DocumentCode
2644138
Title
Improvement of robot control by neural computers
Author
Eckmiller, R. ; Kreimeier, B.
Author_Institution
Dept. of Biophys., Heinrich-Heine-Univ., Dusseldorf, Germany
fYear
1991
fDate
18-21 Nov 1991
Firstpage
1837
Abstract
Currently available robot control systems have various limitations in comparison to biological motor systems partly due to a lack of a general control theory for robots in a dynamic environment and partly due to the real-time challenge for conventional computers. The authors review current approaches to (1) speeding up the neural computation by means of adaptive load distribution on massively parallel computers and (2) the design of adaptive neural net modules for path planning, obstacle avoidance, inverse kinematics, system identification, and dynamic control of robot manipulators
Keywords
identification; kinematics; neural nets; planning (artificial intelligence); robots; adaptive load distribution; adaptive neural net modules; dynamic control; inverse kinematics; massively parallel computers; neural computers; obstacle avoidance; path planning; robot control; system identification; Adaptive control; Biology computing; Concurrent computing; Control theory; Distributed computing; Manipulator dynamics; Neural networks; Programmable control; Real time systems; Robot control;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN
0-7803-0227-3
Type
conf
DOI
10.1109/IJCNN.1991.170689
Filename
170689
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