEE, 2005) pp. 2040–2044.Google Scholar
Ranjitha, T. D. and Guruprasad, K. R., “Pseudo Spanning Tree-Based Complete and Competitive Robot Coverage Using Virtual Nodes,” IFACPapersOnLine: Proceedings of 4th IFAC Conference on Advances in Control and Optimization of Dynamical Systems (ACODS), Tiruchirappalli, India (2016).Google Scholar
Ranjitha, T. D. and Guruprasad, K. R., “ST-CTC: A Spanning Tree-Based Competitive and Truly Complete Coverage Algorithm for Mobile Robots,” Proceedings of Advances in Robotics, 2nd International Conference of Robotics Society of India, Goa, India (2015).Google Scholar