Color-Based Proprioception of Soft Actuators Interacting with Objects

Color-Based Proprioception of Soft Actuators Interacting with Objects

IEEE/ASME Transactions on Mechatronics, 2019

Rob B.N. Scharff, Rens M. Doornbusch, Zjenja Doubrovski, Jun Wu, Jo M.P. Geraedts, Charlie C. L. Wang
Department of Design Engineering, TU Delft
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong


Actuators using soft materials feature a large number of degrees of freedom. This tremendous flexibility allows a soft actuator to passively adapt its shape to the objects under interaction. In this paper we propose a novel proprioception method for soft actuators during real-time interaction with priorly unknown objects. Firstly, we design a color-based sensing structure that instantly translates the inflation of a bellow into changes in color, which are subsequently detected by a miniaturized color sensor. The color sensor is small and thus multiple of them can be integrated into soft pneumatic actuators to reflect local deformations. Secondly, we make use of a Feed-forward Neural Network (FNN) to reconstruct a multivariate global shape deformation from local color signals. Our results demonstrate that deformations of the actuator during interaction, including the sigmoid-like shapes, can be accurately reconstructed. The accurate shape sensing represents a significant step towards closed-loop control of soft robots in unstructured environments.




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title = "Color-Based Proprioception of Soft Actuators Interacting with Objects",
journal = "IEEE/ASME Transactions on Mechatronics",
year = "2019",
volume = "24",
number = "5",
pages = "1964-1973",
doi = "",
author = "Rob B.N. Scharff and Rens M. Doornbusch and Zjenja Doubrovski and Jun Wu and J.M.P. Geraedts and Charlie C. L. Wang"