Shape reconstruction of a soft actuator based on Bezier curves using soft strain sensors

Official URL: https://dx.doi.org/10.1109/RoboSoft60065.2024.10521994

Shape reconstruction of deformable actuators by means of intrinsic sensors is highly crucial for an accurate control of soft robots. Given the unstructured nature of the intrinsic sensors employed, a common approach is to leverage recurrent neural networks to estimate the position of a number of points along the main axis of the actuator. The shape is, then, reconstructed by fitting a kinematic model to the estimated points on the actuator. This paper proposes an alternative method in which a parameterized curve is chosen to model the deformation of the actuator. Feedback from the intrinsic sensors are utilized to directly estimate the model parameters by means of a neural network. The performance of the proposed approach was tested on a setup with an array of soft strain sensors attached to a tendon-driven actuator. The experiments were configured so that the actuator interacts with an external environment that applies a variable load on the deforming body, inducing a significant variation in the curvature of the backbone of the actuator. The proposed approach achieved an average estimation error of 1.16 mm in the tip position (0.6% of the actuator length) and 1.2 degrees in the tip orientation (less than 1% of the maximum tip orientation).