Embodied Evolution for Collective Indoor Surveillance and Location

  • Pedro Trueba
  • Abraham Prieto
  • Francisco Bellas
  • Richard J. Duro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9108)


This work is devoted with the application of a canonical Embodied Evolution algorithm in a collective task in which a fleet of Micro Aerial Vehicles (MAVs) have to survey an indoor scenario. The MAVs need to locate themselves to keep track of their trajectories and to share this information with other robots. This localization is performed using the IMU, artificial landmarks that can be sensed using the onboard camera and the position of other MAVs in sight. The accuracy in the decentralized location of each MAV has been included as a part of the problem to solve. Therefore, the collective control system is in charge of organizing the MAVs in the scenario in order to increase the accuracy of the fleet location, and consequently, the speed at which a new point of interest is reached.


Embodied Evolution Indoor Navigation Collective Tasks 


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  1. 1.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)Google Scholar
  2. 2.
    Siegwart, R.: Nourbakhsh. I., Scaramuzza, D.: Introduction to Autonomous Mobile Robots. MIT Press (2011)Google Scholar
  3. 3.
    Chatterjee, A., Rakshit, A., Singh, N.: Vision Based Autonomous Robot Navigation: Algorithms and Implementations. SCI, vol. 455. Springer, Heidelberg (2013)Google Scholar
  4. 4.
    Shen, S.: Michael, N., Kumar, V.: Autonomous multi-floor indoor navigation with a computationally constrained MAV. In: Proceedings ICRA 2011, pp. 20–25 (2011)Google Scholar
  5. 5.
    Lippiello, V., Loianno, G., Siciliano, B.: MAV indoor navigation based on a closed-form solution for absolute scale velocity estimation using Optical Flow and inertial data. In: Proceedings CDC-ECC 2011, pp. 3566–3571 (2011)Google Scholar
  6. 6.
    Trianni, V., Nolfi, S.: Evolving collective control, cooperation and distributed cognition. In: Handbook of Collective Robotics, pp. 127–166. Springer (2012)Google Scholar
  7. 7.
    Nitschke, G.S.: Neuro-Evolution approaches to collective behavior. In: Proceedings CEC 2009, pp. 1554–1561 (2009)Google Scholar
  8. 8.
    Watson, R., Ficici, S., Pollack, J.: Embodied evolution: Distributing an evolutionary algorithm in a population of robots. Robotics and Autonomous Systems 39(1), 1–18 (2002)CrossRefGoogle Scholar
  9. 9.
    Schut, M.C., Haasdijk, E., Prieto, A.: Is situated evolution an alternative for classical evolution?. In: Proceedings CEC 2009, pp. 2971–2976 (2009)Google Scholar
  10. 10.
    Haasdijk, E., Eiben, A.E., Karafotias, G.: On-line evolution of robot controllers by an encapsulated evolution strategy. In: Proceedings IEEE CEC 2010, pp. 1–7 (2010)Google Scholar
  11. 11.
    Elfwing, S., Uchibe, E., Doya, K., Christensen, H.: Darwinian embodied evolution of the learning ability for survival. Adaptive Behavior 19(2), 101–120 (2011)CrossRefGoogle Scholar
  12. 12.
    Bredeche, N., Montanier, J.M., Liu, W., Winfield, A.: Environment-driven Distributed Evolutionary Adaptation in a Population of Autonomous Robotic Agents. Mathem. and Comput. Modelling of Dynamical Systems 18(1), 101–129 (2012)CrossRefzbMATHGoogle Scholar
  13. 13.
    Prieto, A., Becerra, J.A., Bellas, F., Duro, R.J.: Open-ended Evolution as a means to Self-Organize Heterogeneous Multi-Robot Systems in Real Time. Robotics and Autonomous Systems 58, 1282–1291 (2010)CrossRefGoogle Scholar
  14. 14.
    Trueba, P., Prieto, A., Bellas, F., Caamao, P., Duro, R.J.: Specialization analysis of embodied evolution for robotic collective tasks. Robotics and Autonomous Systems 61(7), 682–693 (2012)CrossRefGoogle Scholar
  15. 15.
    Trueba, P., Prieto, A., Bellas, F.: Distributed embodied evolution for collective tasks: parametric analysis of a canonical algorithm. In: Proc. GECCO 2013, pp. 37–38 (2013)Google Scholar
  16. 16.
    Olson, E.: AprilTag: A robust and flexible visual fiducial system. In: Proceedings ICRA 2011, pp. 3400–3407 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pedro Trueba
    • 1
  • Abraham Prieto
    • 1
  • Francisco Bellas
    • 1
  • Richard J. Duro
    • 1
  1. 1.Integrated Group for Engineering ResearchUniversidade da CoruñaFerrolSpain

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