Abhijit Boruah
ABSTRACT
The problem of knowing what to grasp and deciding how to grasp is an open issue for development of intelligent prosthetic hands. To emulate the potentialities of a human hand, knowledge of the grasping domain has to be accumulated and modelled in a machine interpretable format. In this paper, we have tried to comprehend and model a specific part of the knowledge (information) of a prosthetic hand-grasping domain into a reusable Web-Ontology-Language (OWL) format. This ontology build after basic analysis of hand object coordination, can be used for preserving, improving and sharing the captured knowledge. We begin with our description of the required knowledge of a geometrical concept formed during human grasping, to a point where it can be used to plan grasping based on the objects identified. Using tactile and kinesthetic information along with relevant domain concepts, we emphasized on the rationality of designing an ontology for reusability and sustainability of knowledge. We tried to lay down a visual model of the ontology, also called the Ontograph, which illuminates the existence and relationships among the various objects of the grasping domain. We have also checked the decisive capability of the ontology by reasoning it with Description Logic (DL) queries of data property values for individuals of geometric classes. The output of the queries provided us with individuals of the specific geometric pattern, which can be used to decide the type of grasp that could be implemented on objects.
- Lederman, S. and Klatzky, R. (1987). Hand movements: A window into haptic object recognition. Cognitive Psychology, 19(3), pp.342--368.Google Scholar
Cross Ref
- Lederman, S. and Klatzky, R. (1987). Hand movements: A window into haptic object recognition. Cognitive Psychology, 19(3), pp.342--368.Google ScholarCross Ref
- Lederman, S. and Klatzky, R. (1987). Hand movements: A window into haptic object recognition. Cognitive Psychology, 19(3), pp.342--368.Google ScholarCross Ref
- Boruah, A., Kakoty, N. and Ali, T. (2018). Object Recognition based on Surface Detection - A Review. Procedia Computer Science, 133, pp.63--74.Google ScholarCross Ref
- Tenorth, M. and Beetz, M. (2009). KNOWROB --- knowledge processing for autonomous personal robots. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.Google ScholarCross Ref
- ElKoura, G., & Singh, K. (2003, July). Handrix: animating the human hand. In Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation (pp. 110--119). Eurographics Association.Google Scholar
- Feix, T., Romero, J., Schmiedmayer, H. B., Dollar, A. M., & Kragic, D. (2016). The grasp taxonomy of human grasp types. IEEE Transactions on Human- Machine Systems, 46(1), 66--77.Google ScholarCross Ref
- Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology.Google Scholar
- Compton, M., Barnaghi, P., Bermudez, L., GarcíA-Castro, R., Corcho, O., Cox, S., ... & Huang, V. (2012). The SSN ontology of the W3C semantic sensor network incubator group. Web semantics: science, services and agents on the World Wide Web, 17, 25--32.Google Scholar
- Morita, N., Nogami, H., Higurashi, E., & Sawada, R. (2018). Grasping Force Control for a Robotic Hand by Slip Detection Using Developed Micro Laser Doppler Velocimeter. Sensors, 18(2), 326.Google ScholarCross Ref
- Heydarabad, S. M., Milella, F., Davis, S., & Nefiti-Meziani, S. (2017, May). High-performing adaptive grasp for a robotic gripper using super twisting sliding mode control. In Robotics and Automation (ICRA), 2017 IEEE International Conference on (pp. 5852--5857). IEEE.Google ScholarDigital Library
- Pangercic, D., Tenorth, M., Jain, D., & Beetz, M. (2010, October). Combining perception and knowledge processing for everyday manipulation. In IROS (pp. 1065--1071).Google Scholar
- Bekey, G. A., Iberall, T., Tomovic, R., & Liu, H. (1991). Knowledge-Based models of human and robot grasping. IFAC Proceedings Volumes, 24(3), 699--704.Google ScholarCross Ref
- Varadarajan, K., & Vincze, M. (2011, September). Ontological knowledge management framework for grasping and manipulation. In IROS Workshop: Knowledge Representation for Autonomous Robots.Google Scholar
- Albert, B., Zanni-Merk, C., de Beuvron, F. D. B., Maire, J. L., Pillet, M., Charrier, J., & Knecht, C. (2016, November). A Smart System for Haptic Quality Control. In Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 21--30). SCITEPRESS-Science and Technology Publications, Lda.Google ScholarDigital Library
- Zanni-Merk, C. (2015, November). KREM: A Generic Knowledge-based Framework for Problem Solving in Engineering. In Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 381--388). SCITEPRESS-Science and Technology Publications, Lda.Google ScholarDigital Library
- Myrgioti, E., Bassiliades, N., & Miliou, A. (2013). Bridging the HASM: An OWL ontology for modeling the information pathways in haptic interfaces software. Expert Systems with Applications, 40(4), 1358--1371.Google ScholarDigital Library
Index Terms
- Reasoning on Objects' Geometric Shapes for Prosthetic Hand Grasping
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