Abnormal motion signal detection of mobile robot based on deep learning

In order to solve the problem of high false positive rate and false negative rate of mobile robot motion signal anomaly detection, a new method based on deep learning is designed. The abnormal state of mobile robot is analyzed, and the feature of mobile robot running data is extracted by using correlation dimension. The PNN training is completed by adopting the multi-neural network structure of deep learning to deal with the abnormal state sample data of the robot. Based on the motion control method and double evolutionary probability neural network, the abnormal motion signal is detected by fuzzy weighting method and fuzzy matching. Experimental results show that the method can effectively solve the problem of high false alarm rate and false positive rate, and promote the development of robot motion signal anomaly detection technology.