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Journal of Intelligent & Robotic Systems

, Volume 92, Issue 3–4, pp 395–412 | Cite as

Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging

  • Farshad Arvin
  • Simon Watson
  • Ali Emre Turgut
  • Jose Espinosa
  • Tomáš Krajník
  • Barry Lennox
Article

Abstract

Swarm robotics studies the intelligent collective behaviour emerging from long-term interactions of large number of simple robots. However, maintaining a large number of robots operational for long time periods requires significant battery capacity, which is an issue for small robots. Therefore, re-charging systems such as automated battery-swapping stations have been implemented. These systems require that the robots interrupt, albeit shortly, their activity, which influences the swarm behaviour. In this paper, a low-cost on-the-fly wireless charging system, composed of several charging cells, is proposed for use in swarm robotic research studies. To determine the system’s ability to support perpetual swarm operation, a probabilistic model that takes into account the swarm size, robot behaviour and charging area configuration, is outlined. Based on the model, a prototype system with 12 charging cells and a small mobile robot, Mona, was developed. A series of long-term experiments with different arenas and behavioural configurations indicated the model’s accuracy and demonstrated the system’s ability to support perpetual operation of multi-robotic system.

Keywords

Swarm robotics Wireless charging Long-term autonomy Perpetual swarm 

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Notes

Acknowledgments

This work was supported by Innovate UK (Project No. KTP009811), UK EPSRC (Reference: EP/P01366X/1) and Czech Science Foundation project 17-27006Y.

References

  1. 1.
    Gerling, K., Hebesberger, D., Dondrup, C., Körtner, T., Hanheide, M.: Robot deployment in long-term care. Zeitschrift für Gerontologie und Geriatrie 49(4), 288–297 (2016)CrossRefGoogle Scholar
  2. 2.
    Hebesberger, D., Koertner, T., Gisinger, C., Pripfl, J., Dondrup, C.: Lessons learned from the deployment of a long-term autonomous robot as companion in physical therapy for older adults with dementia: a mixed methods study. In: 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (2016)Google Scholar
  3. 3.
    Bayindir, L.: A review of swarm robotics tasks. Neurocomputing 172, 292–321 (2016)CrossRefGoogle Scholar
  4. 4.
    Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: International Workshop on Swarm Robotics, pp. 10–20. Springer (2004)Google Scholar
  5. 5.
    Valentini, G., Ferrante, E., Hamann, H., Dorigo, M.: Collective decision with 100 kilobots: speed versus accuracy in binary discrimination problems. Auton. Agent. Multi-Agent Syst. 30(3), 553–580 (2016)CrossRefGoogle Scholar
  6. 6.
    Arvin, F., Murray, J.C., Shi, L., Zhang, C., Yue, S.: Development of an autonomous micro robot for swarm robotics. In: IEEE International Conference on Mechatronics and Automation (ICMA), pp. 635–640 (2014)Google Scholar
  7. 7.
    Turgut, A.E., Çelikkanat, H., Gökçe, F., Sahin, E.: Self-organized flocking in mobile robot swarms. Swarm Intell. 2(2), 97–120 (2008)CrossRefGoogle Scholar
  8. 8.
    McLurkin, J., Smith, J., Frankel, J., Sotkowitz, D., Blau, D., Schmidt, B.: Speaking swarmish: human-robot interface design for large swarms of autonomous mobile robots. In: AAAI Spring Symposium (2006)Google Scholar
  9. 9.
    Santos, J.M., Krajník, T., Fentanes, J.P., Duckett, T.: Lifelong information-driven exploration to complete and refine 4-D spatio-temporal maps. IEEE Robotics and Automation Letters 1(2), 684–691 (2016)CrossRefGoogle Scholar
  10. 10.
    Bonani, M., Longchamp, V., Magnenat, S., Rétornaz, P., Burnier, D., Roulet, G., Vaussard, F., Bleuler, H., Mondada, F.: The Marxbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In: IROS (2010)Google Scholar
  11. 11.
    Floreano, D., Mondada, F.: Automatic creation of an autonomous agent: genetic evolution of a neural network driven robot. In: 3rd International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3 (1994)Google Scholar
  12. 12.
    Watson, R.A., Ficiei, S., Pollack, J. B.: Embodied evolution: embodying an evolutionary algorithm in a population of robots. In: Congress on Evolutionary Computation (1999)Google Scholar
  13. 13.
    Winfield, A.F., Nembrini, J.: Emergent swarm morphology control of wireless networked mobile robots. In: Morphogenetic Engineering (2012)CrossRefGoogle Scholar
  14. 14.
    Krieger, M.J., Billeter, J.-B., Keller, L.: Ant-like task allocation and recruitment in cooperative robots. Nature 406(6799), 992–995 (2000)CrossRefGoogle Scholar
  15. 15.
    Klingner, J., Kanakia, A., Farrow, N., Reishus, D., Correll, N.: A stick-slip omnidirectional drive-train for low-cost swarm robotics: mechanism, calibration, and control. In: IROS (2014)Google Scholar
  16. 16.
    Takaya, Y.U., Arita, T.: Situated and embodied evolution in collective evolutionary robotics. In: International Symposium on Artificial Life and Robotics (2003)Google Scholar
  17. 17.
    Arvin, F., Krajník, T., Turgut, A.E., Yue, S.: COS F: Artificial Pheromone System for Robotic Swarms Research. In: IROS (2015)Google Scholar
  18. 18.
    Deyle, T., Reynolds, M.: Surface based wireless power transmission and bidirectional communication for autonomous robot swarms. In: ICRA (2008)Google Scholar
  19. 19.
    Karpelson, M., et al.: A wirelessly powered, biologically inspired ambulatory microrobot. In: ICRA (2014)Google Scholar
  20. 20.
    Zhang, Z., Xu, X., Li, B., Deng, B.: An energy-encrypted contactless charging system for swarm robots. In: Magnetics Conference (INTERMAG) (2015)Google Scholar
  21. 21.
    Caprari, G., Estier, T., Siegwart, R.: Fascination of down scaling - alice the sugar cube robot. Journal of Micro-Mechatronics 1(3), 177–189 (2002)Google Scholar
  22. 22.
    Arvin, F., Samsudin, K., Ramli, A. R.: Development of a miniature robot for swarm robotic application. International Journal of Computer and Electrical Engineering 1, 436–442 (2009)CrossRefGoogle Scholar
  23. 23.
    Arvin, F., Murray, J., Zhang, C., Yue, S.: Colias: an autonomous micro robot for swarm robotic applications. Int. J. Adv. Robot. Syst. 11(113), 1–10 (2014)Google Scholar
  24. 24.
    Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J. -C., Floreano, D., Martinoli, A.: The e-puck, a robot designed for education in engineering. In: 9th Conference on Autonomous Robot Systems and Competitions (2009)Google Scholar
  25. 25.
    Dorigo, M., Floreano, D., Gambardella, L. M., Mondada, F., Nolfi, S., Baaboura, T., Birattari, M., Bonani, M., Brambilla, M., Brutschy, A., et al.: Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robot. Autom. Mag. 20(4), 60–71 (2013)CrossRefGoogle Scholar
  26. 26.
    Kernbach, S., Thenius, R., Kernbach, O., Schmickl, T.: Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic system. Adapt. Behav. 17(3), 237–259 (2009)CrossRefGoogle Scholar
  27. 27.
    Mondada, F., Franzi, E., Ienne, P.: Mobile robot miniaturisation: a tool for investigation in control algorithms. In: Experimental Robotics III (1994)Google Scholar
  28. 28.
    Rubenstein, M., Ahler, C., Hoff, N., Cabrera, A., Nagpal, R.: Kilobot: a low cost robot with scalable operations designed for collective behaviors. Robot. Auton. Syst. 62(7), 966–975 (2014)CrossRefGoogle Scholar
  29. 29.
    Mondada, F., Pettinaro, G.C., Guignard, A., Kwee, I.W., Floreano, D., Deneubourg, J. -L., Nolfi, S., Gambardella, L. M., Dorigo, M.: Swarm-bot: a new distributed robotic concept. Auton. Robot. 17(2–3), 193–221 (2004)CrossRefGoogle Scholar
  30. 30.
    Caprari, G., Siegwart, R.: Mobile micro-robots ready to use: Alice. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3295–3300. IEEE (2005)Google Scholar
  31. 31.
    Schmickl, T., Thenius, R., Moeslinger, C., Radspieler, G., Kernbach, S., Szymanski, M., Crailsheim, K.: Get in touch: cooperative decision making based on robot-to-robot collisions. Auton. Agent. Multi-Agent Syst. 18(1), 133–155 (2009)CrossRefGoogle Scholar
  32. 32.
    Walter, W.G.: The Living Brain. Norton, New York (1953)Google Scholar
  33. 33.
    Yuta, S., Hada, Y.: Long term activity of the autonomous robot-proposal of a bench-mark problem for the autonomy. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp 1871–1878 (1998)Google Scholar
  34. 34.
    Roufas, K., Zhang, Y., Duff, D., Yim, M.: Six degree of freedom sensing for docking using ir led emitters and receivers. In: Experimental Robotics VII, pp. 91–100. Springer (2001)Google Scholar
  35. 35.
    Minten, B.W., Murphy, R.R., Hyams, J., Micire, M.: Low-order-complexity vision-based docking. IEEE Trans. Robot. Autom. 17(6), 922–930 (2001)CrossRefGoogle Scholar
  36. 36.
    Krajník, T., Nitsche, M., Faigl, J., Vanek, P., Saska, M., Preucil, L., Duckett, T., Mejail, M.: A practical multirobot localization system. J. Intell. Robot. Syst. 76(3–4), 539–562 (2014)CrossRefGoogle Scholar
  37. 37.
    Arvin, F., Samsudin, K., Ramli, A.R.: Swarm robots long term autonomy using moveable charger. In: International Conference on Future Computer and Communication, pp. 127–130. IEEE (2009)Google Scholar
  38. 38.
    Attarzadeh, A.: Development of advanced power management for autonomous micro-robots. Master’s thesis, University of Stuttgart, Germany (2006)Google Scholar
  39. 39.
    Martinoli, A., Franzi, E., Matthey, O.: Towards a reliable set-up for bio-inspired collective experiments with real robots. In: Experimental Robotics V, pp. 595–608. Springer (1998)Google Scholar
  40. 40.
    Wei, X., Wang, Z., Dai, H.: A critical review of wireless power transfer via strongly coupled magnetic resonances. Energies 7(7), 4316–4341 (2014)CrossRefGoogle Scholar
  41. 41.
    Lu, X., Wang, P., Niyato, D., Kim, D.I., Han, Z.: Wireless charging technologies: Fundamentals, standards, and network applications. IEEE Commun. Surv. Tutorials 18, 1413–1452 (2015)CrossRefGoogle Scholar
  42. 42.
    Lu, X., Wang, P., Niyato, D., Kim, D.I., Han, Z.: Wireless networks with RF energy harvesting: a contemporary survey. IEEE Commun. Surv. Tutorials 17(2), 757–789 (2015)CrossRefGoogle Scholar
  43. 43.
    Cannon, B., Hoburg, J., Stancil, D., Goldstein, S.: Magnetic resonant coupling as a potential means for wireless power transfer to multiple small receivers. IEEE Trans. Power Electron. 24(7), 1819–1825 (2009)CrossRefGoogle Scholar
  44. 44.
    Chen, C.J., Chu, T.H., Lin, C.L., Jou, Z.C.: A study of loosely coupled coils for wireless power transfer. IEEE Trans. Circuits Syst. Express Briefs 57(7), 536–540 (2010)CrossRefGoogle Scholar
  45. 45.
    Zhong, W., Lee, C.K., Hui, S.Y.: Wireless power domino-resonator systems with noncoaxial axes and circular structures. IEEE Trans. Power Electron. 27(11), 4750–4762 (2012)CrossRefGoogle Scholar
  46. 46.
    Hu, C., Arvin, F., Xiong, C., Yue, S.: A bio-inspired embedded vision system for autonomous micro-robots: the LGMD case. IEEE Transactions on Cognitive and Developmental Systems 9(3), 241–254 (2016)CrossRefGoogle Scholar
  47. 47.
    Arvin, F., Bekravi, M.: Encoderless position estimation and error correction techniques for miniature mobile robots. Turk. J. Electr. Eng. Comput. Sci. 21, 1631–1645 (2013)CrossRefGoogle Scholar
  48. 48.
    Arvin, F., Samsudin, K., Ramli, A.R.: Development of IR-based short-range communication techniques for swarm robot applications. Advances in Electrical and Computer Engineering 10(4), 61–68 (2010)CrossRefGoogle Scholar
  49. 49.
    Krajník, T., Nitsche, M., Faigl, J., Duckett, T., Mejail, M., Preucil, L.: External localization system for mobile robotics. In: 16Th International Conference on Advanced Robotics (ICAR), pp. 1–6. IEEE (2013)Google Scholar
  50. 50.
    Ahmad, F.A., Ramli, A.R., Samsudin, K., Hashim, S.J.: Optimization of power utilization in multimobile robot foraging behavior inspired by honeybees system. Scientific World Journal 2014, 1–12 (2014)Google Scholar
  51. 51.
    Arvin, F., Turgut, A.E., Krajník, T., Yue, S.: Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm. Adapt. Behav. 24(2), 102–118 (2016)CrossRefGoogle Scholar
  52. 52.
    Ferrante, E., Turgut, A.E., Duéñez-Guzmán, E., Dorigo, M., Wenseleers, T.: Evolution of self-organized task specialization in robot swarms. PLoS Comput Biol 11(8), e1004273 (2015)CrossRefGoogle Scholar
  53. 53.
    Arvin, F., Turgut, A.E., Bazyari, F., Arikan, K.B., Bellotto, N., Yue, S.: Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method. Adapt. Behav. 22(3), 189–206 (2014)CrossRefGoogle Scholar
  54. 54.
    Arvin, F., Samsudin, K., Ramli, A. R., Bekravi, M.: Imitation of honeybee aggregation with collective behavior of swarm robots. International Journal of Computational Intelligence Systems 4(4), 739–748 (2011)Google Scholar
  55. 55.
    Neter, J., Kutner, M.H., Nachtsheim, C.J., Wasserman, W.: Applied Linear Statistical Models, vol. 4. Irwin, Chicago (1996)Google Scholar
  56. 56.
    Correll, N., Martinoli, A.: Modeling self-organized aggregation in a swarm of miniature robots. In: IEEE International Conference on Robotics and Automation, Workshop on Collective Behaviors Inspired by Biological and Biochemical Systems (2007)Google Scholar
  57. 57.
    Martinoli, A., Ijspeert, A., Mondada, F.: Understanding collective aggregation mechanisms: from probabilistic modelling to experiments with real robots. Robot. Auton. Syst. 29(1), 51–63 (1999)CrossRefGoogle Scholar
  58. 58.
    Soysal, O., Sahin, E.: A macroscopic model for self-organized aggregation in swarm robotic systems. In: Sahin, E., Spears, W., Winfield, A. (eds.) Swarm Robotics, Ser. Lecture Notes in Computer Science, vol. 4433, pp 27–42 (2007)Google Scholar
  59. 59.
    Bayindir, L., Sahin, E.: Modeling self-organized aggregation in swarm robotic systems. In: Swarm Intelligence Symposium, pp. 88–95. IEEE (2009)Google Scholar
  60. 60.
    Hamann, H.: Space-time continuous models of swarm robotics systems: Supporting global-to-local programming. Ph.D. dissertation, Department of Computer Science, University of Karlsruhe (2008)Google Scholar
  61. 61.
    Schmickl, T., Hamann, H., Worn, H., Crailsheim, K.: Two different approaches to a macroscopic model of a bio-inspired robotic swarm. Robot. Auton. Syst. 57(9), 913–921 (2009)CrossRefGoogle Scholar
  62. 62.
    Arvin, F., Attar, A., Turgut, A., Yue, S.: Power-law distribution of long-term experimental data in swarm robotics. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) Advances in Swarm and Computational Intelligence, Ser. Lecture Notes in Computer Science, vol. 9140, pp 551–559 (2015)CrossRefGoogle Scholar
  63. 63.
    Taheri, P., Mansouri, A., Schweitzer, B., Yazdanpour, M., Bahrami, M.: Electrical constriction resistance in current collectors of large-scale lithium-ion batteries. J. Electrochem. Soc. 160(10), A1731–A1740 (2013)CrossRefGoogle Scholar
  64. 64.
    Senyshyn, A., Mühlbauer, M., Dolotko, O., Hofmann, M., Ehrenberg, H.: Homogeneity of lithium distribution in cylinder-type li-ion batteries. Sci. Rep. 5, 18380 (2015)CrossRefGoogle Scholar
  65. 65.
    Garnier, S., Gautrais, J., Asadpour, M., Jost, C., Theraulaz, G.: Self-Organized Aggregation triggers collective decision making in a group of cockroach-like robots. Adapt. Behav. 17(2), 109–133 (2009)CrossRefGoogle Scholar
  66. 66.
    Jeanson, R., Rivault, C., Deneubourg, J. -L., Blanco, S., Fournier, R., Jost, C., Theraulaz, G.: Self-organized aggregation in cockroaches. Anim. Behav. 69(1), 169–180 (2005)CrossRefGoogle Scholar
  67. 67.
    Liwanag, H., Oraze, J., Costa, D., Williams, T.: Thermal benefits of aggregation in a large marine endotherm: huddling in california sea lions. J. Zool. 293(3), 152–159 (2014)CrossRefGoogle Scholar
  68. 68.
    Arvin, F., Turgut, A.E., Bellotto, N., Yue, S.: Comparison of different cue-based swarm aggregation strategies. In: Advances in Swarm Intelligence, Ser. Lecture Notes in Computer Science, vol. 8794, pp 1–8 (2014)Google Scholar
  69. 69.
    Arvin, F., Turgut, A.E., Yue, S.: Fuzzy-based aggregation with a mobile robot swarm. In: Swarm Intelligence, Ser. Lecture Notes in Computer Science, vol. 7461, pp 346–347 (2012)Google Scholar

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Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringUniversity of ManchesterManchesterUK
  2. 2.Mechanical Engineering DepartmentMiddle East Technical UniversityAnkaraTurkey
  3. 3.Artificial Intelligence Centre, Faculty of Electrical EngineeringCzech Technical UniversityPragueCzechia

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