KI - Künstliche Intelligenz

, Volume 30, Issue 3–4, pp 289–299 | Cite as

Knowledge-Based Instrumentation and Control for Competitive Industry-Inspired Robotic Domains

  • Tim NiemuellerEmail author
  • Sebastian Zug
  • Sven Schneider
  • Ulrich Karras


Autonomy is an increasing trend in manufacturing industries. Several industry-inspired robotic competitions have been established in recent years to provide testbeds of comprehensible size. In this paper, we describe a knowledge-based instrumentation and control framework used in several of these competitions. It is implemented using a rule-based production system and creates the task goals for autonomous mobile robots. It controls the environment’s agency using sensor data from processing stations and instructs proper reactions. The monitoring and collection of various data allows for an effective instrumentation of the competitions for evaluation purposes. The goal is to achieve automated runs with no or as little human intervention as possible which would allow for more and longer lasting runs. It provides a general framework adaptable to suit many scenarios and is an interesting test case for knowledge-based systems in an industry-inspired setting.


Mobile robotics Autonomy Rule-based production systems Smart factory Factory instrumentation RoboCup industrial Benchmarking Industry 4.0 


  1. 1.
    Amigoni F, Bastianelli E, Berghofer J, Bonarini A, Fontana G, Hochgeschwender N, Iocchi L, Kraetzschmar G, Lima P, Matteucci M, Miraldo P, Nardi D, Schiaffonati V (2015) Competitions for benchmarking: task and functionality scoring complete performance assessment. IEEE Robot Autom Mag 22:53–61CrossRefGoogle Scholar
  2. 2.
    Amigoni F, Bonarini A, Fontana G., Matteucci M, Schiaffonati V.: Benchmarking through competitions. In: European robotics forum—workshop on robot competitions: benchmarking, technology transfer, and education (2013)Google Scholar
  3. 3.
    Barbosa M, Bernardino A, Figueira D, Gaspar J, Goncalves N, Lima P, Moreno P, Pahliani, A, Santos-Victor J, Spaan M, Sequeira J.: Isrobotnet: a testbed for sensor and robot network systems. In: IEEE/RSJ international conference on intelligent robots and systems (IROS) (2009)Google Scholar
  4. 4.
    Brachman RJ, Levesque HJ.: Knowledge representation and reasoning. Elsevier, San Francisco (2004)Google Scholar
  5. 5.
    Bray T (2014) The JavaScript Object Notation (JSON) Data Interchange Format. RFC 7159, Internet Engineering Task ForceGoogle Scholar
  6. 6.
    Calli B, Walsman A, Singh A, Srinivasa S, Abbeel P, Dollar AM (2015) Benchmarking in manipulation research. IEEE Robot Autom Mag 22:36–51CrossRefGoogle Scholar
  7. 7.
    Dwiputra R, Berghofer J, Ahmad A, Awaad I, Amigoni F, Bischoff R, Bonarini A, Fontana G, Hegger F, Hochgeschwender N, Locchi L, Kraetzschmar G, Lima PU, Matteucci M, Nardi D, Schiaffonati V, Schneider S (2014) The RoCKIn@Work Challenge. In: 45th international symposium on robotics (ISR)Google Scholar
  8. 8.
    Fontana G., Matteucci M, Sorrenti DG Rawseeds: building a benchmarking toolkit for autonomous robotics. In: Amigoni F, Schiaffonati V (eds) Methods and experimental techniques in computer engineering, SpringerBriefs in applied sciences and technology. Springer International Publishing, pp 55–68 (2014)Google Scholar
  9. 9.
    Forgy CL (1982) Rete: a fast algorithm for the many pattern/many object pattern match problem. Artif Intell 19(1)Google Scholar
  10. 10.
    Giarratano JC (2007) CLIPS reference manuals.
  11. 11.
    International Electrotechnical Commission (2013) Enterprise-control system integration—Part 1: models and terminologyGoogle Scholar
  12. 12.
    International Electrotechnical Commission (2015) International Organization for Standardization: ISO/IEC DIS 20922: Information technology—message queuing telemetry transport (MQTT) v3.1.1Google Scholar
  13. 13.
    Kagermann H, Wahlster W, Helbig J (2013) Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final Report, Platform Industrie 4.0Google Scholar
  14. 14.
    Kasper A, Xue Z, Dillmann R (2012) The kit object models database: an object model database for object recognition, localization and manipulation in service robotics. Int J Robot Res 31:927–934CrossRefGoogle Scholar
  15. 15.
    Kitano H, Asada M, Kuniyoshi Y, Noda I, Osawa E Robocup: the Robot World Cup Initiative. In: 1st Int. conference on autonomous agents (1997)Google Scholar
  16. 16.
    Moll M, Sucan IA, Kavraki LE (2015) Benchmarking motion planning algorithms. IEEE Robot Autom Mag 22:96–102CrossRefGoogle Scholar
  17. 17.
    Niemueller T, Ewert D, Reuter S, Ferrein A (2013) Carologistics RoboCup team: autonomous referee box and visualization tool for the logistics league sponsored by Festo. RoboCup Grant Report Poster, RWTH Aachen University and FH Aachen UoaS.
  18. 18.
    Niemueller T, Ewert D, Reuter S, Ferrein A, Jeschke S, Lakemeyer G (2013) RoboCup Logistics League Sponsored by Festo: a competitive factory automation testbed. In: RoboCup SymposiumGoogle Scholar
  19. 19.
    Niemueller T, Karpas E, Vaquero T, Timmons E (2016) Planning competition for logistics robots in simulation. In: WS on planning and robotics (PlanRob) at Int. Conf. on Aut. planning and scheduling (ICAPS)Google Scholar
  20. 20.
    Niemueller T, Lakemeyer G, Ferrein A (2013) Incremental task-level reasoning in a competitive factory automation scenario. In: AAAI spring symposium 2013—designing intelligent robots: reintegrating AIGoogle Scholar
  21. 21.
    Niemueller T, Lakemeyer G, Ferrein A (2015) The RoboCup logistics league as a benchmark for planning in robotics. In: WS on planning and robotics (PlanRob) at Int. Conf. on Aut. planning and scheduling (ICAPS)Google Scholar
  22. 22.
    Niemueller T, Lakemeyer G, Ferrein A, Reuter S, Ewert D, Jeschke S, Pensky D, Karras U () Proposal for advancements to the LLSF in 2014 and beyond. In: ICAR—1st workshop on developments in robocup leagues (2013)Google Scholar
  23. 23.
    Niemueller T, Lakemeyer G, Reuter S, Jeschke S, Ferrein A (2017) Cyber-physical systems—foundations, principles, and applications, chap. Benchmarking of Cyber-Physical Systems in Industrial Robotics—The RoboCup Logistics League as a CPS Benchmark Blueprint. Elsevier (to appear) Google Scholar
  24. 24.
    Niemueller T, Reuter S, Ferrein A, Jeschke S, Lakemeyer G (2015) Evaluation of the RoboCup logistics league and derived criteria for future competitions. In: RoboCup SymposiumGoogle Scholar
  25. 25.
    Pennisi A, Bloisi DD, Iocchi L, Nardi D (2013) Ground truth acquisition of humanoid soccer robot behaviour. In: RoboCup symposiumGoogle Scholar
  26. 26.
    Reinhart G, Krug S, Hüttner S, Mari Z, Riedelbauch F, Schlögel M (2010) Automatic configuration (plug&produce) of industrial ethernet networks. In: 9th IEEE/IAS international conference on Industry applications (INDUSCON), 2010, pp 1–6. doi: 10.1109/INDUSCON.2010.5739892
  27. 27.
    RoboCup SPL Technical Committee (2015) RoboCup SPL Technical Committee: RoboCup Standard Platform League (NAO) Rule Book 2015Google Scholar
  28. 28.
    Schneider S, Hegger F, Hochgeschwender N, Dwiputra R, Moriarty A, Berghofer J, Kraetzschmar G (2015) Design and development of a benchmarking testbed for the factory of the future. In: IEEE International conference on emerging technologies and factory automation (ETFA)Google Scholar
  29. 29.
    Sturm J, Engelhard N, Endres F, Burgard W, Cremers D (2012) A benchmark for the evaluation of RGB-D slam systems. In: IEEE/RSJ international conference on intelligent robots and systems (IROS)Google Scholar
  30. 30.
    Wang Z, Gu F, He Y, Han J, Wang Y () Design and implementation of multiple-rotorcraft-flying-robot testbed. In: IEEE international conference on robotics and biomimetics (ROBIO) (2011)Google Scholar
  31. 31.
    Wygant RM (1989) CLIPS: a powerful development and delivery expert system tool. Comput Ind Eng 17(1–4):546–549CrossRefGoogle Scholar
  32. 32.
    Zwilling F, Niemueller T, Lakemeyer G (2014) Simulation for the RoboCup logistics league with real-world environment agency and multi-level abstraction. In: RoboCup SymposiumGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Knowledge-Based Systems GroupRWTH Aachen UniversityAachenGermany
  2. 2.Otto-von-Guericke UniversityMagdeburgGermany
  3. 3.Bonn-Rhein-Sieg UoASSt. AugustinGermany
  4. 4.RoboCup Executive Committee

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