Natural Computing

, Volume 16, Issue 4, pp 579–596 | Cite as

Task-oriented hierarchical control architecture for swarm robotic system

  • Yuquan LengEmail author
  • Cen Yu
  • Wei Zhang
  • Yang Zhang
  • Xu He
  • Weijia Zhou


An increasing number of robotic systems involving lots of robotic individuals are used to serve human, such as intelligent terminal, intelligent storage, intelligence factories, etc. It is a trend of robotics technology that robotics system will become huger with more individuals. In these systems, they form the robotic societies and need establish some computing rules and mechanisms to ensure the operation like all biological social systems. In this paper, a novel system architecture for swarm robotic system, including three layers: human–computer interaction layer, planning layer and execution layer, is put forward, which is effective for task-oriented swarm robotic system. Then, a hierarchical organizational model for the system is presented, which is used to establish management relationship between different layers and individuals. Because task-oriented characteristic is required, this paper elaborates task description knowledge to explain the relationship between tasks for task decomposition and task logic. In addition, a method of behavior generation based on proposition/transition Petri networks is designed, which would effectively assist the system to construct combined behavior using simple individual behavior to solve a variety of tasks. At last, Illustration is shown to prove effectiveness and an implementation of the method based on SociBuilder system is introduced.


Hierarchical control architecture Self-organization Task-oriented Swarm robotic system SociBuilder system 


  1. Abelson H, Allen D, Coore D, Hanson C, Homsy G, Knight TF, Nagpal R, Rauch E, Sussman G, Weiss R (1999) Morphous computing. MIT, BostonGoogle Scholar
  2. Bahceci E, Sahin E (2005) Evolving aggregation behaviors for swarm robotic systems: a systematic case study. In: Proceedings of the 2005 swarm intelligence symposium. IEEE Press, Piscataway, pp 333–340Google Scholar
  3. Barclay M, Delores K (1955) Results of treatment of enuresis by a conditioned response method. J Consult Psychol 19(1):71–73CrossRefGoogle Scholar
  4. Beni G (2005) From swarm intelligence to swarm robotics. In: Swarm robotics, volume 3342 of Lecture Notes in Computer Science, Springer, Berlin, pp 1–9Google Scholar
  5. Cardoso HL, Leitão P, Oliveira E (2006) An approach to inter-organizational workflow management in an electronic institution. In: 11th IFAC symposium on information control problemsGoogle Scholar
  6. Dasgupta P (2012) Multi-agent coordination techniques for multi-robot task allocation and multi-robot area coverage. In: IEEE international conference collaboration technologies and systems, pp 75–85Google Scholar
  7. Dorigo M (2005) SWARM-BOT: an experiment in swarm robotics. In: IEEE swarm intelligence symposium, pp 192–200Google Scholar
  8. Dorigo M, Floreano D, Gambardella LM (2013) Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Trans Robotics Autom Mag 20:60–71CrossRefGoogle Scholar
  9. Durrant-Whyte H, Roy N, Abbeel P (2012) TERMES: an autonomous robotic system for three-dimensional collective construction. MIT Press, Cambridge, MA, pp 257–264Google Scholar
  10. Erskine A, Herrmann JM (2015) CriPS: critical particle swarm optimisation. In: 2015 Proceedings of the European conference on artificial life 2015 (ECAL 2015), vol 13, pp 207–214Google Scholar
  11. Ferber J, Gutknecht O (1998) A meta-model for the analysis and design of organizations in multi-agent systems. In: 1998 IEEE international conference on multi agent systems. IEEE Press, pp 128–135Google Scholar
  12. Gutowitz H (1991) Cellular automata—theory and experiment. MIT Press, Cambridge, MAzbMATHGoogle Scholar
  13. Higgins F, Tomlinson A, Martin KM (2009) Survey on security challenges for swarm robotics. In: 2009 IEEE international conference on autonomic and autonomous systems. IEEE Press, Valencia, pp 307–312Google Scholar
  14. Jeyaraman S, Tsourdos A, Żbikowski R, White B (2006) Kripke modelling approaches of a multiple robots system with minimalist communication: a formal approach of choice. Int J Syst Sci 37(6):339–349CrossRefzbMATHGoogle Scholar
  15. Kernbach S (2008) Structural self-organization in multi-agents and multi-robotic systems. Logos Verlag Berlin GmbH, BerlinGoogle Scholar
  16. Kim Y, Oral S, Shipman GM et al (2011) Harmonia: a globally coordinated garbage collector for arrays of solid-state drives. In: 27th IEEE symposium on mass storage systems and technologies. IEEE Press, Denver, pp 1–12Google Scholar
  17. Kim H, Cheong J, Lee S, Kim J (2012) Task-oriented synchronous error monitoring framework in robotic manufacturing process. In: 2012 IEEE international conference on automation science and engineering. IEEE Press, Seoul, pp 48–490Google Scholar
  18. Liang R, Zhou J (1997) Probability method applied to dynamic pile-driving control. J Geotech Geoenviron Eng 2(137):137–144CrossRefGoogle Scholar
  19. Loizou SG, Kyriakopoulos KJ (2005) Automated planning of motion tasks for multi-robot systems. In: 44th IEEE international conference on decision and control. IEEE Press, pp 78–83Google Scholar
  20. Loizou SG, Kyriakopoulos KJ (2005) Automated planning of motion tasks for multi-robot systems. In: IEEE international conference decision and control, pp 78–83Google Scholar
  21. Lueth TC, Laengle T (1994) Task description, decomposition, and allocation in a distributed autonomous multi-agent robot system. In: IEEE international conference intelligent robots and systems, pp 1516–1523Google Scholar
  22. Mataroc M, Ostergaard EH (2003) Multi-robot task allocation in uncertain environment. Auton Robots 14(2):255–263CrossRefGoogle Scholar
  23. Meinhardt H (1982) Models of biological pattern formation. Academic Press, LondonGoogle Scholar
  24. Mohan Y, Ponnambalam SG (2009) An extensive review of research in swarm robotics. In: IEEE world congress on nature & biologically inspired computing. IEEE Press, Coimbatore, pp 140–145Google Scholar
  25. Murray JD (1989) Mathematical biology. Springer, New YorkCrossRefzbMATHGoogle Scholar
  26. Nagpal R (1999) Organizing a global coordinate system from local information on an amorphous computer. MIT, BostonGoogle Scholar
  27. Ouyang Q, Swinney HL (1991) Transition from a uniform state to hexagonal and striped Turing patterns. Nature 352:610–612CrossRefGoogle Scholar
  28. Parker LE (1988) ALLIANCE: an architecture for fault tolerant multirobot cooperation. IEEE Trans Robot Autom 14(2):220–240CrossRefGoogle Scholar
  29. Pfeifer R, Lungarella M, Iida F (2007) Self-organization, embodiment, and biologically inspired robotics. Science 318(5853):1088–1093CrossRefGoogle Scholar
  30. Purnamadjaja AH, Russell RA (2006) Robotic pheromones: using temperature modulation in tin oxide gas sensor to differentiate swarm’s behaviours. In: IEEE International conferences control, automation, robotics and vision, pp 1–6Google Scholar
  31. Rubenstein M, Ahler C, Nagpal R (2012) Kilobot: a low cost scalable robot system for collective behaviors. In: IEEE international conferences robotics and automation, pp 3293–3298Google Scholar
  32. Toffoli T (1998) Cellular automata. In: The handbook of brain theory and neural networks, pp 166–169Google Scholar
  33. Turing AM (1952) The chemical basis of morphogenesis. Philos Trans R Soc Lond B 237(641):37–72CrossRefMathSciNetGoogle Scholar
  34. Tzes A, Kim S, McShane WR (1996) Applications of Petri networks to transportation network modeling. IEEE Trans Veh Technol 45(2):391–400CrossRefGoogle Scholar
  35. Yang BH, Asada HH (1997) Robot impedance generation from logic task description through progressive learning. In: IEEE international conference robotics and automation, pp 3403–3408Google Scholar
  36. Zhang YZ, Xue SD, Zeng JC (2014) Dynamic task allocation with closed-loop adjusting in swarm robotic search for multiple targets. Robot 36(1):57–67Google Scholar
  37. Zlot R, Dias MB (2002) Multi-robot exploration controlled by a market economy. In: Proceedings of IEEE international conference on robotics and automation, pp 3016–3023Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Yuquan Leng
    • 1
    • 2
    Email author
  • Cen Yu
    • 3
  • Wei Zhang
    • 1
  • Yang Zhang
    • 1
  • Xu He
    • 1
  • Weijia Zhou
    • 1
  1. 1.State Key Laboratory of RoboticsShenyang Institute of AutomationShenyangChina
  2. 2.University of Chinese Academy of ScienceBeijingChina
  3. 3.Anhui Xinhe Defense Equipment Technology Corporation LimitedHefeiChina

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