Hostname: page-component-7b9c58cd5d-hxdxx Total loading time: 0 Render date: 2025-03-13T14:27:47.008Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys

Published online by Cambridge University Press:  15 April 2018

Héctor Azpúrua ,
Gustavo M. Freitas ,
Douglas G. Macharet  and
Mario F. M. Campos
Héctor Azpúrua*
Affiliation:
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil. E-mails: [email protected], [email protected] Instituto Tecnológico Vale, Ouro Preto, MG 35400-000, Brazil. E-mail: [email protected]
Gustavo M. Freitas
Affiliation:
Instituto Tecnológico Vale, Ouro Preto, MG 35400-000, Brazil. E-mail: [email protected]
Douglas G. Macharet
Affiliation:
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil. E-mails: [email protected], [email protected]
Mario F. M. Campos
Affiliation:
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting.

Keywords

Multi-robot systemsArea coveragePath planningGeophysical surveys
Type
Articles
Information
Robotica , Volume 36 , Issue 8 , August 2018 , pp. 1144 - 1166
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Thrun, S. et al., “Robotic Mapping: A Survey,” In: Exploring Artificial Intelligence in the New Millennium (Lakemeyer, G. and Nebel, B., eds.) (Morgan Kaufmann, San Mateo, CA, 2002) pp. 1–35.Google Scholar
2. Gage, D. W., “Command control for many-robot systems,” Unmanned Syst. Mag. 10 (4), 28–34 (1992).Google Scholar
3. Macharet, D. G., Perez-Imaz, H. I. A., Rezeck, P. A. F., Potje, G. A., Benyosef, L. C. C., Wiermann, A., Freitas, G. M., Garcia, L. G. U. and Campos, M. F. M., “Autonomous aeromagnetic surveys using a fluxgate magnetometer,” Sensors 16 (12), 2075 (2016).CrossRefGoogle ScholarPubMed
4. Applegate, D. L., Bixby, R. E., Chvatal, V. and Cook, W. J., The Traveling Salesman Problem: A Computational Study, Princeton Series in Applied Mathematics (Princeton University Press, Princeton, NJ, USA, 2007).Google Scholar
5. Arkin, E. M. and Hassin, R., “Approximation algorithms for the geometric covering salesman problem,” Discrete Appl. Math. 55 (3), 197–218 (1994).CrossRefGoogle Scholar
6. Englot, B. and Hover, F. S., “Sampling-Based Coverage Path Planning for Inspection of Complex Structures,” In: 22nd International Conference Automated Planning and Scheduling, Sao Paulo, Brazil (Jun. 25–29, 2012) pp. 29–37, Palo Alto, CA: AAAI Press.CrossRefGoogle Scholar
7. Chin, W.-P. and Ntafos, S., “Shortest watchman routes in simple polygons,” Discrete & Computational Geom. 6 (1), 9–31 (1991).CrossRefGoogle Scholar
8. Guo, Y. and Balakrishnan, M., “Complete Coverage Control for Nonholonomic Mobile Robots in Dynamic Environments,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation ICRA (2006) pp. 1704–1709.Google Scholar
9. Cao, Z. L., Huang, Y. and Hall, E. L., “Region filling operations with random obstacle avoidance for mobile robots,” J. Robot. Syst. 5 (2), 87–102 (1988).CrossRefGoogle Scholar
10. Choset, H., “Coverage for robotics–A survey of recent results,” Ann. Math. Artif. Intell. 31 (1–4), 113–126 (2001).CrossRefGoogle Scholar
11. Galceran, E. and Carreras, M., “A survey on coverage path planning for robotics,” Robot. and Auton. Syst. 61 (12), 1258–1276 (2013).CrossRefGoogle Scholar
12. Lumelsky, V. J., Mukhopadhyay, S. and Kang, S., “Dynamic path planning in sensor-based terrain acquisition,” IEEE Trans. Robot. Autom. 6 (4), 462–472 (1990).CrossRefGoogle Scholar
13. Acar, E. U., Choset, H., Rizzi, A. A., Atkar, P. N. and Hull, D., “Morse decompositions for coverage tasks,” Int. J. Robot. Res. 21 (4), 331–344 (2002).CrossRefGoogle Scholar
14. Peless, E., Abramson, S., Friedman, R. and Peleg, I., “Area coverage with an autonomous robot,” (2003). US Patent 6,615,108.Google Scholar
15. Huang, W. H., “Optimal Line-Sweep-Based Decompositions for Coverage Algorithms,” Proceedings of the 2001 IEEE International Conference on Robotics and Automation ICRA, vol. 1 (2001) pp. 27–32.Google Scholar
16. Jimenez, P. A., Shirinzadeh, B., Nicholson, A. and Alici, G., “Optimal Area Covering Using Genetic Algorithms,” Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2007) pp. 1–5.Google Scholar
17. Jan, G. E., Luo, C., Hung, L.-P. and Shih, S.-T., “A Computationally Efficient Complete Area Coverage Algorithm for Intelligent Mobile Robot Navigation,” Proceedings of the International Joint Conference on Neural Networks IJCNN (2014) pp. 961–966.Google Scholar
18. Xu, A., Viriyasuthee, C. and Rekleitis, I., “Optimal Complete Terrain Coverage Using An Unmanned Aerial Vehicle,” Proceedings of the IEEE International Conference on Robotics and Automation ICRA (2011) pp. 2513–2519.Google Scholar
19. Choset, H. and Pignon, P., “Coverage Path Planning: The Boustrophedon Cellular Decomposition,” In: International Conference on Field and Service Robotics, Canberra, Australia (Springer, 1998) pp. 203–209.CrossRefGoogle Scholar
20. Lin, L. and Goodrich, M. A., “Hierarchical heuristic search using a Gaussian mixture model for UAV coverage planning,” IEEE Trans. Cybern. 44 (12), 2532–2544 (2014).CrossRefGoogle ScholarPubMed
21. Meuth, R. J., Vian, J. L., Saad, E. W. and Wunsch, D. C., “Adaptive multi-vehicle area coverage optimization system and method,” (2012). US Patent 8,260,485.Google Scholar
22. Carneiro, A. and Amorim, M., “Coverage strategy for periodic readings in robotic-assisted monitoring systems,” Ad Hoc Netw. 11 (7), 1907–1918 (2013).CrossRefGoogle Scholar
23. Sagan, H., Space-Filling Curves, vol. 18 (Springer-Verlag, New York, NY, 1994).CrossRefGoogle Scholar
24. Rekleitis, I., New, A. P., Rankin, E. S. and Choset, H., “Efficient Boustrophedon multi-robot coverage: an algorithmic approach,” Ann. Math. Artif. Intell. 52 (2–4), 109–142 (2008).CrossRefGoogle Scholar
25. 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
26. Hazon, N., Mieli, F. and Kaminka, G. A., “Towards Robust On-Line Multi-Robot Coverage,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation ICRA (2006) pp. 1710–1715.Google Scholar
27. Agmon, N., Hazon, N. and Kaminka, G. A., “Constructing Spanning Trees for Efficient Multi-Robot Coverage,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation ICRA (2006) pp. 1698–1703.Google Scholar
28. Breitenmoser, A., Schwager, M., Metzger, J.-C., Siegwart, R. and Rus, D., “Voronoi Coverage of Non-Convex Environments with a Group of Networked Robots,” Proceedings of the IEEE International Conference on Robotics and Automation ICRA (2010) pp. 4982–4989.Google Scholar
29. Luo, C., Yang, S. X. and Stacey, D. A., “Real-Time Path Planning with Deadlock Avoidance of Multiple Cleaning Robots,” Proceedings of the IEEE International Conference on Robotics and Automation ICRA, vol. 3 (2003) pp. 4080–4085.Google Scholar
30. Wagner, I. A., Altshuler, Y., Yanovski, V. and Bruckstein, A. M., “Cooperative cleaners: A study in ant robotics,” Int. J. Robot. Res. 27 (1), 127–151 (2008).CrossRefGoogle Scholar
31. Sujit, P., Saripalli, S. and Sousa, J. B., “Unmanned aerial vehicle path following: A survey and analysis of algorithms for fixed-wing unmanned aerial vehicless,” IEEE Control Syst. 34 (1), 42–59 (2014).Google Scholar
32. Maza, I. and Ollero, A., “Multiple UAV Cooperative Searching Operation Using Polygon Area Decomposition and Efficient Coverage Algorithms,” In: Distributed Autonomous Robotic Systems, vol. 6 (Springer, 2007) pp. 221–230 (Chapter).Google Scholar
33. Santamaria, E., Segor, F. and Tchouchenkov, I., “Rapid Aerial Mapping with Multiple Heterogeneous Unmanned Vehicles,” In: Proceedings of the 10th International ISCRAM Conference, Kristiansand, Norway (May 24–27, 2015).Google Scholar
34. Jiao, Y.-S., Wang, X.-M., Chen, H. and Li, Y., “Research on the Coverage Path Planning of UAVs for Polygon Areas,” Proceedings of the 5th IEEE Conference on Industrial Electronics and Applications ICIEA (2010) pp. 1467–1472.Google Scholar
35. Avellar, G. S. C., Pereira, G. A. S., Pimenta, L. C. A. and Iscold, P., “Multi-UAV routing for area coverage and remote sensing with minimum time,” Sensors 15 (11), 27783 (2015).CrossRefGoogle ScholarPubMed
36. Toth, P. and Vigo, D., eds., The Vehicle Routing Problem (Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 2001).Google Scholar
37. Di Franco, C. and Buttazzo, G., “Coverage path planning for UAVs photogrammetry with energy and resolution constraints,” J. Intell. Robot. Syst. 83 (3–4), 445–462 (2016).CrossRefGoogle Scholar
38. Mannadiar, R. and Rekleitis, I., “Optimal Coverage of a Known Arbitrary Environment,” Proceedings of the IEEE International Conference on Robotics and Automation ICRA (2010) pp. 5525–5530.Google Scholar
39. Easton, K. and Burdick, J., “A Coverage Algorithm for Multi-Robot Boundary Inspection,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation ICRA (2005) pp. 727–734.Google Scholar
40. de Avellar, G. S. C., Navegação de Veículos Aéreos Não Tripulados Para Cobertura de Áreas com Minimização de Tempo Master's Thesis, Universidade Federal de Minas Gerais (UFMG), CDU: 621.3(043) (2014).Google Scholar
41. Zheng, X. and Koenig, S., “Robot Coverage of Terrain with Non-Uniform Traversability,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems IROS (2007) pp. 3757–3764.Google Scholar
42. Reeves, C., “Aeromagnetic surveys: Principles, practice and interpretation,” Course Unit i 50, 33–99 (2005).Google Scholar
43. Kershner, R., “The number of circles covering a set,” Am. J. Math. 61 (3), 665–671 (1939).CrossRefGoogle Scholar
44. Snyder, M. E., Foundations of Coverage Algorithms in Autonomic Mobile Sensor Networks Ph.D. Thesis (Missouri University of Science and Technology, 2014).Google Scholar
45. Clarke, K. C., “Criteria and Measures for the Comparison of Global Geocoding Systems,” In: Discrete Global Grids: A Web Book. (University of California, Santa Barbara, 2002). [http://www.ncgia.ucsb.edu/globalgrids-book].Google Scholar
46. Xiaochong, T., Jin, B., Song, J. and Yongsheng, Z., “The Subdivision of Partial Geo-Grid Based on Global Geo-Grid Frame,” Proceedings of the ISPRS Workshop on Updating Geo-spatial Databases with Imagery & The 5th ISPRS Workshop on DMGISs, Urumchi: Xinjiang, China (2007) pp. 229–235.Google Scholar
47. Lloyd, S., “Least squares quantization in PCM,” IEEE Trans. Inform. Theory 28 (2), 129–137 (1982).CrossRefGoogle Scholar
48. Perez-imaz, H. I. A., Rezeck, P. A. F., Macharet, D. G. and Campos, M. F. M., “Multi-Robot 3d Coverage Path Planning for First Responders Teams,” Proceedings of the IEEE International Conference on Automation Science and Engineering CASE (2016) pp. 1374–1379.Google Scholar
49. Hert, S. and Lumelsky, V., “Polygon area decomposition for multiple-robot workspace division,” Int. J. Comput. Geom. Appl. 8 (4), 437–466 (1998).CrossRefGoogle Scholar
50. Bhattacharyya, A., “On a measure of divergence between two statistical population defined by their population distributions,” Bull. Calcutta Math. Soc. 35, 99–109 (1943).Google Scholar
51. Kailath, T., “The divergence and Bhattacharyya distance measures in signal selection,” IEEE Trans. Commun. Technol. 15 (1), 52–60 (1967).CrossRefGoogle Scholar