Robust connectivity maintenance for fallible robots

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Notes

  1. 1.

    This definition of the edge-weights introduces a discontinuity in the control action that can be avoided introducing a smooth bump function (Do 2008). However, from an implementation viewpoint, the effect of the discontinuity can be made negligible by choosing a sufficiently small threshold \(\varDelta \).

  2. 2.

    https://www.k-team.com/mobile-robotics-products/khepera-iv.

  3. 3.

    https://github.com/MISTLab/Buzz.

  4. 4.

    https://github.com/MISTLab/BuzzKH4.

  5. 5.

    http://optitrack.com/products/prime-13/specs.html.

  6. 6.

    https://github.com/MISTLab/blabbermouth.

  7. 7.

    To do so, we evaluated all the combinations of gains using the following values: [0.01, 0.25, 0.5, 0.75, 1., 1.5, 2.0].

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.

    MathSciNet  Article  MATH  Google Scholar 

  2. Albert, R., Jeong, H., & Barabasi, A. L. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378–382.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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).

  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.

    Article  Google 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.

  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.

    Google 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.

    MathSciNet  Article  MATH  Google 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.

    Article  Google Scholar 

  13. Elsayed, E. A. (2012). Reliability engineering (2nd ed.). Hoboken: Wiley Publishing.

    Google Scholar 

  14. Fiedler, M. (1973). Algebraic connectivity of graphs. Czechoslovak Mathematical Journal, 23(2), 298–305.

    MathSciNet  MATH  Google 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.

    Article  Google 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.

    MathSciNet  Article  Google 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.

  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.

  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.

    MathSciNet  Article  Google Scholar 

  20. Godsil, C., & Royle, G. (2001). Algebraic graph theory. Berlin: Springer.

    Google 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.

    MathSciNet  Article  MATH  Google 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.

    Article  Google 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.

    Article  Google 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.

    Google 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.

    Article  Google 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.

    Article  Google 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.

  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).

  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).

  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.

    Article  Google 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.

    Article  Google 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).

  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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    MathSciNet  Article  MATH  Google 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.

    Google 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.

    Article  Google 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.

    Google 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.

    Article  MATH  Google 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.

    MathSciNet  Article  MATH  Google Scholar 

Download references

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”.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jacopo Panerati.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is one of the several papers published in Autonomous Robots comprising the Special Issue on Foundations of Resilience for Networked Robotic Systems.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Panerati, J., Minelli, M., Ghedini, C. et al. Robust connectivity maintenance for fallible robots. Auton Robot 43, 769–787 (2019). https://doi.org/10.1007/s10514-018-9812-8

Download citation

Keywords

  • Swarm robotics
  • Connectivity
  • Resilience
  • Fault-tolerance
  • Robotic hardware