Tactile robot-based assembly line adaptation of new products is still limited by the manual redesign, manufacturing, and exchange of the end-effector setup since the gripper fingers must often be adapted to the geometry of the product components to ensure a successful assembly process. In this article we present an automatic finger design, production, and evaluation pipeline, developed to improve this adaptation process. Two different form-closure–based design principles have been implemented to automatically generate the fingertip geometry: a projected surface representation-based approach as well as a Bézier surface fitting strategy. The resulting fingertips are printed via an automatic production unit and experimentally evaluated based on pick and insertion tasks for three different manipulation objects. To illustrate the potential usage of the introduced design methods for machine learning-based fingertip design approaches, we set up the training and testing process for a neural network-based design method. The proposed automatic fingertip design, production, and application framework represent a step further toward small-batch–size production, since the assembly adaptation effort, flexibility, and scalability are significantly improved.
