Multilayer-perceptron-based Slip Detection Algorithm Using Normal Force Sensor Arrays

Abstract

Slip detection is an essential technology for robotic grippers to autonomously grasp unknown objects and can be achieved using a tactile sensor. In this paper, we propose a high-performance multilayer-perceptron-based slip detection algorithm that utilizes only normal force data obtained by frequency selective surface(FSS) sensor arrays. This is achieved in three stages in this study. First, slip and no-slip training data are aggregated such that the data closely resemble those of the real world. Second, the most suitable means of preprocessing the raw sensor output is identified. Third, the classification method with the highest performance is chosen on the basis of a performance comparison among various classification techniques. The online performance of the algorithm is evaluated by conducting two tasks: a simple pick and place task and a task of maintaining a stable grasp of an object whose weight is changing.