With the ongoing advancements in automation technology, four-wheeled independent drive and steering (4WID-4WIS) automated guided vehicles (AGVs) are increasingly employed in intelligent logistics and warehousing systems. To enhance the performance of path tracking accuracy and cruising stability of AGVs, an automatic cruising methodology is proposed operating in complex environments. The approach integrates lateral control through model predictive control (MPC), which is optimized by a Long Short-Term Memory (LSTM) network, alongside fuzzy PID control for longitudinal management. By utilizing the LSTM network for trajectory prediction, the system can anticipate future vehicle states and outputs, thereby facilitating proactive adjustments that enhance the performance of the MPC lateral controller and improve both trajectory tracking accuracy and response speed. Concurrently, the fuzzy PID control strategy for longitudinal management increases the system’s adaptability to dynamic environments. The proposed methodology has been demonstrated in a physical prototype operating in real practical environments. Comparative results demonstrate that the LSTM-MPC significantly outperforms conventional MPC in lateral control accuracy. Additionally, the fuzzy PID controller yields superior longitudinal performance compared to traditional dual-PID and constant-speed strategies. This advantage is particularly evident in curved path segments, where the proposed fuzzy PID–LSTM–MPC framework achieves significantly higher lateral and longitudinal tracking accuracy compared to other control strategies.

Compared to four-wheel independent drive and steering (4WID4WIS) mobile robots, three-wheel independent drive and steering (3WID3WIS) mobile robots are more cost-effective due to their lower cost, lighter weight, and better handling performance, even though their acceleration performance is reduced. This paper proposes a dynamic model predictive control (DMPC) controller for trajectory tracking of 3WID3WIS mobile robots to simplify the computational complexity and improve the accuracy of traditional model predictive control (MPC). The A* algorithm with a non-point mass model is used for path planning, enabling the robot to navigate quickly in narrow and constrained environments. Firstly, the kinematic model of the 3WID3WIS mobile robot is established. Then, based on this model, a DMPC trajectory tracking controller with dynamic effects is developed. By replacing MPC with DMPC, the computational complexity of MPC is reduced. During each control period, the prediction horizon is dynamically adjusted based on changes in trajectory curvature, establishing a functional relationship between trajectory curvature and prediction horizon. Subsequently, a comparative study between the proposed controller and the traditional MPC controller is conducted. Finally, the new controller is applied to address the trajectory tracking problem of the 3WID3WIS mobile robot. The experimental results show that DMPC improves the lateral trajectory tracking accuracy by 62.94% and the heading angle tracking accuracy by 34.81% compared to MPC.

The appearance quality of fair-faced concrete plays a crucial role in evaluating the engineering quality, as the abundance of small-area bubbles generated during construction diminishes the surface quality of concrete. However, existing methods are plagued by sluggish detection speed and inadequate accuracy. Therefore, this paper proposes an improved method based on YOLOv5 to rapidly and accurately detect small bubble defects on the surface of fair-faced concrete. Firstly, to address the issue of YOLOv5 in generating prior boxes for imbalanced samples, we divide the image preprocessing part into small-, medium-, and large-area intervals corresponding to the number of heads. Additionally, we propose an area-based k-means clustering approach specifically tailored for the anchor boxes within each of these intervals. Moreover, we adjust the number of prior boxes generated by k-means clustering according to the training loss function to adapt to bubbles of different sizes. Then, we introduce the ECA (Efficient Channel Attention) mechanism into the neck part of the model to effectively capture inter-channel interactions and enhance feature representation. Subsequently, we incorporate feature concatenation in the neck part to facilitate the fusion of low-level and high-level features, thereby improving the accuracy and generalization ability of the network. Finally, we construct our own dataset containing 980 images of two classes: cement and bubbles. Comparative experiments are conducted on our dataset using YOLOv5s, YOLOv6s, YOLOxs, and our method. Experimental results demonstrate that the proposed method achieves the highest detection accuracy in terms of mAP0.5, mAP0.75, and mAP0.5:0.95. Compared to YOLOv5s, our method achieves a 7.1% improvement in mAP0.5, a 3.7% improvement in mAP0.75, and a 4.5% improvement in mAP0.5:0.95.

To address the frequent cleaning requirements of casting molds in bridge tower construction, a wall-climbing cleaning robot based on negative pressure adsorption is designed to safely and efficiently replace manual labor for cleaning tasks. The primary focus of this paper is the establishment of a theoretical model for negative pressure adsorption, along with an analysis of potential adhesion hazards. Initially, the robot’s chassis was designed, followed by the development of a theoretical model for the rotational-flow suction unit that incorporates two critical parameters: the number of blades and their thickness. This model was validated through computational fluid dynamics (CFD) and experimental methods. The findings indicate that, with fewer blades, an increase in blade quantity significantly improves the distribution of nonlinear velocity in the z-plane, resulting in a substantial enhancement of suction force up to a certain limit. As the number of blades increases, the thickness of the blades primarily influences the volume of air within the rotating domain, thereby affecting the suction force; thinner blades are preferable. Moreover, this study reveals that square suction units provide greater suction force compared to circular ones, attributable to their superior negative pressure effect and larger adsorption area. The most critical adhesion risk identified is leakage at the edges of the suction unit.