A review on the deformation tracking methods in vision-based tactile sensing technology

Abstract

In daily life, human need various senses to obtain information about their surroundings, and touch is one of the five major human sensing signals. Similarly, it is extremely important for robots to be endowed with tactile sensing ability. In recent years, vision-based tactile sensing technology has been the research hotspot and frontier in the field of tactile perception. Compared to conventional tactile sensing technologies, vision-based tactile sensing technologies are capable of obtaining high-quality and high-resolution tactile information at a lower cost, while not being limited by the size and shape of sensors. Several previous articles have reviewed the sensing mechanism and electrical components of vision-based sensors, greatly promoting the innovation of tactile sensing. Different from existing reviews, this article concentrates on the underlying tracking method which converts real-time images into deformation information, including contact, sliding and friction. We will show the history and development of both model-based and model-free tracking methods, among which model-based approaches rely on schematic mechanical theories, and model-free approaches mainly involve machine learning algorithms. Comparing the efficiency and accuracy of existing deformation tracking methods, future research directions of vision-based tactile sensors for smart manipulations and robots are also discussed.