Abstract
The paper describes a computational solution with cloud implementing, extending robot-vision capabilities of real-time multiple articulated objects recognition for on-the-fly robot grasping. Articulated objects are recognized by matching the unknown object’s skeleton computed from the input image in a cloud virtual machine (VM) with a set of learned skeleton signatures. This High Performance Computing (HPC) process represents a powerful capability for qualitative shape matching because it unambiguously synthesizes and helps estimating the topology of the object and its shape. The skeleton-based matching process is performed as an application-driven robotic service in a private cloud, ten times faster than the robot controller is able to do it and nearly twice faster than two PC-type robot terminals for multiple parts moving on conveyor belts. The parameters of the virtualization process and experimental results which confirm the solution are presented.
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Acknowledgments
This work has been funded by University Politehnica of Bucharest through the project GSCR “Generarea Suprafeţelor Complexe în Robotică” in the Research Grants Program UPB-GEX, 2/26.09.2016, and by the IBM FA 2016 Research Project DTM: “Big Data, Analytics and Cloud for Digital Transformation on Manufacturing”.
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Anton, F.D., Borangiu, T., Anton, S., Raileanu, S. (2018). Cloud Robot Vision Services Extend High-Performance Computing Capabilities of Robot Systems. In: Ferraresi, C., Quaglia, G. (eds) Advances in Service and Industrial Robotics. RAAD 2017. Mechanisms and Machine Science, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-61276-8_35
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DOI: https://doi.org/10.1007/978-3-319-61276-8_35
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