Design of Powered Floor Systems for Mobile Robots with Differential Evolution

  • Eric MedvetEmail author
  • Stefano Seriani
  • Alberto Bartoli
  • Paolo Gallina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11454)


Mobile robots depend on power for performing their task. Powered floor systems, i.e., surfaces with conductive strips alternatively connected to the two poles of a power source, are a practical and effective way for supplying power to robots without interruptions, by means of sliding contacts. Deciding where to place the sliding contacts so as to guarantee that a robot is actually powered irrespective of its position and orientation is a difficult task. We here propose a solution based on Differential Evolution: we formally define problem-specific constraints and objectives and we use them for driving the evolutionary search. We validate experimentally our proposed solution by applying it to three real robots and by studying the impact of the main problem parameters on the effectiveness of the evolved designs for the sliding contacts. The experimental results suggest that our solution may be useful in practice for assisting the design of powered floor systems.


Multi-objective optimization Automatic design Swarm Robotics Evolutionary Robotics 


  1. 1.
    Shing, A., Wong, P.: Wear of pantograph collector strips. Proc. Inst. Mech. Eng. Part F: J. Rail Rapid Transit 222(2), 169–176 (2008)CrossRefGoogle Scholar
  2. 2.
    Pastena, L.: A catenary-free electrification for urban transport: an overview of the tramwave system. IEEE Electrification Mag. 2(3), 16–21 (2014)CrossRefGoogle Scholar
  3. 3.
    Wang, J., Hu, M., Cai, C., Lin, Z., Li, L., Fang, Z.: Optimization design of wireless charging system for autonomous robots based on magnetic resonance coupling. AIP Adv. 8(5), 055004 (2018)CrossRefGoogle Scholar
  4. 4.
    Yang, M., Yang, G., Li, E., Liang, Z., Lin, H.: Modeling and analysis of wireless power transmission system for inspection robot. In: 2013 IEEE International Symposium on Industrial Electronics (ISIE), pp. 1–5. IEEE (2013)Google Scholar
  5. 5.
    Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)CrossRefGoogle Scholar
  6. 6.
    Nolfi, S., Bongard, J., Husbands, P., Floreano, D.: Evolutionary robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 2035–2068. Springer, Cham (2016). Scholar
  7. 7.
    Watson, R.A., Ficici, S.G., Pollack, J.B.: Embodied evolution: distributing an evolutionary algorithm in a population of robots. Robot. Auton. Syst. 39(1), 1–18 (2002)CrossRefGoogle Scholar
  8. 8.
    Klingner, J., Kanakia, A., Farrow, N., Reishus, D., Correll, N.: A stick-slip omnidirectional powertrain for low-cost swarm robotics: mechanism, calibration, and control. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 846–851. IEEE (2014)Google Scholar
  9. 9.
    Sloane, N.J., Wyner, A.D.: Claude Elwood Shannon: Collected Papers. IEEE press, Piscataway (1993)CrossRefGoogle Scholar
  10. 10.
    Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRefGoogle Scholar
  12. 12.
    Heinerman, J., Rango, M., Eiben, A.: Evolution, individual learning, and social learning in a swarm of real robots. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 1055–1062. IEEE (2015)Google Scholar
  13. 13.
    Heinerman, J., Zonta, A., Haasdijk, E., Eiben, A.E.: On-line evolution of foraging behaviour in a population of real robots. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9598, pp. 198–212. Springer, Cham (2016). Scholar
  14. 14.
    Silva, F., Correia, L., Christensen, A.L.: Evolutionary online behaviour learning and adaptation in real robots. Roy. Soc. Open Sci. 4(7), 160938 (2017)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eric Medvet
    • 1
    Email author
  • Stefano Seriani
    • 1
  • Alberto Bartoli
    • 1
  • Paolo Gallina
    • 1
  1. 1.Department of Engineering and ArchitectureUniversity of TriesteTriesteItaly

Personalised recommendations