During the construction of prefabricated building, there are some problems such as a time consuming, low-level of automation when precast concrete members are assembled and positioned. This paper presents vision-based intelligent assembly alignment guiding technology for columnar precast concrete members. We study the video images of assembly alignment of the hole at the bottom of the precast concrete members and the rebar on the ground. Our goal is to predict the trajectory of the moving target in a future moment and the movement direction at each position during the alignment process by assembly image sequences. However, trajectory prediction is still subject to the following challenges: (1) the effect of external environment (illumination) on image quality; (2) small target detection in complex backgrounds; (3) low accuracy of trajectory prediction results based on the visual context model. In this paper, we use mask and adaptive histogram equalization to improve the quality of the image and improved method to detect the targets. In addition, aiming at the low position precision of trajectory prediction based on the context model, we propose the end point position-matching equation according to the principle of end point pixel matching of the moving target and fixed target, as the constraint term of the loss function to improve the prediction accuracy of the network. In order to evaluate comprehensively the performance of the proposed method on the trajectory prediction in the assembly alignment task, we construct the image dataset, use Hausdorff distance as the evaluation index, and compare with existing prediction methods. The experimental results show that, this framework is better than the existing methods in accuracy and robustness at the prediction of assembly alignment motion trajectory of columnar precast concrete members.

The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. When the fault frequency changes in variable working conditions, the signal processing effect becomes different with fixed parameters, leading to errors in fault detection. In this paper, an underdamped second‐order adaptive general variable‐scale stochastic resonance (USAGVSR) method with potential well parameters’ optimization is put forward. For input signals with different fault frequencies, the potential well parameters related to the barrier height are figured out and optimized through the ant colony algorithm. On this basis, further optimization is carried out on undamped factor and step size for better fault detection performance. Cases with diverse fault types and in different working conditions are studied, and the performance of the proposed method is validated through experiments. The results testify that this method has better performances of weak fault feature extraction and can accurately identify different fault types in the input signals. The method proves to be effective in the weak fault extraction and classification and has a good application prospect in rolling bearings’ fault feature recognition.

This paper proposes a visual servo scheme for hoisting positioning under disturbance conditions. In actual hoisting work, disturbances such as equipment and load vibration are inevitable, which brings challenges to the development of a visual servo for hoisting positioning. The main problems are as follows: (1) the correlation between visual error and disturbance is not considered or well resolved; (2) the disturbance has a great influence on the control stability, but it is difficult to model. At present, there is no detailed research on the above problems. In this paper, the visual error is defined by the image error of the feedback signal based on dynamic equations containing disturbances. An adaptive sliding mode control algorithm is employed to decrease the influence of external disturbance, and the coefficient of the slide surface is established based on the adaptive gain. In view of the belief that it is difficult to model disturbance terms, a nonlinear disturbance observer is introduced to obtain equivalent disturbance. On this basis, an adaptive control algorithm with disturbance compensation is proposed to improve the robustness of the visual servo system. We use Lyapunov’s method to analyze the stability conditions of the system. Compared with the other state-of-the-art methods, the simulation results show that our method has superior performance in convergence, accuracy, and restraining disturbance. Finally, the proposed algorithm is applied to the hoisting platform for experimental research, which proves the effectiveness of the controller.