Navigation and collision avoidance are major areas of research in mobile robotics that involve varying degrees of uncertainty. In general, the problem consists of achieving sensor based motion control of a mobile robo...
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Navigation and collision avoidance are major areas of research in mobile robotics that involve varying degrees of uncertainty. In general, the problem consists of achieving sensor based motion control of a mobile robot among obstacles in structured and/or unstructured environments with collision-free motion as the proirity. A fuzzy logic based intelligent control strategy has been developed here to computationally implement the approximate reasoning necessary for handling the uncertainty inherent in the collision avoidance problem.< >
A comparative study of a classical expert system and an adaptive fuzzy expert control system for an inverted pendulum is presented. Specifically, three controllers are compared: constant gain, expert controller, and a...
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A comparative study of a classical expert system and an adaptive fuzzy expert control system for an inverted pendulum is presented. Specifically, three controllers are compared: constant gain, expert controller, and an adaptive fuzzy expert controller. The comparative study was done by means of computer simulation through the Matlab and Togai InfraLogic's Fuzzy-C software programs. It is seen that the fuzzy expert controller provides much better performance than the regular expert system.< >
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