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CPC Algorithm: Exact Area Coverage by a Mobile Robot Using Approximate Cellular Decomposition

Published online by Cambridge University Press:  07 October 2020

K. R. Guruprasad Open the ORCID record for K. R. Guruprasad [Opens in a new window]  and
T. D. Ranjitha
K. R. Guruprasad*
Affiliation:
Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, India
T. D. Ranjitha
Affiliation:
Telit Communications India Pvt Ltd, Bengaluru, India E-mail: ranjithatd.26@gmail.com
*
*Corresponding author. E-mail: krgprao@gmail.com
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Summary

A new coverage path planning (CPP) algorithm, namely cell permeability-based coverage (CPC) algorithm, is proposed in this paper. Unlike the most CPP algorithms using approximate cellular decomposition, the proposed algorithm achieves exact coverage with lower coverage overlap compared to that with the existing algorithms. Apart from a formal analysis of the algorithm, the performance of the proposed algorithm is compared with two representative approximate cellular decomposition-based coverage algorithms reported in the literature. Results of demonstrative experiments on a TurtleBot mobile robot within the robot operating system/Gazebo environment and on a Fire Bird V robot are also provided.

Keywords

Coverage path planningExact coverageSpanning treeCell permeabilityMinor nodesClustered minor nodesMobile robot
Type
Article
Information
Robotica , Volume 39 , Issue 7 , July 2021 , pp. 1141 - 1162
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

†

Part of this work was carried as part of Masters’ thesis of T. D. Ranjitha in the Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru 575025, India.

References

Acar, E. U., Choset, H., Zhang, Y. and Schervish, M., “Path planning for robotic demining: Robust sensor-based coverage of unstructured environments and probabilistic methods,” Int. J. Robot. Res. 22(7–8), 441–466 (2003).CrossRefGoogle Scholar
Dasgupta, P., Muñoz-Meléndez, A. and Guruprasad, K. R., “Multi-robot Terrain Coverage and Task Allocation for Autonomous Detection of Landmines,” Proceedings of SPIE 8359, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XI, 83590H (2012).CrossRefGoogle Scholar
Sheng, W., Xi, N., Song, M., Chen, Y. and MacNeille, P., “Automated CAD-Guided Robot Path Planning for Spray Painting of Compound Surfaces,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000), vol. 3 (2000) pp. 1918–1923.Google Scholar
Doty, K. L. and Harrison, R. R., “Sweep Strategies for a Sensory-Driven, Behavior-Based Vacuum Cleaning Agent,” Proceedings of AAAI 1993 Fall Symposium Series (1993) pp. 1–6.Google Scholar
Waanders, M., “Coverage Path Planning for Mobile Cleaning Robots,” Proceedings of 15th Twente Student Conference on IT, The Netherlands. Copyright (2011).Google Scholar
Weiss-Cohen, M., Sirotin, I. and Rave, E., “Lawn Mowing System for Known Areas,” Proceedings of Computational Intelligence for Modelling Control & Automation, 2008 International Conference on (IEEE, 2008) pp. 539–544.CrossRefGoogle Scholar
Arkin, E. M., Fekete, S. P. and Mitchell, J. S. B., “Approximation algorithms for lawn mowing and milling,” Comput. Geom. 17(1), 25–50 (2000).CrossRefGoogle Scholar
Macleod, C. N., Dobie, G., Pierce, S. G., Summan, R. and Morozov, M., “Machining-based coverage path planning for automated structural inspection,” IEEE Trans. Robot. 15(1), 202–213 (2018).Google Scholar
Choset, H., “Coverage for robotics–a survey of recent results,” Ann. Math. Artif. Intell. 31(1–4), 113–126 (2001).CrossRefGoogle Scholar
Galceran, E. and Carreras, M., “A survey on coverage path planning for robotics,” Robot. Auto. Syst. 61(12), 1258–1276 (2013).CrossRefGoogle Scholar
Gabriely, Y. and Rimon, E., “Spanning-tree based coverage of continuous areas by a mobile robot,” Ann. Math. Artif. Intell. 31(1–4), 77–98 (2001).CrossRefGoogle Scholar
Gabriely, Y. and Rimon, E., “Competitive on-line coverage of grid environments by a mobile robot,” Comput. Geom. 24(3), 197–224 (2003).CrossRefGoogle Scholar
Agmon, N., Hazon, N. and Kaminka, G. A., “Constructing Spanning Trees for Efficient Multi-robot Coverage,” Proceedings of IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, May 2006, pp. 1698–1703.Google Scholar
Zheng, X., Jain, S., Koenig, S. and Kempe, D., “Multi-robot Forest Coverage,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, August 2005, pp. 3852–3857.Google Scholar
Gonzalez, E., Alarcon, M., Aristizabal, P. and Parra, C., “BSA: A Coverage Algorithm,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2 (IEEE, 2003) pp. 1679–1684.Google Scholar
Gonzalez, E., Alvarez, O., Diaz, Y., Parra, C. and Bustacara, C., “BSA: A Complete Coverage Algorithm,” Proceedings of IEEE International Conference on Robotics and Automation (IEEE, 2005) pp. 2040–2044.Google Scholar
Jisha, V. R. and Ghose, D., “Frontier based goal seeking for robots in unknown environments,” J. Intell. Robot. Syst. 67, 229–254 (2012).CrossRefGoogle Scholar
Choset, H., “Coverage of known spaces: The boustrophedon cellular decomposition,” Auto. Robots 9(3), 247–253 (2000).CrossRefGoogle Scholar
Yang, S. X and Luo, C., “A neural network approach to complete coverage path planning,” IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(1), 718–725 (2004).Google ScholarPubMed
Xu, A., Viriyasuthee, C. and Rekleitis, I., “Efficient complete coverage of a known arbitrary environment with applications to aerial operations,” Auto. Robots 36, 365–381 (2014).CrossRefGoogle Scholar
Mannadiar, R. and Rekleitis, I., “Optimal Coverage of a Known Arbitrary Environment,” Proceedings of IEEE International Conference on Robotics and Automation (IEEE, 2010) pp. 5525–5530.CrossRefGoogle Scholar
Strimel, G. P. and Veloso, M. M., “Coverage Planning with Finite Resources,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, September 2014, pp. 2950–2956.CrossRefGoogle Scholar
Shnaps, I. and Rimon, E., “Online coverage of planar environments by a battery powered autonomous mobile robot,” IEEE Trans. Autom. Sci. Eng. 13(2), 425–436 (2016).CrossRefGoogle Scholar
Song, J. and Gupta, S., “ε*: An online coverage path planning algorithm,” IEEE Trans. Robot. 34(2), 526–533 (2018).CrossRefGoogle Scholar
Guruprasad, K. R. and Ranjitha, T. D., “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, July 2015.CrossRefGoogle Scholar