Title :
Improvement of robot control by neural computers
Author :
Eckmiller, R. ; Kreimeier, B.
Author_Institution :
Dept. of Biophys., Heinrich-Heine-Univ., Dusseldorf, Germany
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;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170689
