UCAM

(Partner: DLR with UMEA)

With robotic systems moving out of isolated working environments and into our everyday life the need for advanced sensory capabilities increases. Pressure-sensitive skin-like coatings allow robots to interact in a secure and much more precise manner with their surroundings. Getting direct tactile feedback from, say, the finger tips of a robotic hand, enables the robot to adjust the force of its grip and detect slippage of the grasped object. Another application area is human prosthetics where a pressure sensitive skin could be used to provide tactile information from the artificial limb to the patient.

There is a variety of pressure sensors available, however none are applicable for use in an artificial skin system. Requirements include robustness regarding wear and tear, easy low-cost mass production and a space-saving, fast read-out mechanism. The skin should be soft and exhibit mechanical properties comparable to that of human skin.

DLR's developed robust yet accurate touch sensors for robotic hands. At this, their work is guided by the mechanical and information processing capabilities of the human sensory system. The artificial skin consists of soft, elastic material filled with carbon black particles. They experiment with both rubber and injection-moldable ultrasoft polymers as primary materials. When applying pressure to the composite its resistance decreases, which is due to the creation of conductive pathways from the carbon black. Wires running through the material at different layers are then used to locally measure resistance changes. The first prototype uses two layers, which are orthogonal to each other so as to maximize the number of read-out points.

The resulting system can easily be varied in size, shape and spatial resolution and can be fitted onto 3D surfaces because of its inherent flexbility. By changing the number of wires as well as their location, spatial resolution can be influenced and does not have to stay constant within a single skin patch.

(Partner: UEDIN with UMEA) It has been shown that during dexterous object manipulation, humans rely on a variety of sensory information, for example from vision or audition. Among these different sensory sources, tactile feedback from mechanoreceptors within the human skin is especially crucial: without it, object handling is severely impaired, as important input about object properties like smoothness, curvature, or slippage that are necessary for adjusting grip forces during manipulation is missing.

How the tactile system can provide rapid yet accurate information about tactile object properties is still under debate. Recently is has become clear that traditional methods of decoding the information contained in neuronal spike trains are too slow to provide the brain with the necessary information in time.

We have used an information-theoretic approach to examine various decoding schemes with respect to the amount of information they can transmit and the time at which this information becomes available.

Under natural conditions, multiple tactile features can be expected to vary simultaneously. Moreover, due to mechanical properties of the skin, its indentation is influenced both by the current as well as previous stimuli. It has been analysed how such conditions affect classification of tactile stimuli and what techniques the brain uses to ensure fast and reliable information transmission.