DocumentCode :
2696928
Title :
Learning the motion map of a robot arm with neural networks
Author :
Saxon, James Bennett ; Mukerjee, Amitabha
fYear :
1990
fDate :
17-21 June 1990
Firstpage :
777
Abstract :
The integration of neural self-organization and circular reaction into a robot guidance system (Neurobot) are discussed. A two-degree-of-freedom robot arm learns a cognitive map which contains both the visual workspace and also the robot´s joint angles in a biologically inspired neural network. The range of positions of the robot´s end effector is called the workspace, and the corresponding joint angle space is called the configuration space. In other words, the workspace is the physical world of the robot, whereas the configuration space is an abstract space necessary for controlling the arm motions. Neurobot creates an association between its visual position and its joint position by training a self-organizing neural network using both spaces as inputs
Keywords :
neural nets; position control; robots; self-adjusting systems; Neurobot; cognitive map; motion map; neural networks; neural self-organization; robot arm; robot guidance system; self-organizing neural network; two-degree-of-freedom;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1990., 1990 IJCNN International Joint Conference on
Conference_Location :
San Diego, CA, USA
Type :
conf
DOI :
10.1109/IJCNN.1990.137794
Filename :
5726752
Link To Document :
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