Abstract
Grasping an object in unstructured and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exits. High-level knowledge and reasoning processes, as well as the allowing of interaction between objects, can enhance the planning efficiency in such environments. In this direction, this study proposes a knowledge-oriented physics-based motion planning approach for a hand-arm system that uses a high-level knowledge-based reasoning to partition the workspace into regions to both guide the planner and reason about the result of the dynamical interactions between rigid bodies. The proposed planner is a kinodynamic RRT that uses a region-biased state sampling strategy and a smart validity checker that takes into account uncertainty in the pose of the objects. Complex dynamical interactions along with possible physics-based constraints such as friction and gravity are handled by a physics engine that is used as the RRT state propagator. The proposal is validated for different scenarios in simulation and in a real environment using a 7-degree-of-freedom KUKA Lightweight robot equipped with a two-finger gripper. The results show a significant improvement in the success rate of the execution of the computed plan in the presence of object pose uncertainty.
J. Rosell—This work was partially supported by the Spanish Government through the projects DPI2013-40882-P and DPI2016-80077-R.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bicchi, A., Kumar, V.: Robotic grasping and contact: a review. In: IEEE International Conference on Robotics and Automation (ICRA) (2000)
Suárez, R., Roa, M., Cornellá, J.: Grasp quality measures. Technical University of Catalonia, Technical report (2006)
Siciliano, B., Khatib, O.: Springer Handbook of Robotics. Springer Science & Business Media, Heidelberg (2008)
Kavraki, L.E., Švestka, P., Latombe, J.-C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996)
LaValle, S.M.: Rapidly-exploring random trees: a new tool for path planning (1998)
Dogar, M., Srinivasa, S.: Push-grasping with dexterous hands: mechanics and a method. In: IEEE International Conference on Intelligent Robots and Systems (IROS) (2010)
Stilman, M., Kuffner, J.J.: Navigation among movable obstacles: real-time reasoning in complex environments. Int. J. Humanoid Rob. 2, 479–503 (2005)
Dogar, M., Srinivasa, S.: A framework for push-grasping in clutter. In: Robotics: Science and Systems (RSS), July 2011
Rodríguez, C., Montaño, A., Suárez, R.: Planning manipulation movements of a dual-arm system considering obstacle removing. Robot. Auton. Syst. 62(12), 1816–1826 (2014)
Dogar, M., Hsiao, K., Ciocarlie, M., Srinivasa, S.: Physics-based grasp planning through clutter. In: Robotics: Science and Systems (RSS) (2012)
Kitaev, N., Mordatch, I., Patil, S., Abbeel, P.: Physics-based trajectory optimization for grasping in cluttered environments. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3102–3109 (2015)
Erwin, C.: Bullet physics library (2013). http://bulletphysics.org
Luders, B., Kothari, M., How, J.: Chance constrained RRT for probabilistic robustness to environmental uncertainty. In: AIAA Guidance, Navigation, and Control Conference, p. 8160 (2010)
Bry, A., Roy, N.: Rapidly-exploring random belief trees for motion planning under uncertainty. In: IEEE International Conference on Robotics and Automation, pp. 723–730 (2011)
Pilania, V., Gupta, K.: Localization aware sampling and connection strategies for incremental motion planning under uncertainty. Auton. Robots 41(1), 111–132 (2017)
Du Toit, N.E., Burdick, J.W.: Robot motion planning in dynamic, uncertain environments. IEEE Trans. Rob. 28(1), 101–115 (2012)
Melchior, N.A., Simmons, R.: Particle RRT for path planning with uncertainty. In: IEEE International Conference on Robotics and Automation, pp. 1617–1624 (2007)
Muhayyuddin, Akbari, A., Rosell, J.: Ontological physics-based motion planning for manipulation. In: IEEE International Conference on Emerging Technologies Factory Automation (ETFA), pp. 1–7 (2015)
Antoniou, G., van Harmelen, F.: Web ontology language: OWL. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies in Information Systems. Springer, pp. 67–92 (2004)
LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. Int. J. Robot. Res. 20(5), 378–400 (2001)
Şucan, I.A., Moll, M., Kavraki, L.E.: The open motion planning library. IEEE Robot. Autom. Mag. 19(4), 72–82 (2012)
Rosell, J., Pérez, A., Aliakbar, A., Muhayyuddin, Palomo, L., García, N.: The kautham project: a teaching and research tool for robot motion planning. In: IEEE International Conference on Emerging Technologies and Factory Automation (2014)
Tenorth, M., Beetz, M.: Knowrob knowledge processing for autonomous personal robots, pp. 4261–4266 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Muhayyuddin, Akbari, A., Rosell, J. (2018). Knowledge-Oriented Physics-Based Motion Planning for Grasping Under Uncertainty. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-70833-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-70833-1_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70832-4
Online ISBN: 978-3-319-70833-1
eBook Packages: EngineeringEngineering (R0)