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It is difficult to realize the stable control of a wheeled biped robot (WBR), as it is an underactuated nonlinear system. To improve the balance and dynamic locomotion capabilities of a WBR, a decoupled control framework is proposed. First, the WBR is decoupled into a variable-length wheeled inverted pendulum and a five-link multi-rigid body system. Then, for the above two simplified models, a time-varying linear quadratic regulator and a model predictive controller are designed, respectively. In addition, in order to improve the accuracy of the feedback information of the robot, the Kalman filter is used to optimally estimate the system state. The control framework can enable the WBR to realize changing height, resisting external disturbances, velocity tracking and jumping. The results obtained by simulations and physical experiments verify the effectiveness of the framework.

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The detection of abnormal activities in deep learning is of great significance for preventing the occurrence of abnormal disasters in mine production. As the underground scenes of coal mines are characterized by much noise and uneven light, the traditional manual feature extraction method has little obvious effect in the underground and low accuracy of anomaly detection. To solve the above problems, a feature extraction method combining CNN+LSTM is proposed. Secondly, the obtained features are matched by graph structure. Finally, multiple classifiers are used to classify the features before and after matching. In this paper, experiments are carried out in coal mine dataset and UCSDped1 dataset respectively, and comparisons are made with some classical algorithms. Experimental show that the algorithm achieves high recognition accuracy in different abnormal event datasets.