A Movement Control System Based on Qualitative Reasoning

  • Przemysław Wałęga
  • Emilio Muñoz-Velasco
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 368)


We present \(QR_M\), a movement control system based on Qualitative Reasoning. The representation of relative movement of an object with respect to another is done by using different components given by qualitative values, such as velocity, orientation, latitude, longitude, etc. These qualitative values are obtained from quantitative data by means of a nonlinear system with hysteresis. We also use composition tables for new data inferring and a table-based control system. The system is implemented in Robotic Operating System ROS and tested with computer simulator STAGE. We show how \(QR_M\) works in real applications on the basis of two experiments.


Qualitative reasoning Qualitative movement Movement Control Collision avoidance 



The work presented in this paper is partially supported by the Polish National Science Centre grant 2011/02/A/HS1/00395 and by the Spanish Project TIN12-39353-C04-01.


  1. 1.
    Andrea C, Boris G, Fabien S, François C (2013) Avoiding moving obstacles during visual navigation. In: 2013 IEEE international conference on robotics and automation (ICRA), pp 3069–3074. IEEEGoogle Scholar
  2. 2.
    Cohn AG, Renz J (2008) Qualitative spatial representation and reasoning. Handb Knowl Represent 3:551–596Google Scholar
  3. 3.
    Delafontaine M, Bogaert P, Cohn AG, Witlox F, De Maeyer P, Van de Weghe N (2011) Inferring additional knowledge from qtcn relations. Inf Sci 181(9):1573–1590CrossRefGoogle Scholar
  4. 4.
    Escrig MT, Toledo F (2002) Qualitative velocity. In: Topics in artificial intelligence, pp 29–39. SpringerGoogle Scholar
  5. 5.
    Forbus KD (2008) Qualitative modeling. Handb Knowl Represent 3:361–393Google Scholar
  6. 6.
    Frank AU (1992) Qualitative spatial reasoning about distances and directions in geographic space. J Vis Lang Comput 3(4):343–371. ElsevierGoogle Scholar
  7. 7.
    Gedig M, Stiemer S (2003) Qualitative and semi-quantitative reasoning techniques for engineering projects at conceptual stage. Electron J Struct Eng 3:67–88Google Scholar
  8. 8.
    Gerkey B, Vaughan RT, Howard A (2003) The player/stage project: tools for multi-robot and distributed sensor systems. In: Proceedings of the 11th international conference on advanced robotics, vol 1, pp 317–323Google Scholar
  9. 9.
    Khalil HK, Grizzle J (2002) Nonlinear systems, volume 3. Prentice Hall Upper Saddle RiverGoogle Scholar
  10. 10.
    Liu H, Brown DJ, Coghill GM (2008) Fuzzy qualitative robot kinematics. IEEE Trans Fuzzy Syst 16(3):808–822CrossRefGoogle Scholar
  11. 11.
    Liu W, Li S, Renz J (2009) Combining RCC-8 with qualitative direction calculi: algorithms and complexity. In; IJCAI, pp 854–859Google Scholar
  12. 12.
    Muñoz-Velasco E, Burrieza A, Ojeda-Aciego M (2014) A logic framework for reasoning with movement based on fuzzy qualitative representation. Fuzzy Sets Syst 242:114–131CrossRefGoogle Scholar
  13. 13.
    Quigley M, Conley K, Gerkey B, Faust J, Foote T, Leibs J, Wheeler R, Ng AY (2009) ROS: an open-source robot operating system. In: ICRA workshop on open source software, vol 3, p 5Google Scholar
  14. 14.
    Van de Weghe N, Kuijpers B, Bogaert P, De Maeyer P (2005) A qualitative trajectory calculus and the composition of its relations. In: GeoSpatial Semantics, pp 60–76. SpringerGoogle Scholar
  15. 15.
    Zvi S, Frederic L, Sepanta S (2001) Motion planning in dynamic environments: Obstacles moving along arbitrary trajectories. In: 2001 IEEE international conference on robotics and automation (ICRA), pp 3716–3721. IEEEGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Przemysław Wałęga
    • 1
  • Emilio Muñoz-Velasco
    • 2
  1. 1.Institute of PhilosophyUniversity of WarsawWarsawPoland
  2. 2.Department of Applied MathematicsUniversity of MalagaMálagaSpain

Personalised recommendations