Title :
Neural redundant robotic trajectory optimization with diagnostic motor control
Author_Institution :
T.J. Watson Sch. of Eng. & Appl. Sci., Binghamton Univ., NY, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
The joint commands of a simulated redundant PUMA robot and hand are allocated via a suggested command weight neural network for each DC motor. Three inputs of this network are diagnostic outputs of the neural network motor controller. Commands minimize time, energy expended, and error while attempting to reduce stress on motors experiencing extremes in loading, friction, or stiffness. This diagnostic control, combined with knowledge of a best vector of approach provided by an object grasp category network, enables the hand to attempt an accurate and stable first grasp of an object
Keywords :
DC motors; adaptive control; manipulators; neural nets; neurocontrollers; optimal control; optimisation; position control; DC motor; adaptive motor control; command weight neural network; diagnostic motor control; joint commands; object grasp category network; optimal control; redundant PUMA robot; redundant robotic trajectory optimization; Actuators; Adaptive control; Control systems; DC motors; Friction; Motion control; Motor drives; Neural networks; Optimal control; Robot sensing systems;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374606
