Surface estimation from multi-modal tactile data

Abstract

The increasing popularity of Robotic applications has seen use in healthcare, surgery, and as an industrial tool. These robots are expected to be able to make physical contact with the objects in the environment which allows tasks such as grasping and manipulation, while also allowing to obtain information about the objects such as shape, texture, and hardness. In an ideal world, a complete model of the environment would be known beforehand and robots would not need to explore objects and surfaces since their information would be available in the model of the world. In the real world, most environments are unstructured and robots must be able to operate safely without causing harm to themselves or objects while taking into account environmental uncertainties and building models for the environment and its objects. To overcome this, the trend has been to use computer vision to detect objects in the environment. Although computer vision has seen great advancement in this regard, there are some problems that cannot be solved by using vision alone. Objects that are occluded, transparent, or do not have rich visual features cannot be detected by using vision. It is also impossible to estimate features such as hardness or tactile texture using vision. To this end, we use a bio-inspired tactile sensor consisting of a compliant structure, a MARG sensor, and a pressure sensor along with a robotic manipulator to explore surfaces with the only assumption that the general location of the surface is known. This sensing module allows the robotic manipulator to have a predetermined angle of approach which is essential when exploring unseen surfaces. [...]