Beschreibung
The adaptability and inherent safety of soft material robotic systems offer great potential for applications in which their rigid counter parts reach their limits in terms of flexibility and safety. The soft materials used in these systems allow for a safe interaction between humans and robots. Despite advances in the development of soft robots in the recent years, for them to step into application, more research needs to be conducted in the field of accurate modeling and control. For model-based design, path planning, and control computationally efficient models need to be developed that are able to capture the often highly nonlinear deformation behavior of soft actuators. Our previous research showed that artificial neural networks (ANN) are a powerful tool for representig an actuator’s nonlinear kinematics, while at the same time they are computationally efficient. In this article, we propose a transfer learning scheme for minimizing the effort of generating realworld data for neural network training.We showed that the generation of 50 real-world data pairs is sufficient to train an ANN that has a mean accuracy of less than 0.6% with respect to initial actuator length. The resulting ANN is applicable to open and closed loop kinematic control.
