Humans can localize lumps in soft tissue using the distributed tactile feedback and processing afforded by the fingers and brain. This task becomes extremely difficult when the fingers are not in direct contact with the tissue, such as in laparoscopic or robot-assisted procedures. Tactile sensors have been proposed to characterize and detect lumps in robot-assisted palpation. In this work, we compare the performance of a capacitive tactile sensor with that of the human finger. We evaluate the response of the sensor as it pertains to robot-assisted palpation and compare the sensor performance to that of human subjects performing an equivalent task on the same set of artificial tissue models. Furthermore, we investigate the effects of various tissue parameters (lump size, lump depth, and surrounding tissue stiffness) on the performance of both the human finger and the tactile sensor. Using signal detection theory for determining tactile sensor lump detection thresholds, the tactile sensor outperforms the human finger in a palpation task.

The response of a tactile sensor system (consisting of the sensors themselves and the material covering them) was characterized via robotic experiments. A point spread function model of this response was developed for typical interaction forces, allowing the use of graphics and imaging techniques respectively for simulating and interpreting tactile sensor readings. This model was implemented in software as a generic artificial tactile sensor simulator, and its accuracy at approximating the output of our test system is demonstrated.