Remote Tactile Sensor
The purpose of this pioneering application is to further the development of a remote diagnosis and training system for dermatology. We developed and tested a tactile probe suitable for the detection of skin texture and the evaluation of skin profile. The unit, shown here is compact and light in weight suitable for mounting on a slave robotic arm. The sensing is done by an array of piezoelectric sensors formed on a piece of PZT film. As part of the design of this sensor we did extensive work on mathematical modeling of tactile sensors interacting with compliant substrates, such as skin for the purpose of evaluating texture.
The tactile probe has been tested on human subjects and has been successful at discriminating between skin on the back of the arm and that on the front of the arm and hand. It has also been possible to trace skin profiles, although the limitations of the Omni robots preclude accurate rendering of mechanical properties. (
Watch video of the sensor.)
We developed separate means of sensing of skin profile: that is the palpation of lumps in order to evaluate their geometry, location and mechanical properties. This requires a combination of the tactile probe, and the native haptics of the slave manipulator. We have successfully operated the tactile probe mounted on an OMNI slave robot and programmed to maintain constant pressure on the skin, in order to sense the skin profile.
The dynamic performance of the Omni robot is insufficient to satisfactorily act as a display device for profile data. For this reason we have built and tested a prototype of a lightweight, tendon driven haptic display device. We have also developed a vibrating coil display for texture data. The vibrating coil is mounted on the handle of the haptic display, in order to provide high-fidelity display of the complete spectrum of tactile sensation.
The network parameter of particular relevance to the Remote Tactile Sensor is latency. Closing a control loop around a delay of the order of that generated by speed of light and switching delays (10’s or 100’s of milliseconds) results in dynamic instability. Control algorithms, such as the wave variable algorithm, cause a softening of the haptic sensation. We are investigating calibration methods based on the average latency for each specific session. We are also investigating a model-driven approach to collecting haptic data where the learner then interacts with that model, rather than the actual patient.