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

Abstract

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.

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Notes

  1. 1.

    http://www.robocup-logistics.org.

  2. 2.

    http://www.robocupatwork.org/.

  3. 3.

    RoCKIn and RockEU2 also have domestic service robot branches, which are not further detailed for the sake of brevity.

  4. 4.

    https://developers.google.com/protocol-buffers/.

  5. 5.

    http://wiki.robocup.org/wiki/Middle_Size_League.

  6. 6.

    http://www.tzi.de/spl/.

  7. 7.

    http://wiki.robocup.org/wiki/Small_Size_League.

  8. 8.

    Technical Challenges in RoboCup are tasks in addition to the main task used to foster development of new capabilities in a league.

  9. 9.

    https://github.com/dzhu/ssl-autoref.

  10. 10.

    https://github.com/robotics-erlangen/autoref.

  11. 11.

    http://gitlab.tigers-mannheim.de/open-source/AutoReferee.

  12. 12.

    The source of the RoboCup Industrial Referee Box is available at https://github.com/robocup-industrial/rci-refbox.

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Correspondence to Tim Niemueller.

Additional information

T. Niemueller was supported by the German National Science Foundation (DFG) research unit FOR 1513 on Hybrid Reasoning for Intelligent Systems (http://www.hybrid-reasoning.org).

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Niemueller, T., Zug, S., Schneider, S. et al. Knowledge-Based Instrumentation and Control for Competitive Industry-Inspired Robotic Domains. Künstl Intell 30, 289–299 (2016). https://doi.org/10.1007/s13218-016-0438-8

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Keywords

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