Abstract
This paper proposes a general approach to design automatic controls to manipulate elastic objects into desired shapes. The object’s geometric model is defined as the shape feature based on the specific task to globally describe the deformation. Raw visual feedback data is processed using classic regression methods to identify parameters of data-driven geometric models in real-time. Our proposed method is able to analytically compute a pose-shape Jacobian matrix based on implicit functions. This model is then used to derive a shape servoing controller. To validate the proposed method, we report a detailed experimental study with robotic manipulators deforming an elastic rod.
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Acknowledgements
This work is supported in part by the Research Grants Council (grants 14203917, G-PolyU507/18), in part by the Jiangsu Industrial Technology Research Institute Collaborative Research Program Scheme (grant ZG9V), in part by the Key-Area Research and Development Program of Guangdong Province 2020 (project 76), and in part by PolyU (grants YBYT, ZZHJ).
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Ma, W., Zhu, J., Navarro-Alarcon, D. (2022). Shape Control of Elastic Objects Based on Implicit Sensorimotor Models and Data-Driven Geometric Features. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham. https://doi.org/10.1007/978-3-030-95892-3_40
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DOI: https://doi.org/10.1007/978-3-030-95892-3_40
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