A Haptic Exploration and Surface Classification of Objects with Four Typical Surface Properties

To effectively interact with the physical world, an intelligent robot is required to have the ability to obtain the detailed features of an unknown object. Visual devices are commonly used to detect the global geometry of an object; however, detailed information such as surface properties cannot be identified using these devices. The current study proposes an efficient haptic exploration method to recognize the physical properties of surfaces using an intelligent fingertip. Our surface-following algorithm utilizes the normal force vector and the tangential force vector at the contact point between the fingertip and the target object to predict the moving direction and implement the surface exploration. And a correction index
$K$
is introduced to adjust the sliding velocity. The algorithm is proved to be robust and high performance both in simulation and lab experiments. Finally, we propose and explore a haptic prediction neural network, which enables our robot to have an accurate feel through physical interaction. In the classification experiment of four types of objects with different surface properties, the average accuracy of the proposed model is 90.2%.