Title :
A neural estimator of object stiffness applied to force control of a robotic finger with opponent artificial muscles
Author :
PedreÑo-Molina, J.L. ; Guerrero-gonzÁlez, A. ; García-córdova, F. ; López-Coronado, J.
Author_Institution :
Dept. of Inf. Technol. & Commun., Politechnical Univ. of Cartagena, Spain
Abstract :
We present a solution for real-time neural estimation of the stiffness characteristics of objects which are pressed with a predefined force threshold by an anthropomorphic robotic finger provided with opponent movement of their artificial muscles. The proposed architecture links three neural models in order to satisfy the requirements in our control system. This model based on adaptive learning allows the controller to grasp any object with different stiffness characteristics in a smooth way and with the desired final force
Keywords :
adaptive control; feedback; force control; learning (artificial intelligence); manipulator kinematics; neurocontrollers; real-time systems; tactile sensors; adaptive learning; anthropomorphic robotic finger; artificial muscles; feedback; force control; grasping; manipulators; neural estimator; neural models; neural nets; real-time system; stiffness characteristics; tactile sensors; Deformable models; Fingers; Force control; Humans; Muscles; Neurons; Robot sensing systems; Robotics and automation; Service robots; Systems engineering and theory;
Conference_Titel :
Systems, Man, and Cybernetics, 2001 IEEE International Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-7087-2
DOI :
10.1109/ICSMC.2001.971976
