Haptic Object Recognition in Underwater and Deep‐sea Environments

Abstract

Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for perception and object recognition in underwater and deep‐sea environments. However, the limited availability of tactile sensors for underwater use has led to limited research in this domain. Recently, we have developed a deep‐sea‐capable tactile sensing system, with high spatial and force resolutions, which has made underwater haptic exploration possible for the first time. This paper presents a tactile sensor‐based object recognition and localization methodology for structured underwater and deep‐sea applications. Our approach is based on database matching using a local feature‐based Random Sampling and Consensus (RANSAC) algorithm, and sequentially evolving the resulting hypotheses over the course of object exploration. It can handle a large database of three‐dimensional objects of complex shapes, and it performs a complete six degree of freedom localization of a static object. An approach to utilize both contact and free‐space measurements is presented. Extensive experimentation is performed in underwater environments for validating both the sensor system and the algorithms. To our knowledge, this is the first instance of haptic object recognition and localization in underwater and deep‐sea environments.