Abstract
The paper presents an implementation of knowledge representation including the capabilities of the system, based on ontologies for a Visually Guided Bin Picking Task. The ontology based approach was used to define the work environment, the robot, the machine vision system, and the capabilities that are needed to be performed by the robotic system, to perform the bin-picking task. The work proposes a novel application framework that is able to locate the object to pick from the bin and place it in a cell from a kit. For that, the framework, delivers the task implementation (PDDL) files that should be executed by the robot. The method used to detect the objects is based on Chamfer Match (CM) and Oriented Chamfer Match (OCM) which take advantage of the image edge map. To complete the pose estimation problem the robot manipulator is equipped with a laser range finder that can measure the object height. The robotic system was validated experimentally with simulation. using the V-REP environment interfacing with ROS, where the knowledge representation and reasoning framework is implemented. The system showed its capability to correctly pick and place a specific object. Moreover, the ontology based approach was very useful to define the task, the actions to be performed by the robot, based on its capabilities.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: ECCV, pp. 404–417 (2006)
Lowe, D.G.: Distinctive image features from scale-invariant key-points. IJCV 60, 91–110 (2004)
Rapoza, K.: India: China’s New Low-Cost Labor Hub? Forbes, September 2014
Barrow, H.G.: Parametric correspondence and chamfer matching: two new techniques for image matching. SRI Int. (1977)
Shotton, J.: Multiscale categorical object recognition using contour fragments. IEEE Trans. PAMI 30(7), 1270–1281 (2008)
Prestes, E., Carbonera, J.L., Fiorini, S.R., et al.: Towards a core ontology for robotics and automation. Robot. Auton. Syst. 61(11) (2013). https://doi.org/10.1016/j.robot.2013.04.005
Farinha, R., Gonćalves, P.J.S.: Knowledge based robotic system, towards ontology driven pick and place taks. Rom. Rev. Precis. Mech. Opt. Mechatron. 49, 152–157 (2016). https://doi.org/10.17683/rrpmom.issue.49
Balakirsky, S., Schlenoff, C., et al.: Towards a robot task ontology standard. In: Proceedings of the 12th Manufacturing Science and Engineering Conference. Los Angeles, USA (2017)
IEEE Std. 1872-2015: IEEE Standard Ontologies for Robotics and Automation (2015). https://doi.org/10.1109/IEEESTD.2015.7084073
Pease, A., Niles, I., Li, J.: The suggested upper merged ontology: a large ontology for the semantic web and its applications. Working Notes of the AAAI 2002 Workshop on Ontologies and the Semantic Web, vol. 28 (2002)
Rohmer, E., Singh, S.P.N., Freese, M.: V-REP: a versatile and scalable robot simulation framework. In: 2013 IEEE IROS, 3–7 November 2013
Rovida, F., Crosby, M., Holz, D., Polydoros, A., Großmann, B., Petrick, R., Krüger, V.: SkiROS - A Skill-Based Robot Control Platform on Top of ROS. Springer Book on Robot Operating System, vol. 2. Springer, Cham (2017)
McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., Wilkins, D.: PDDL—the planning domain definition language. Technical report CVC TR98003/DCS TR1165. Yale Center for Computational Vision and Control, New Haven, CT (1998)
Olszewska, J., Carbonera, J.L., Olivares-Alarcos, A., et al.: Ontology for autonomous robotics. In: 26th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), August 2017
Acknowledgments
This work was partially supported by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2019. This work was partially supported by project 0043- EUROAGE-4-E (POCTEP Programa Interreg V-A Spain-Portugal).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gonçalves, P.J.S., Caldas Pinto, J.R., Torres, F. (2020). Knowledge and Capabilities Representation for Visually Guided Robotic Bin Picking. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-35990-4_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-35990-4_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35989-8
Online ISBN: 978-3-030-35990-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)