DocumentCode :
3336854
Title :
Hand control by a neural network using tactile and positional information
Author :
Sperduti, Alessandro ; Starita, Antonina
Author_Institution :
Dipartimento di Inf., Pisa Univ., Italy
fYear :
1991
fDate :
19-22 June 1991
Firstpage :
1747
Abstract :
The development of manipulators with perceptive tactile capabilities which are really similar to those of the human hand has been hindered until now by the complexity of the sensory structures involved. Furthermore, sensors and actuators need to be closely integrated. The paper considers contact touch, a particular perceptive capability, during tasks that don´t require force; and studies how it can be used by the actuators to control the movements of the hand. The organization that the authors propose is implemented with a structured neural network. Backpropagation is used to map tactile receptors onto motor actuators.<>
Keywords :
backpropagation; manipulators; neural nets; position control; tactile sensors; contact touch; hand control; manipulators; motor actuators; neural network; perceptive tactile capabilities; position control; positional information; tactile receptors; Actuators; Backpropagation; Computational modeling; Computer networks; Force control; Force sensors; Humans; Neural networks; Quantization; Tactile sensors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Robotics, 1991. 'Robots in Unstructured Environments', 91 ICAR., Fifth International Conference on
Conference_Location :
Pisa, Italy
Print_ISBN :
0-7803-0078-5
Type :
conf
DOI :
10.1109/ICAR.1991.240350
Filename :
240350
Link To Document :
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