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Shape sensing in continuum robots is a crucial aspect of their operation as it enables the robots to accurately perceive and respond to their environment. However, traditional sensors such as encoders or potentiometers are not well suited for these types of robots as they cannot conform to the robot’s shape and are prone to damage. In this research, we investigate the problem of shape sensing in continuum robots using a new approach of a soft e-textile resistive sensor with multiple stacked layers. The e-textile, which can conform to the shape of the continuum robot body, employs a resistive material that changes its resistance in response to any deflection in the continuum robot. The characterization of the sensor, including hysteresis analysis and time response, is first presented. This is followed by an examination of the application of this e-textile sensor in estimating the curvature of a continuum robot using a feed-forward artificial neural network (ANN) to contribute to developing more robust and efficient shape sensing techniques for continuum robots.
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DOI: 10.23919/sice59929.2023.10354216
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