Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-22T05:25:01.573Z Has data issue: false hasContentIssue false
  • English
  • Français

A distributed control algorithm for area search by a multi-robot team

Published online by Cambridge University Press:  29 April 2016

Ahmad Baranzadeh  and
Andrey V. Savkin
Ahmad Baranzadeh*
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia. E-mail: [email protected]
Andrey V. Savkin
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, we present a novel algorithm for exploring an unknown environment using a team of mobile robots. The suggested algorithm is a grid-based search method that utilizes a triangular pattern which covers an area so that exploring the whole area is guaranteed. The proposed algorithm consists of two stages. In the first stage, all the members of the team make a common triangular grid of which they are located on the vertices. In the second stage, they start exploring the area by moving between vertices of the grid. Furthermore, it is assumed that the communication range of the robots is limited, and the algorithm is based on the information of the nearest neighbours of the robots. Moreover, we apply a new mapping method employed by robots during the search operation. A mathematically rigorous proof of convergence with probability 1 of the algorithm is given. Moreover, our algorithm is implemented and simulated using a simulator of the real robots and environment and also tested via experiments with Adept Pioneer 3DX wheeled mobile robots.

Keywords

Mobile robotsMulti-agent networksMulti-robot searchDistributed controlSearch and rescue
Type
Articles
Information
Robotica , Volume 35 , Issue 6 , June 2017 , pp. 1452 - 1472
Copyright
Copyright © Cambridge University Press 2016 

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. Viet, H. H., Dang, V.-H., Laskar, M. N. U. and Chung, T., “Ba*: An online complete coverage algorithm for cleaning robots,” Appl. Intell. 39 (2), 217–235 (2013).Google Scholar
2. Hess, J., Beinhofer, M. and Burgard, W., “A Probabilistic Approach to High-Confidence Cleaning Guarantees for Low-Cost Cleaning Robots,” IEEE International Conference on Robotics and Automation (ICRA), IEEE, Hong Kong (2014) pp. 5600–5605.Google Scholar
3. Marjovi, A. and Marques, L., “Multi-robot olfactory search in structured environments,” Robot. Auton. Syst. 59 (11), 867–881 (2011).CrossRefGoogle Scholar
4. Guarnieri, M., Kurazume, R., Masuda, H., Inoh, T., Takita, K., Debenest, P., Hodoshima, R., Fukushima, E. and Hirose, S., “Helios System: A Team of Tracked Robots for Special Urban Search and Rescue Operations,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, IEEE, St. Louis, MO, USA (2009) pp. 2795–2800.Google Scholar
5. Kruijff, G.-J. M., Janíček, M., Keshavdas, S., Larochelle, B., Zender, H., Smets, N. J., Mioch, T., Neerincx, M. A., Diggelen, J. V., Colas, F. et al., “Experience in System Design for Human-Robot Teaming in Urban Search and Rescue,” In: Field and Service Robotics (Yoshida, K. and Tadokoro, S., eds.) (Springer, 2014) pp. 111–125.Google Scholar
6. Portugal, D. and Rocha, R. P., “Multi-robot patrolling algorithms: Examining performance and scalability,” Adv. Robot. 27 (5), 325–336 (2013).Google Scholar
7. Reiser, U., Jacobs, T., Arbeiter, G., Parlitz, C. and Dautenhahn, K., “Care-o-bot® 3–Vision of a Robot Butler,” In: Your Virtual Butler (Trappl, R., ed.) (Springer, 2013) pp. 97–116, 2013.Google Scholar
8. Marjovi, A., Nunes, J. G., Marques, L. and de Almeida, A., “Multi-Robot Fire Searching in Unknown Environment,” In: Field and Service Robotics (Howard, A., Iagnemma, K., and Kelly, A., eds.) (Springer, 2010) pp. 341–351.CrossRefGoogle Scholar
9. Zadorozhny, V. and Lewis, M., “Information Fusion Based on Collective Intelligence for Multi-Robot search and Rescue Missions,” IEEE 14th International Conference on Mobile Data Management, IEEE, Los Alamitos, CA, USA (2013) pp. 275–278.Google Scholar
10. Pereira, T., Moreira, A. P. and Veloso, M., “Coordination for Multi-Robot Exploration using Topological Maps,” In: Proceedings of the 11th Portuguese Conference on Automatic Control, CONTROLO2014 (Moreira, P. A., Matos, A., and Veiga, G., eds.) (Springer, Cham, Switzerland, 2015) pp. 515–524.Google Scholar
11. Carvalho, F. F., Cavalcante, R. C., Vieira, M., Chaimowicz, L. and Campos, M. F., “A Multi-Robot Exploration Approach Based on Distributed Graph Coloring,” Latin American Robotics Symposium and Competition (LARS/LARC), IEEE, Arequipa, Peru (2013) pp. 142–147.CrossRefGoogle Scholar
12. Sharma, S., Sur, C., Shukla, A. and Tiwari, R., “Multi-Robot Area Exploration using Particle Swarm Optimization with the Help of cbdf-Based Robot Scattering,” In: Computational Vision and Robotics (Sethi, I. K., ed.) (Springer, 2015) pp. 113–123.CrossRefGoogle Scholar
13. Savkin, A. V., Cheng, T. M., Li, Z., Javed, F., Xi, Z., Matveev, A. S. and Nguyen, H., Decentralized Coverage Control Problems For Mobile Robotic Sensor and Actuator Networks (Wiley-IEEE Press, New Jersey, USA, 2015).CrossRefGoogle Scholar
14. Sarker, M. O. F., Dahl, T. S., Arcaute, E. and Christensen, K., “Local interactions over global broadcasts for improved task allocation in self-organized multi-robot systems,” Robot. Auton. Syst. 62 (10), 1453–1462 (2014).Google Scholar
15. Cheng, T. M. and Savkin, A. V., “Decentralized control for mobile robotic sensor network self-deployment: Barrier and sweep coverage problems,” Robotica 29 (2), 283–294 (2011).Google Scholar
16. Chand, P. and Carnegie, D. A., “A two-tiered global path planning strategy for limited memory mobile robots,” Robot. Auton. Syst. 60 (2), 309–321 (2012).Google Scholar
17. Chand, P. and Carnegie, D. A., “Mapping and exploration in a hierarchical heterogeneous multi-robot system using limited capability robots,” Robot. Auton. Syst. 61 (6), 565–579 (2013).CrossRefGoogle Scholar
18. Savkin, A. V. and Teimoori, H., “Decentralized navigation of groups of wheeled mobile robots with limited communication,” IEEE Trans Robot. 26 (6), 1099–1104 (2010).Google Scholar
19. Parker, L. E., “Distributed algorithms for multi-robot observation of multiple moving targets,” Auton. Robots 12 (3), 231–255 (2002).CrossRefGoogle Scholar
20. Cunningham, A., Wurm, K. M., Burgard, W. and Dellaert, F., “Fully Distributed Scalable Smoothing and Mapping with Robust Multi-Robot Data Association,” In: IEEE International Conference on Robotics and Automation (ICRA), IEEE, Saint Paul, MN, USA (2012) pp. 1093–1100.Google Scholar
21. Erinc, G. and Carpin, S., “Anytime merging of appearance-based maps,” Auton. Robots 36 (3), 241–256 (2014).Google Scholar
22. Vu, T.-D., Burlet, J. and Aycard, O., “Grid-based localization and local mapping with moving object detection and tracking,” Inform. Fusion 12 (1), 58–69 (2011).Google Scholar
23. Marinakis, D. and Dudek, G., “Pure topological mapping in mobile robotics,” IEEE Trans. Robot. 26 (6), 1051–1064 (2010).CrossRefGoogle Scholar
24. Agmon, N., Kraus, S. and Kaminka, G. A., “Multi-Robot Perimeter Patrol in Adversarial Settings,” IEEE International Conference on Robotics and Automation, ICRA, IEEE, Pasadena, CA, USA (2008) pp. 2339–2345.Google Scholar
25. Chaimowicz, L., Cowley, A., Gomez-Ibanez, D., Grocholsky, B., Hsieh, M., Hsu, H., Keller, J., Kumar, V., Swaminathan, R. and Taylor, C., “Deploying Air-Ground Multi-Robot Teams in Urban Environments,” In: Multi-Robot Systems. From Swarms to Intelligent Automata Volume III (Parker, L. E., Schneider, F. E., and Schult, A. C., eds.) (Springer, 2005) pp. 223–234.Google Scholar
26. Wurm, K. M., Stachniss, C. and Burgard, W., “Coordinated Multi-Robot Exploration using a Segmentation of the Environment,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, IEEE, Nice, France (2008) pp. 1160–1165.Google Scholar
27. Baxter, J. L., Burke, E., Garibaldi, J. M. and Norman, M., “Multi-Robot Search and Rescue: A Potential Field Based Approach,” In: Autonomous Robots and Agents (Mukhopadhyay, S. C. and Gupta, G. S., eds.) (Springer, 2007) pp. 9–16.Google Scholar
28. Zlot, R., Stentz, A., Dias, M. and Thayer, S., “Multi-Robot Exploration Controlled by a Market Economy,” IEEE International Conference on Robotics and Automation, ICRA '02, IEEE, Washington, DC, USA, vol. 3 (2002) pp. 3016–3023.Google Scholar
29. Guruprasad, K. and Ghose, D., “Performance of a class of multi-robot deploy and search strategies based on centroidal voronoi configurations,” Int. J. Syst. Sci. 44 (4), 680–699 (2013).Google Scholar
30. Fink, J., Hsieh, M. A. and Kumar, V., “Multi-Robot Manipulation Via Caging in Environments with Obstacles,” IEEE International Conference on Robotics and Automation, ICRA, IEEE, Pasadena, CA, USA (2008) pp. 1471–1476.Google Scholar
31. Baranzadeh, A., “A Decentralized Control Algorithm for Target Search by a Multi-Robot Team,” Proceedings of Australasian Conference on Robotics and Automation, ARAA, Sydney, Australia (2013).Google Scholar
32. Savkin, A. V., Javed, F. and Matveev, A. S., “Optimal distributed blanket coverage self-deployment of mobile wireless sensor networks,” IEEE Commun. Lett. 16 (6), 949–951 (2012).Google Scholar
33. Kershner, R., “The number of circles covering a set,” Am. J. Math. 61 (3), 665–671 (1939).Google Scholar
34. Tadokoro, S., Rescue Robotics: DDT Project on Robots and Systems for Urban Search and Rescue (Springer Science & Business Media, London, UK, 2009).Google Scholar
35. Murphy, R. R., Kravitz, J., Stover, S. and Shoureshi, R., “Mobile robots in mine rescue and recovery,” IEEE Robot. Autom. Mag. 16 (2), 91–103 (2009).Google Scholar
36. Savkin, A. V. and Javed, F., “A Method for Decentralized Self-Deployment of a Mobile Sensor Network with Given Regular Geometric Patterns,” in Proceedings of the 7th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE, Adelaide, Australia (2011) pp. 371–376.Google Scholar
37. Jadbabaie, A., Lin, J. and Morse, A. S., “Coordination of groups of mobile autonomous agents using nearest neighbor rules,” IEEE Trans. Autom. Control 48 (6), 988–1001 (2003).Google Scholar
38. Ren, W., Beard, R. W. and Atkins, E. M., “A Survey of Consensus Problems in Multi-Agent Coordination,” Proceedings of the American Control Conference, IEEE, Portland, Oregon, USA (2005) pp. 1859–1864.Google Scholar
39. Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5 (1), 90–98 (1986).Google Scholar
40. Sutantyo, D. K., Kernbach, S., Levi, P. and Nepomnyashchikh, V., “Multi-Robot Searching Algorithm using Lévy Flight and Artificial Potential Field,” IEEE International Workshop on Safety Security and Rescue Robotics (SSRR), IEEE, Bremen, Germany (2010) pp. 1–6.Google Scholar