Robot Cognition and Planning

This course deals with sensor fusion, decision making and information procession on real time for intelligent robots. To have a higher level of cognition, advanced level of problem-solving methods are presented for task planning, scheduling and navigation planning.
(Prerequisite: EE682, EE683)

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  • This course is intended to present the fundamental result of analysis and design of nonlinear control systems. Especially, this course is concerned with the analysis tools for nonlinear dynamical systems and the design techniques for nonlinear control systems. (Prerequisite: EE581)

  • Among the various well-known intelligent control techniques, the methods of fuzzy control and neural net-based learning control are first introduced to allow for handling ambiguous/uncertain situations and effective supervised learning, respectively. Specifically, the theory of fuzzy sets and fuzzy logic-based inference mechanism are studied and the design techniques of fuzzy control are introduced. Then, the neural net learning structure is discussed and the control system based on artificial neural nets is studied. Fuzzy-neuro systems are also considered. In the second part of the course work, some other computational intelligence techniques such as GA and the rough set are briefly covered and then the basic machine learning techniques and the reinforcement learning method are studied in conjunction with their use in control system design. (Prerequisite: EE581)

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