Aiming at the problem that ultra-wide band (UWB) cannot be accurately localized in environments with large noise variations and unknown statistical properties, a combinatorial localization method based on improved cubature (CKF) is proposed. First, in order to overcome the problem of inaccurate local approximation or even the inability to converge due to the initial value not being set near the optimal solution in the process of solving the UWB position by the least-squares method, the Levenberg–Marquardt algorithm (L–M) is adopted to optimally solve the UWB position. Secondly, because UWB and IMU information are centrally fused, an adaptive factor is introduced to update the measurement noise covariance matrix in real time to update the observation noise, and the fading factor is added to suppress the filtering divergence to achieve an improvement for the traditional CKF algorithm. Finally, the performance of the proposed combined localization method is verified by field experiments in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, respectively. The results show that the proposed method can maintain high localization accuracy in both LOS and NLOS scenarios. Compared with the Extended Kalman filter (EKF), unbiased Kalman filter (UKF), and CKF algorithms, the localization accuracies of the proposed method in NLOS scenarios are improved by 25.2%, 18.3%, and 11.3%, respectively.

Latent heat thermal energy storage systems play a crucial role in aligning energy supply with demand, enhancing the efficiency of energy usage, thereby aiding in energy conservation and emissions reduction, and promoting the efficient use of renewable energy. Therefore, we constructed an experimental apparatus for a shell-and-tube latent heat storage. This apparatus was utilized to investigate how varying the inclination angle of the heat storage device, the inlet temperature of the heat transfer fluid (HTF), and water flow direction affect both the heat transfer behavior and the thermal efficiency of the system. The findings indicate that as the inlet temperature rises, the melting rate of the phase-change material (PCM) increases; when the inclination angle is 0°, for every 5 °C increase in water temperature, the time required to reach thermal equilibrium is shortened by 2 h, and the time needed for the PCM to transition from a solid to a liquid state is correspondingly reduced by 2 h. Additionally, the temperature variation trend of the phase-change material remains fundamentally consistent at different inclination angles. However, as the angle increases from 0° to 90°, there is a gradual reduction in the melting rate. Whether the water enters from the top or bottom, the melting rate of the PCM remains almost unchanged, and the stabilized temperature of the PCM is also nearly the same.

Prefabricated construction has pioneered a new model in the construction industry, where prefabricated component modules are produced in factories and assembled on-site by construction workers, resulting in a highly efficient and convenient production process. Within the construction industry value chain, the smoothing and roughening of precast concrete components are critical processes. Currently, these tasks are predominantly performed manually, often failing to achieve the desired level of precision. This paper designs and develops a robotic system for smoothing and roughening precast concrete surfaces, along with a multi-degree-of-freedom integrated intelligent end-effector for smoothing and roughening. Point-to-point path planning methods are employed to achieve comprehensive path planning for both smoothing and roughening, enhancing the diversity of textural patterns using B-spline curves. In the presence of embedded obstacles, a biologically inspired neural network method is introduced for precise smoothing operation planning, and the A* algorithm is incorporated to enable the robot’s escape from dead zones. Experimental validation further confirms the feasibility of the entire system and the accuracy of the machining path planning methods. The experimental results demonstrate that the proposed system meets the precision requirements for smoothing and offers diversity in roughening, affirming its practicality in the precast concrete process and expanding the automation level and application scenarios of robots in the field of prefabricated construction.

To improve the detection accuracy of highway pavement cracks, a crack detection method combining ef-fective multi-scale feature fusion structure and cascade optimization is proposed for the problems of crack polymorphism and noise interference. Then, aiming at the focus of encoder-decoder on feature information, spatial and channel attention mechanisms are introduced to suppress background noise interference and enhance crack feature expression. Finally, the joint optimization learning of different levels of decoding features is carried out to strengthen the use of low-level feature information and improve the coherence of crack segmentation lines. The experimental results on data sets such as Crack500 show that the visual crack results detected by the proposed method have clear and complete texture, and the F1 score and average intersection over union on the comprehensive test set are 90.07% and 82.07%, which has better recognition effect and robustness, and can provide a deep learning method for automatic crack defect detection tasks.

The problem of self-organizing flexible wrapping for swarm robots refers to the spontaneous formation of stable, deformable, and adaptive structures by a robot swarm relying solely on local perception, without a global map or centralized control, to tightly enclose irregular or dynamic targets. This holds significant application value in fields such as environmental containment, disaster rescue, and wide-area monitoring. This paper proposes a self-organizing flexible wrapping method for swarm robots based on local density interactions. First, a local density field model integrating individual area weights is constructed to achieve the quantitative characterization of the target distribution space. Second, a weighted density is calculated based on the distributions of neighborhood robots and targets. Combined with diffusion and collision avoidance mechanisms, a distributed control law is designed to drive the swarm to adaptively converge towards the target boundaries while maintaining a uniform distribution. Finally, simulation and real-world experiments demonstrate that the proposed method can control the robot swarm to form a flexible wrapping structure. Compared with traditional methods, it significantly improves the conformity to complex target boundaries and overall adaptability, thereby verifying the feasibility and effectiveness of the proposed algorithm.