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Robust connectivity maintenance for fallible robots

  • Jacopo Panerati
  • Marco Minelli
  • Cinara Ghedini
  • Lucas Meyer
  • Marcel Kaufmann
  • Lorenzo Sabattini
  • Giovanni Beltrame
Article
Part of the following topical collections:
  1. Special Issue: Foundations of Resilience for Networked Robotic Systems

Abstract

Multi-robot systems are promising tools for many hazardous real-world problems. In particular, the practical application of swarm robotics was identified as one of the grand challenges of the next decade. As swarms enter the real world, they have to deal with the inevitable problems of hardware, software, and communication failure, especially for long-term deployments. Communication is a key element for effective collaboration, and the ability of robots to communicate is expressed by the swarm’s connectivity. In this paper, we analyze a set of techniques to assess, control, and enforce connectivity in the context of fallible robots. Past research has addressed the issue of connectivity but, for the most part, without making system reliability a constitutional part of the model. We introduce a controller for connectivity maintenance in the presence of faults and discuss the optimization of its parameters and performance. We validate our approach in simulation and via physical robot experiments.

Keywords

Swarm robotics Connectivity Resilience Fault-tolerance Robotic hardware 

Notes

Acknowledgements

The authors would like to thank Québec’s Ministère des Relations Internationales et de la Francophonie (MRIF) and Italy’s Ministry of Foreign Affairs and International Cooperation (MAECI) for supporting SCMQI’s project QU17MO04 “Maintenance and Control of Distributed Robot and Sensor Network”.

References

  1. Ajorlou, A., Momeni, A., & Aghdam, A. G. (2010). A class of bounded distributed control strategies for connectivity preservation in multi-agent systems. IEEE Transactions on Automatic Control, 55, 2828–2833.MathSciNetCrossRefGoogle Scholar
  2. Albert, R., Jeong, H., & Barabasi, A. L. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378–382.CrossRefGoogle Scholar
  3. Avizienis, A., Laprie, J. C., Randell, B., & Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on Dependable and Secure Computing, 1(1), 11–33.  https://doi.org/10.1109/TDSC.2004.2.CrossRefGoogle Scholar
  4. Bertrand, A., & Moonen, M. (2013). Distributed computation of the fiedler vector with application to topology inference in ad hoc networks. Signal Processing, 93(5), 1106–1117.  https://doi.org/10.1016/j.sigpro.2012.12.002.CrossRefGoogle Scholar
  5. Biscani, F., Izzo, D., & Märtens, M. (2017). esa/pagmo2: pagmo 2.6.  https://doi.org/10.5281/zenodo.1054110.
  6. Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1–41.  https://doi.org/10.1007/s11721-012-0075-2.CrossRefGoogle Scholar
  7. Cao, Y., & Ren, W. (2010). Distributed coordinated tracking via a variable structure approach—part I: Consensus tracking. part II: Swarm tracking. In Proceedings of the American Control Conference, (pp. 4744–4755).Google Scholar
  8. Couceiro, M. S., Figueiredo, C. M., Rocha, R. P., & Ferreira, N. M. (2014). Darwinian swarm exploration under communication constraints: Initial deployment and fault-tolerance assessment. Robotics and Autonomous Systems, 62(4), 528–544.  https://doi.org/10.1016/j.robot.2013.12.009.CrossRefGoogle Scholar
  9. Cvetkovic, D., & Rowlinson, P. (2004). Spectral graph theory. In L. W. Beineke, R. J. Wilson, & P. J. Cameron (Eds.), Topics in algebraic graph theory (pp. 88–112). Cambridge University Press.Google Scholar
  10. Deb, K., & Deb, K. (2014). Multi-objective Optimization (pp. 403–449). Boston: Springer.  https://doi.org/10.1007/978-1-4614-6940-7_15.CrossRefzbMATHGoogle Scholar
  11. Di Lorenzo, P., & Barbarossa, S. (2014). Distributed estimation and control of algebraic connectivity over random graphs. IEEE Transactions on Signal Processing, 62(21), 5615–5628.  https://doi.org/10.1109/TSP.2014.2355778.MathSciNetCrossRefzbMATHGoogle Scholar
  12. Do, K. D. (2008). Formation tracking control of unicycle-type mobile robots with limited sensing ranges. IEEE Transactions on Control Systems Technology, 16, 527–538.CrossRefGoogle Scholar
  13. Elsayed, E. A. (2012). Reliability engineering (2nd ed.). Hoboken: Wiley Publishing.zbMATHGoogle Scholar
  14. Fiedler, M. (1973). Algebraic connectivity of graphs. Czechoslovak Mathematical Journal, 23(2), 298–305.MathSciNetzbMATHGoogle Scholar
  15. Gasparri, A., Sabattini, L., & Ulivi, G. (2017). Bounded control law for global connectivity maintenance in cooperative multi-robot systems. IEEE Transactions on Robotics, 33(3), 700–717.  https://doi.org/10.1109/TRO.2017.2664883.CrossRefGoogle Scholar
  16. Ghedini, C., & Ribeiro, C. H. C. (2011). Rethinking failure and attack tolerance assessment in complex networks. Physica A: Statistical Mechanics and its Applications, 390(23–24), 4684–4691.MathSciNetCrossRefGoogle Scholar
  17. Ghedini, C., Secchi, C., Ribeiro, C.H.C., & Sabattini, L. (2015). Improving robustness in multi-robot networks. In: Proceedings of the IFAC Symposium on Robot Control (SYROCO), Salvador, Brazil.Google Scholar
  18. Ghedini, C., Ribeiro, C.H.C., & Sabattini, L. (2016). A decentralized control strategy for resilient connectivity maintenance in multi-robot systems subject to failures. In Proceedings of the International Symposium on Distributed Autonomous Robotic Systems (DARS), London, UK.Google Scholar
  19. Ghedini, C., Ribeiro, C., & Sabattini, L. (2017). Toward fault-tolerant multi-robot networks. Networks, 70(4), 388–400.  https://doi.org/10.1002/net.21784.MathSciNetCrossRefGoogle Scholar
  20. Godsil, C., & Royle, G. (2001). Algebraic graph theory. Berlin: Springer.CrossRefGoogle Scholar
  21. Gupta, S., Ansari, A., Feng, S., & Mahlke, S. (2009). Adaptive online testing for efficient hard fault detection. In: 2009 IEEE International Conference on Computer Design, IEEE, (pp. 343–349).  https://doi.org/10.1109/ICCD.2009.5413132.
  22. Gutierrez, A., Campo, A., Dorigo, M., Donate, J., Monasterio-Huelin, F., & Magdalena, L. (2009). Open e-puck range and bearing miniaturized board for local communication in swarm robotics. In 2009 IEEE International Conference on Robotics and Automation, (pp. 3111–3116),  https://doi.org/10.1109/ROBOT.2009.5152456.
  23. He, Z., Liu, S., & Zhan, M. (2013). Dynamical robustness analysis of weighted complex networks. Physica A: Statistical Mechanics and its Applications, 392(18), 4181–4191.MathSciNetCrossRefGoogle Scholar
  24. Hsieh, M. A., Cowley, A., Kumar, V., & Talyor, C. J. (2008). Maintaining network connectivity and performance in robot teams. Journal of Field Robotics, 25(1), 111–131.CrossRefGoogle Scholar
  25. Hutter, F., Hoos, H. H., & Stützle, T. (2007). Automatic algorithm configuration based on local search. Aaai, 7, 1152–1157.Google Scholar
  26. Ji, M., & Egerstedt, M. (2007). Distributed coordination control of multiagent systems while preserving connectedness. IEEE Transactions on Robotics, 23, 693–703.CrossRefGoogle Scholar
  27. Kantor, G., Singh, S., Peterson, R., Rus, D., Das, A., Kumar, V., et al. (2006). Distributed search and rescue with robot and sensor teams (pp. 529–538). Berlin: Springer.  https://doi.org/10.1007/10991459_51.CrossRefGoogle Scholar
  28. Karnik, T., & Hazucha, P. (2004). Characterization of soft errors caused by single event upsets in cmos processes. IEEE Transactions on Dependable and Secure Computing, 1(2), 128–143.  https://doi.org/10.1109/TDSC.2004.14.CrossRefGoogle Scholar
  29. Krupke, D., Ernestus, M., Hemmer, M., & Fekete, S.P. (2015). Distributed cohesive control for robot swarms: Maintaining good connectivity in the presence of exterior forces. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 413–420).  https://doi.org/10.1109/IROS.2015.7353406
  30. Manzano, M., Calle, E., Torres-Padrosa, V., Segovia, J., & Harle, D. (2013). Endurance: A new robustness measure for complex networks under multiple failure scenarios. Computer Networks, 57(17), 3641–3653.CrossRefGoogle Scholar
  31. Minelli, M., Kaufmann, M., Panerati, J., Ghedini, C., Beltrame, G., & Sabattini, L. (2018). Stop, think, and roll: Online gain optimization for resilient multi-robot topologies. In Proceedings of the International Symposium on Distributed Autonomous Robotic Systems (DARS), Boulder, CO.Google Scholar
  32. Mosteo, A.R., Montano, L., & Lagoudakis, M.G. (2008). Multi-robot routing under limited communication range. In 2008 IEEE International Conference on Robotics and Automation, (pp. 1531–1536).  https://doi.org/10.1109/ROBOT.2008.4543419
  33. Notarstefano, G., Savla, K., Bullo, F., & Jadbabaie, A. (2006). Maintaining limited–range connectivity among second–order agents. In Proceedings of the American Control Conference, (pp. 2134–2129).Google Scholar
  34. Panerati, J., Abdi, S., & Beltrame, G. (2014). Balancing system availability and lifetime with dynamic hidden markov models. In 2014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), (pp. 240–247).  https://doi.org/10.1109/AHS.2014.6880183.
  35. Panerati, J., Gianoli, L., Pinciroli, C., Shabah, A., Nicolescu, G., & Beltrame, G. (2018). From swarms to stars: Task coverage in robot swarms with connectivity constraints. In 2018 IEEE International Conference on Robotics and Automation (ICRA).Google Scholar
  36. Pei, Y., Mutka, M.W., & Xi, N. (2010). Coordinated multi-robot real-time exploration with connectivity and bandwidth awareness. In 2010 IEEE International Conference on Robotics and Automation, (pp. 5460–5465).  https://doi.org/10.1109/ROBOT.2010.5509803
  37. Pinciroli, C., & Beltrame, G. (2016). Swarm-oriented programming of distributed robot networks. Computer, 49(12), 32–41.CrossRefGoogle Scholar
  38. Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., et al. (2012). Argos: A modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intelligence, 6(4), 271–295.  https://doi.org/10.1007/s11721-012-0072-5.CrossRefGoogle Scholar
  39. Pinciroli, C., Lee-Brown, A., & Beltrame, G. (2016). A tuple space for data sharing in robot swarms. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (Formerly BIONETICS), ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), BICT’15, ICST, Brussels, (pp. 287–294).  https://doi.org/10.4108/eai.3-12-2015.2262503
  40. Poonawala, H.A., & Spong, M.W. (2015). Decentralized estimation of the algebraic connectivity for strongly connected networks. In American Control Conference (ACC), IEEE, (pp. 4068–4073).Google Scholar
  41. Rathnam, & Birk, A. (2011). Distributed communicative exploration under underwater communication constraints. In 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, (pp. 339–344).  https://doi.org/10.1109/SSRR.2011.6106767
  42. Roberts, J.F., Stirling, T.S., Zufferey, J.C., & Floreano, D. (2009). 2.5d infrared range and bearing system for collective robotics. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, (pp. 3659–3664).  https://doi.org/10.1109/IROS.2009.5354263
  43. Robuffo Giordano, P., Franchi, A., Secchi, C., & Bülthoff, H. H. (2013). A passivity-based decentralized strategy for generalized connectivity maintenance. The International Journal of Robotics Research, 32(3), 299–323.CrossRefGoogle Scholar
  44. Sabattini, L., Chopra, N., & Secchi, C. (2013a). Decentralized connectivity maintenance for cooperative control of mobile robotic systems. The International Journal of Robotics Research, 32(12), 1411–1423.  https://doi.org/10.1177/0278364913499085.CrossRefGoogle Scholar
  45. Sabattini, L., Secchi, C., Chopra, N., & Gasparri, A. (2013b). Distributed control of multi-robot systems with global connectivity maintenance. IEEE Transactions on Robotics, 29(5), 1326–1332.CrossRefGoogle Scholar
  46. Sahai, T., Speranzon, A., & Banaszuk, A. (2012). Hearing the clusters of a graph: A distributed algorithm. Automatica, 48(1), 15–24.  https://doi.org/10.1016/j.automatica.2011.09.019.MathSciNetCrossRefzbMATHGoogle Scholar
  47. Şahin, E., Girgin, S., Bayindir, L., & Turgut, A. E. (2008). Swarm robotics (pp. 87–100). Berlin: Springer.  https://doi.org/10.1007/978-3-540-74089-6_3.CrossRefGoogle Scholar
  48. Støy, K. (2001). Using situated communication in distributed autonomous mobile robotics. In Proceedings of the Seventh Scandinavian Conference on Artificial Intelligence, SCAI ’01, IOS Press, Amsterdam, (pp. 44–52). URL http://dl.acm.org/citation.cfm?id=645855.669785
  49. Tardioli, D., Mosteo, A., Riazuelo, L., Villarroel, J., & Montano, L. (2010). Enforcing network connectivity in robot team missions. The International Journal of Robotics Research, 29(4), 460–480.  https://doi.org/10.1177/0278364909358274.CrossRefGoogle Scholar
  50. Vasisht, D., Kumar, S., & Katabi, D. (2016). Decimeter-level localization with a single wifi access point. In 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16), USENIX Association, Santa Clara, (pp. 165–178). URL https://www.usenix.org/conference/nsdi16/technical-sessions/presentation/vasisht
  51. Wasserman, S., Faust, K., & Iacobucci, D. (1994). Social network analysis : Methods and applications (structural analysis in the social sciences). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  52. Xiao, L., Boyd, S., & Kim, S. J. (2007). Distributed average consensus with least-mean-square deviation. Journal of Parallel and Distributed Computing, 67(1), 33–46.  https://doi.org/10.1016/j.jpdc.2006.08.010.CrossRefzbMATHGoogle Scholar
  53. Yang, P., Freeman, R. A., Gordon, G. J., Lynch, K. M., Srinivasa, S. S., & Sukthankar, R. (2010). Decentralized estimation and control of graph connectivity for mobile sensor networks. Automatica, 46(2), 390–396.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer and Software EngineeringPolytechnique MontréalMontrealCanada
  2. 2.Department of Science and Methods of EngineeringUniversità degli Studi di Modena e Reggio EmiliaReggio EmiliaItaly
  3. 3.Departamento de Computação CientíficaInstituto Tecnológico de AeronáuticaSão José dos CamposBrazil

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