DocumentCode :
3453368
Title :
Combination of fuzzy logic and neural networks for the intelligent control of micro robotic systems
Author :
Wöhlke, G. ; Fatikow, S.
Author_Institution :
Inst. for Real-Time Comput. Syst. & Robotics, Karlsruhe Univ., Germany
Volume :
3
fYear :
1993
fDate :
26-30 Jul 1993
Firstpage :
1691
Abstract :
The authors present an advanced control concept for microrobotic systems, which is based on the combination of a neural network approach for the adaptation of manipulation parameters and a fuzzy logic approach for the correction of parameter values given to a conventional controller. This multilevel system architecture is suitable for the intelligent control of microrobots that can operate autonomously in changing environments. Typical tasks for these robots are exploration and fine manipulation, which demand intelligent task planning and motion/force control capabilities. The planning component deals with the successive determination of initial manipulation parameters, whereas the neural system performs during manipulation, computing suboptimal grasp forces and learning inference rules used for parameter adjustment
Keywords :
micromechanical devices; fuzzy control; fuzzy logic; grasp forces; inference rules; intelligent control; micro robotic systems; microrobots; motion/force control; multilevel system architecture; neural networks; parameter adjustment; task planning; Actuators; Control systems; Fuzzy logic; Intelligent control; Intelligent robots; Intelligent sensors; Mobile robots; Neural networks; Real time systems; Robot sensing systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems '93, IROS '93. Proceedings of the 1993 IEEE/RSJ International Conference on
Conference_Location :
Yokohama
Print_ISBN :
0-7803-0823-9
Type :
conf
DOI :
10.1109/IROS.1993.583864
Filename :
583864
Link To Document :
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