Data-driven dynamic inclination angle estimation of monorail crane under complex road conditions

Monorail cranes are crucial in facilitating auxiliary transportation within deep mining operations. As unmanned driving technology becomes increasingly prevalent in monorail crane operations, it encounters challenges such as low accuracy and unreliable attitude recognition, significantly jeopardizing the safety of monorail crane operations. Hence, this study proposes a dynamic inclination estimation methodology utilizing the Estimation-Focused-EKFNet algorithm. Firstly, based on the driving characteristics of the monorail crane, a dynamic inclination model of the monorail crane is established, based on which the dynamic inclination value can be calculated in real-time by the extended Kalman filter (EKF) estimator; however, given the complexity of the driving road conditions, in order to improve the dynamic inclination recognition accuracy, the CNN-LSTM-ATT algorithm combining the convolutional neural network (CNN), the long short-term memory (LSTM) neural network and the attention mechanism (ATT) is used to firstly predict the current dynamic camber is predicted by the CNN-LSTM-ATT algorithm combined with the convolutional neural network and the attention mechanism, and then the predicted dynamic inclination value is used as the observation value of the EKF estimator, which finally realizes that the EKF estimator can output the accurate dynamic inclination value in real-time. Experimental results indicate that, compared with the unscented Kalman filter (UKF), LSTM-ATT, and CNN-LSTM algorithms, the Estimation-Focused-EKFNet algorithm enhances dynamic inclination recognition in complex road conditions by at least 52.34%, significantly improving recognition reliability. Its recognition accuracy reaches 99.28%, effectively ensuring the safety of unmanned driving for monorail cranes.