Humanoid Multi-robot Systems

  • John E. AndersonEmail author
Reference work entry


The ability to function socially, both directly in groups and indirectly through understanding the needs and perspectives of others, is an important part of intelligent behavior. This chapter introduces important elements of multi-agent and multi-robot systems and focuses on the particular issues brought about when humanoid robots are employed. Previous work using humanoid robots - both inside and outside of robotics competitions - is reviewed, and open problems are discussed.


  1. 1.
    AAMAS 2017 conference. Accessed 31 July 2017
  2. 2.
    J. Bagot, J. Anderson, J. Baltes, Vision-based multi-agent slam for humanoid robots, in Proceedings of the 5th International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS-2008) (2008), pp. 171–176Google Scholar
  3. 3.
    T. Balch, The impact of diversity on performance in multi-robot foraging, in Proceedings of the Third Annual Conference on Autonomous Agents (AGENTS’99), New York (ACM, 1999), pp. 92–99Google Scholar
  4. 4.
    J. Baltes, Localization for mobile robots using lines, in Proceedings of the Seventh International Conference on Control, Automation, Robotics and Vision (ICARCV) (2002)Google Scholar
  5. 5.
    J. Baltes, N.M. Mayer, J. Anderson, K.-Y. Tu, A. Liu, The humanoid leagues in robot soccer competitions, in Proceedings of the IJCAI Workshop on Competitions in Artificial Intelligence and Robotics, Pasadena (AAAI Press, 2009), pp. 9–16Google Scholar
  6. 6.
    J. Baltes, C.T. Cheng, J. Bagot, J. Anderson, Vision-based obstacle run for teams of humanoid robots (demonstrated system), in Proceedings of the 10th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS-2011), Taipei (2011), pp. 1319–1320Google Scholar
  7. 7.
    J. Baltes, J. Bagot, S. Sadeghnejad, J. Anderson, C.-H. Hsu, Full-body motion planning for humanoid robots using rapidly exploring random trees. KI – Künstliche Intelligenz 8, 1–11 (2016)Google Scholar
  8. 8.
    J. Baltes, K.-Y. Tu, S. Sadeghnejad, J. Anderson, HuroCup: competition for multi-event humanoid robot athletes. Knowl. Eng. Rev. 1–14 (2016). FirstViewGoogle Scholar
  9. 9.
    S. Behnke, J. Stückler, Hierarchical reacive control for humanoid soccer robots. Int. J. Humanoid Robot. 5, 375–396 (2008)CrossRefGoogle Scholar
  10. 10.
    W. Burgard, M. Moorsy, C. Stachniss, F. Schneidery, Coordinated multi-robot exploration. IEEE Trans. Robot. 21(3), 376–386 (2005)CrossRefGoogle Scholar
  11. 11.
    R. Gerndt, D. Seifert, J. Baltes, S. Sadeghnejad, S. Behnke, Humanoid robots in soccer – robots versus humans in robocup 2050. IEEE-RAS Robot. Autom. Mag. 22(3), 147–154 (2015)CrossRefGoogle Scholar
  12. 12.
    D. Goldberg, M.J. Matarić, Interference as a tool for designing and evaluating multi-robot controllers, in Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Conference on Innovative Applications of Artificial Intelligence (AAAI’97/IAAI’97) (AAAI Press, 1997), pp. 637–642Google Scholar
  13. 13.
    T. Gunn, J. Anderson, Dynamic heterogeneous team formation for robotic urban search and rescue. J. Comput. Syst. Sci. 81(3), 553–567 (2015)CrossRefGoogle Scholar
  14. 14.
    M. Javadi, S.M. Azar, S. Azami, S.S. Ghidary, S. Sadeghnejad, J. Baltes, Humanoid robot detection using deep learning: a speed-accuracy tradeoff, in Proceedings of RoboCup-2017, Nagoya (2017)Google Scholar
  15. 15.
    J. Kiener, O. Von Stryk, Cooperation of heterogeneous, autonomous robots: a case study of humanoid and wheeled robots, in Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007) (IEEE, 2007), pp. 959–964Google Scholar
  16. 16.
    M. Missura, C. Münstermann, P. Allgeuer, M. Schwarz, J. Pastrana, S. Schueller, M. Schreiber, S. Behnke, Learning to improve capture steps for disturbance rejection in humanoid soccer, in Proceedings of RoboCup-2013, Eindhoven (2013)Google Scholar
  17. 17.
    G. Nagy, J. Anderson, Active team management strategies for multi-robot teams in dangerous environments, in Advances in Artificial Intelligence: 30th Canadian Conference on Artificial Intelligence, Edmonton (2017), pp. 385–396Google Scholar
  18. 18.
    R. Wagner, U. Frese, and B. Bäuml, Graph SLAM with Signed Distance Function Maps on a Humanoid Robot. Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2014), pp. 2691–2698Google Scholar
  19. 19.
    G. Weiss (ed.), Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence (MIT Press, Cambridge, 1999)Google Scholar
  20. 20.
    M. Wooldridge, An Introduction to Multiagent Systems, 2nd edn. (Wiley, New York, 2009)Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of ManitobaWinnipegCanada

Personalised recommendations