Touch Identification on Sensitive Robot Skin Using Time Domain Reflectometry and Machine Learning Methods
Research output: Contribution to book/conference proceedings/anthology/report › Chapter in book/anthology/report › Contributed › peer-review
The article presents the proof of concept of a novel sensor system for robotic HMI applications, mimicking the human sense of touch. An artificial sensitive skin, consisting of a robust and simple part of the sensing hardware based on electrical TDR, is mounted on the robot. In combination with adaptive AI algorithms, it enables for localisation of touch events on the sensor surface as well as determination of the touch-force magnitudes. Sensor data, obtained from a robotised test stand, are utilised to train and validate regressive DNNs for touch position recognition and classification DNNs for discrete force level classification. The results demonstrate that a high level of accuracy can be obtained, but some additional work is needed to reduce the gap between training and validation accuracy.
|Title of host publication||Artificial Intelligence for Digitising Industry - Applications|
|Publisher||Taylor and Francis Group|
|Number of pages||10|
|Publication status||Published - 2021|
- AI at the edge, food and beverage, Industrial internet of things (IIoT), industrial machinery, machine learning, Machine vision, Silicon-born AI Industrial sectors, Artificial intelligence, Deep learning, Neural Networks, semiconductor