A Tool for Knowledge-Oriented Physics-Based Motion Planning and Simulation

  • Muhayyuddin GillaniEmail author
  • Aliakbar Akbari
  • Jan Rosell
  • Wajahat Mahmood Qazi
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


The recent advancements in robotic systems set new challenges for robotic simulation software, particularly for planning. It requires the realistic behavior of the robots and the objects in the simulation environment by incorporating their dynamics. Furthermore, it requires the capability of reasoning about the action effects. To cope with these challenges, this study proposes an open-source simulation tool for knowledge-oriented physics-based motion planning by extending The Kautham Project, a C++-based open-source simulation tool for motion planning. The proposed simulation tool provides a flexible way to incorporate the physics, knowledge, and reasoning in planning process. Moreover, it provides ROS-based interface to handle the manipulation actions (such as push/pull) and an easy way to communicate with the real robots.



The work of the authors was partially supported by the Spanish Government through the projects DPI2013-40882-P and DPI2016-80077-R.


  1. 1.
    Akbari, A., Gillani, M., & Rosell, J. (2015). Task and motion planning using physics-based reasoning. In IEEE 20th International Conference on Emerging Technologies Factory Automation (ETFA) (pp. 1–7).Google Scholar
  2. 2.
    Akbari, A., Gillani, M., & Rosell, J. (2016). Task planning using physics-based heuristics on manipulation actions. In IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1–8).Google Scholar
  3. 3.
    Andre, G. (2011). A software architecture for robot control and its application to social robotics. In Proceedings of the IEEE International Conference on Robotics and Automation: Workshop on Open Source Software in Robotics.Google Scholar
  4. 4.
    Antoniou, G., & van Harmelen, F. (2003). Web ontology language: OWL. In S. Staab & R. Studer (Eds.), Handbook on ontologies in information systems (pp. 67–92). Berlin: Springer.Google Scholar
  5. 5.
    Barraquand, J., Langlois, B., & Latombe, J. C. (1992). Numerical potential field techniques for robot path planning. IEEE Transactions on Systems, Man, and Cybernetics, 22(2), 224–241.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Bruyninckx, H., Soetens, P., & Koninckx, B. (2003). The real-time motion control core of the OROCOS project. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 2766–2771).Google Scholar
  7. 7.
    Connolly, C. I., & Grupen, R. A. (1993). The applications of harmonic functions to robotics. Journal of Robotic Systems, 10(7), 931–946.CrossRefGoogle Scholar
  8. 8.
    Diankov, R. (August 2010). Automated Construction of Robotic Manipulation Programs. PhD thesis, Carnegie Mellon University, Robotics Institute.Google Scholar
  9. 9.
    Gillani, M., Akbari, A., & Rosell, J. (2015). Ontological physics-based motion planning for manipulation. In Proceedings of the IEEE International Conference on Emerging Technologies Factory Automation (ETFA) (pp. 1–7).Google Scholar
  10. 10.
    Gillani, M., Akbari, A., & Rosell, J. (2016). Physics-based motion planning: Evaluation criteria and benchmarking. In Robot 2015: Second Iberian Robotics Conference (pp. 43–55). Cham: Springer.CrossRefGoogle Scholar
  11. 11.
    Gottschalk, S., Lin, M., Manocha, D., & Larsen, E. (1999). PQP–the proximity query package. Scholar
  12. 12.
    Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.CrossRefGoogle Scholar
  13. 13.
    LaValle, S. M., & Kuffner, J. J. (2001). Randomized kinodynamic planning. The International Journal of Robotics Research, 20(5), 378–400.CrossRefGoogle Scholar
  14. 14.
    Miller, A. T., & Allen, P. K. (2004). Graspit! A versatile simulator for robotic grasping. IEEE Robotics & Automation Magazine, 11(4), 110–122.CrossRefGoogle Scholar
  15. 15.
    Pan, J., Chitta, S., & Manocha, D. (2012). FCL: A general purpose library for collision and proximity queries. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 3859–3866). Piscataway: IEEE.Google Scholar
  16. 16.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., et al. (2009). ROS: An open-source robot operating system. In ICRA Workshop on Open Source Software (Vol. 3, pp. 5).Google Scholar
  17. 17.
    Rosell, J., Pérez, A., Aliakbar, A., Gillani, M., Palomo, L., García, N., et al. (2014). The Kautham project: A teaching and research tool for robot motion planning. In IEEE International Conference on Emerging Technologies Factory Automation (ETFA) (pp. 1–8).Google Scholar
  18. 18.
    Sucan, I., & Kavraki, L. E. (2012). A sampling-based tree planner for systems with complex dynamics. IEEE Transactions on Robotics, 28(1), 116–131.CrossRefGoogle Scholar
  19. 19.
    Suçan, I. A., & Chitta, S. (2013). MoveIt! Scholar
  20. 20.
    Şucan, I. A., Moll, M., & Kavraki, L. E. (2012). The open motion planning library. IEEE Robotics & Automation Magazine, 19, 72–82. Scholar
  21. 21.
    Vahrenkamp, N., Kröhnert, M., Ulbrich, S., Asfour, T., Metta, G., Dillmann, R., et al. (2013). Simox: A robotics toolbox for simulation, motion and grasp planning. In Intelligent autonomous systems (Vol. 12, pp. 585–594). Berlin: Springer.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Muhayyuddin Gillani
    • 1
    Email author
  • Aliakbar Akbari
    • 1
  • Jan Rosell
    • 1
  • Wajahat Mahmood Qazi
    • 2
  1. 1.Institute of Industrial and Control EngineeringUniversitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.Department of Computer ScienceCOMSATS University IslamabadLahore CampusPakistan

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