A Robust Incipient Slip Detection Method With Vision-Based Tactile Sensor Based on Local Deformation Degree

Abstract

Incipient slip detection is important for manipulators to achieve stable grasping. In order to make manipulators adapted to unstructured environments better and realize a wider range of applications, it is necessary to achieve incipient slip detection with robustness to object surface properties. In this paper, we propose a novel incipient slip detection method with vision-based tactile sensor. The method only uses sensor deformation data and does not need any information about the grasped object. In addition, it has the advantages of intuitive principles and a wide range of applicable working conditions. In this paper, the local deformation degree of the sensor is proved to be related to the stick/slip state at the corresponding location by kinematic aspects. Based on this, a method is proposed to select reference points in the stick region with local deformation degree. These reference points are used to fit the rigid motion of the contacted object and the slip field. Furthermore, an iterative algorithm is proposed to delineate the stick/slip region based on the characteristic of the slip field. Afterward, complex contact conditions are constructed through finite element simulation. Curved surfaces, discontinuous surfaces, and surfaces with multiple friction coefficients distribution are used to verify the correctness and superiority of the proposed method compared to other methods. Finally, the slip detection experimental bench is built, and our self-developed vision-based sensor is used to contact common objects with complex surface properties. Through the real slip detection experiments, the adaptability and good application prospects of this method are demonstrated.