| Summary: | 碩士 === 國立臺灣師範大學 === 機電工程學系 === 104 === The primary search in this content are path planning and path tracking of wheel mobile robot. For path planning, adopting A * algorithm with the feature of minimum cost function results to design the shortest path of wheeled mobile robots. However, the result of path planning with this method will generate a lot of turning points and too close to the obstacle on the path. Under this situation, then use B-Spline curve which has local change adjustment feature can adjust the segments of A * algorithm where are not suitable for wheeled mobile robot to track. After the path is generated, the next consideration is path tracking. To design tracking controller, we must know kinematics model of wheeled mobile robot, therefore it is required to derived of kinematics model beforehand. For path tracking, it is accomplished the purpose by adjusting the speed of two wheels of the mobile robot, this paper uses adaptive network-based fuzzy inference system technology which combines with fuzzy and neural network, it contains the feature of qualitative analysis capabilities of fuzzy control and quantitative analysis capabilities of neural network, and has ability of self-learning adjustment , then add genetic algorithm to optimize the ideal result.
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