Towards the realization of an artificial tactile system: fine-form discrimination by a tensorial tactile sensor array and neural inversion algorithms

• Published in: IEEE transactions on systems, man, and cybernetics Vol. 25; no. 6; pp. 933 - 946
• Main Authors: Caiti, A., Canepa, G., De Rossi, D., Germagnoli, F., Magenes, G., Parisini, T.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.1995; Institute of Electrical and Electronics Engineers
• Subjects: Algorithm design and analysis; Artificial neural networks; Backpropagation algorithms; Exact sciences and technology; General equipment and techniques; Instruments, apparatus, components and techniques common to several branches of physics and astronomy; Multi-layer neural network; Multilayer perceptrons; Neural networks; Physics; Polymers; Sensor arrays; Sensors (chemical, optical, electrical, movement, gas, etc.); remote sensing; Servo and control equipment; robots; Shape; Tactile sensors; Design; Algorithms; Inverse problems; Piezoelectric materials; Stress tensor; Tactile sensor; Mechanical contacts; Neural network; Sensor array; Shape detection
• ISSN: 0018-9472
• DOI: 10.1109/21.384256

This paper describes techniques and methodologies so far developed to investigate object fine-form discrimination by means of artificial tactile sensors. Sensor arrays, selectively sensitive to stress-tensor components and based on piezoelectric polymer technology, have been realized. Sensor output data are used to solve inverse elastic contact problems, by means of neural networks suitably trained to learn regularized inverse maps. Two possible neural network designs are considered: one is based on the multilayer perceptron trained with the standard backpropagation algorithm, and the other is based on the use of radial basis functions. In both cases, reconstruction of object shapes is demonstrated to be effective and robust with both simulated and real data.< >