The biped robot research began in the early 1970s, great progresses have been made in the recent several years. Incredible prototypes are constructed constantly, our original cognition are being subverted by the fabulous performances. Because the biped robot platform has attractive preferences of high order and nonlinear, many researches devote themselves to the development of stability criterion, strategy of control and mechanical designing. Stability criterion considered as the base of structure and control method is playing an important role in stable walking. This thesis aspires to propose a synthesis biped robot stability criterion. In the thesis, the principle of criterion based on feature points and Poincare projection are discussed, a concept of synthesis stability criterion is proposed based on the tradition ones. Smooth and stable joints trajectories are planned by the method of ZMP stability criterion and a virtual environment were constructed with the utility of Webots software which is concentrating on the robot simulation. Introducing a Machine Learning classify algorithm, the best classifier are applied after having trained and tested by the certain database. Estimation function is used to determine the trade off of the efficiency and cost. Finally, the basic classifier and the proper operating point will construct the frame of the synthesis stability criterion. Concretely, it includes several aspects, such as: 1. The study of traditional stability criterion. First of all, the thesis introduces what natures should an ideal stability criterion has. Secondly, the principle of traditional criterion is deduced, a comparison is made between the traditional criterion and the ideal one. Finally, considering the different between the tradition and the ideal, the thesis proposes an idea which may involve all the advances of traditional criterion. 2. Generate the stable gait, construct the virtual environment. Firstly, smooth and stable joints trajectories will be generated based on the ZMP criterion using the cubic spline interpolation. Secondly, a robot model and its virtual world file will be constructed using Webots software. Finally, gait which has been generated will be loaded on the robot model controlling the robot walk with the trajectory we set. 3. Build data set, obtain basic classifier. Firstly, the reason of why should we introduce machine learning is discussed; meanwhile, a projection between stability criterion and classifier is ...
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