Evolving Group Transport Strategies for e-Puck Robots: Moving Objects Towards a Target Area

  • Muhanad H. Mohammed Alkilabi
  • Aparajit Narayan
  • Chuan Lu
  • Elio Tuci
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 6)


This paper describes a set of experiments in which a homogeneous group of simulated e-puck robots is required to coordinate their actions in order to transport cuboid objects towards a target location. The objects are heavy enough to require the coordinated effort of all the members of the group to be transported. The agents’ controllers are dynamic neural networks synthesised through evolutionary computation techniques. The results of our experiments indicate that the most effective transport strategies generated by artificial evolution are those in which the robots exploit occlusion by pushing the objects across the portion of their surface, where they occlude the direct line of sight to the goal. The main contribution of this study is the analysis of the relationships between the characteristics of the object (i.e., mass and length), the morphology of the robots, and the group performance. We also test the scalability of the occlusion-based transport strategies to group larger than those used during the evolutionary design phase.



Muhanad H. Mohammed Alkilabi thanks Iraqi Ministry of Higher Education and Scientific Research for funding his Ph.D.


  1. 1.
    Beer, R., Gallagher, J.: Evolving dynamic neural networks for adaptive behavior. Adapt. Behav. 1(1), 91–122 (1992)CrossRefGoogle Scholar
  2. 2.
    Berman, S., Lindsey, Q., Sakar, M., Kumar, V., Pratt, S.: Experimental study and modeling of group retrieval in ants as an approach to collective transport in swarm robotic systems. Proc. IEEE 99(9), 1470–1481 (2011)CrossRefGoogle Scholar
  3. 3.
    Chen, J., Gauci, M., Li, W., Kolling, A., Gross, R.: Occlusion-based cooperative transport with a swarm of miniature mobile robots. IEEE Trans. Robot. 31(2), 307–321 (2015)CrossRefGoogle Scholar
  4. 4.
    Czaczkes, T., Ratnieks, F.: Cooperative transport in ants (hymenoptera: Formicidae) and elsewhere. Myrmecol. News 18, 1–11 (2013)Google Scholar
  5. 5.
    Feener, J., Donald, H., Moss, K.: Defense against parasites by hitchhikers in leaf-cutting ants: a quantitative assessment. Behav. Ecol. Sociobiol. 26(1), 17–29 (1990)CrossRefGoogle Scholar
  6. 6.
    Francesca, G., et al.: An experiment in automatic design of robot swarms. In: Proceedings of the 9th International Conference on Swarm Intelligence, pp. 25–37. Springer (2014)Google Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  8. 8.
    Groß, R., Dorigo, M.: Evolution of solitary and group transport behaviors for autonomous robots capable of self-assembling. Adapt. Behav. 16(5), 285–305 (2008)CrossRefGoogle Scholar
  9. 9.
    Gutiérrez, A., Campo, A., Dorigo, M., Amor, D., Magdalena, L., Monasterio-Huelin, F.: An open localization and local communication embodied sensor. Sensors 8(11), 7545–7563 (2008)CrossRefGoogle Scholar
  10. 10.
    Hölldobler, B., Wilson, E.: The Ants. Harvard University Press, Cambridge (1990)CrossRefGoogle Scholar
  11. 11.
    Mohammed Alkilabi, M.H., Lu, C., Tuci, E.: Cooperative object transport using evolutionary swarm robotics methods. In: Proceedings of the European Conference on Artificial Life, vol. 1, pp. 464–471. MIT (2015)Google Scholar
  12. 12.
    Mohammed Alkilabi, M.H., Narayan, A., Tuci, E.: Design and analysis of proximate mechanisms for cooperative transport in real robots. In: Dorigo, M., et al. (ed.) Proceedings of the 10th International Conference on Swarm Intelligence (ANTS 2016). Springer (2016, in Press)Google Scholar
  13. 13.
    Mondada, F., et al.: The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th International Conference on Autonomous Robot Systems and Competitions, vol. 1, pp. 59–65 (2009)Google Scholar
  14. 14.
    Tanner, C.: Resource characteristics and competition affect colony and individual foraging strategies of the wood ant formica integroides. Ecol. Entomol. 33(1), 127–136 (2008)CrossRefGoogle Scholar
  15. 15.
    Wang, Z., Schwager, M.: Multi-robot manipulation with no communication using only local measurements. In: Proceedings of the 54th IEEE Conference on Decision and Control (CDC), pp. 380–385. IEEE (2015)Google Scholar
  16. 16.
    Wang, Z., Schwager, M.: Kinematic multi-robot manipulation with no communication using force feedback. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 427–432. IEEE (2016)Google Scholar
  17. 17.
    Wang, Z., Schwager, M.: Multi-robot manipulation without communication. In: Distributed Autonomous Robotic Systems, pp. 135–149. Springer (2016)Google Scholar
  18. 18.
    Wang, Z., Takano, Y., Hirata, Y., Kosuge, K.: A pushing leader based decentralized control method for cooperative object transportation. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 1035–1040. IEEE (2004)Google Scholar
  19. 19.
    Yamamoto, A., Ishihara, S., Fuminori, I.: Fragmentation or transportation: mode of large-prey retrieval in arboreal and ground nesting ants. Insect Behav. 22, 1–11 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Muhanad H. Mohammed Alkilabi
    • 1
    • 2
  • Aparajit Narayan
    • 1
  • Chuan Lu
    • 1
  • Elio Tuci
    • 1
  1. 1.Computer Science DepartmentAberystwyth UniversityAberystwythUK
  2. 2.Computer Science DepartmentCollege of Science, Kerbala UniversityKerbalaIraq

Personalised recommendations