To address the shortcomings of traditional bionic algorithms in path planning, such as inefficient search processes, extended planning distances and times, and suboptimal dynamic obstacle avoidance, this paper introduces a fusion algorithm called NRBO-DWA. This algorithm is specifically applied to plan the path for a tube-changing robot in a knitting workshop. The process begins with spatial modeling based on the actual parameters of the workshop, followed by the development of a comprehensive, objective function for the robot in line with the relevant constraints. The NRBO algorithm is then integrated with the DWA algorithm to boost its dynamic obstacle avoidance capabilities, while a path correction mechanism is introduced to minimize unnecessary detours. Finally, a comparative experiment is designed to evaluate the algorithm against the GA, PSO, and SSA algorithms. Simulation results demonstrate that in a dynamically complex 3D environment, the NRBO-DWA algorithm outperforms in terms of higher 3D search efficiency, shorter total path length, and faster planning times.

Intelligent scheduling of knitting workshops is the key to realizing knitting intelligent manufacturing. In view of the uncertainty of the workshop environment, it is difficult for existing scheduling algorithms to flexibly adjust scheduling strategies. This paper proposes a scheduling algorithm architecture based on deep reinforcement learning (DRL). First, the scheduling problem of knitting intelligent workshops is represented by a disjunctive graph, and a mathematical model is established. Then, a multi-proximal strategy (multi-PPO) optimization training algorithm is designed to obtain the optimal strategy, and the job selection strategy and machine selection strategy are trained at the same time. Finally, a knitting intelligent workshop scheduling experimental platform is built, and the algorithm proposed in this paper is compared with common heuristic rules and metaheuristic algorithms for experimental testing. The results show that the algorithm proposed in this paper is superior to heuristic rules in solving the knitting workshop scheduling problem, and can achieve the accuracy of the metaheuristic algorithm. In addition, the response speed of the algorithm in this paper is excellent, which meets the production scheduling needs of knitting intelligent workshops and has a good guiding significance for promoting knitting intelligent manufacturing.

Automatic replacement of bobbins on the frame is one of the core problems that must be solved in the textile industry. In the process of changing the bobbin, it is faced with problems such as the position offset of the bobbin, the loosening and deformation of the bobbin, which will lead to the failure of replacing the bobbin. Therefore, it is necessary to initialize the creel coordinates regularly, also considering the labor intensity and poor reliability of manual positioning. This paper proposes an automatic creel positioning method based on monocular vision. Firstly, the industrial camera was installed on the drum changing manipulator controlled by the truss system, and each yarn frame in the yarn area was inspected. Secondly, the end face image of the creel was collected at a reasonable distance, and the collected images were transmitted to the computer in real time through the Ethernet bus. Thirdly, the center coordinates (x, y) and radius r of the creel were marked by the improved Hough circle detection algorithm. Finally, the coordinate deviation of the creel was calculated and transmitted to the system controller to realize deviation correction. Before the test, the creel positioning markers were specially designed and the camera was calibrated. Thus, the influence of image complex background, creel end roughness, reflection and other factors can be reduced, and the image processing speed and positioning accuracy can be improved. The results show that the positioning effect of this method is fine when the distance between the center of the camera lens and the center of the end face of the creel is 170~190 mm. Moreover, when the distance is 190 mm, the positioning effect is the best, with an average error of only 0.51 mm. In addition, the deviation between the center coordinate and the radius of the end face of the marker is also very small, which is better than the requirements of bobbin yarn changing accuracy.

To address the issue of mechanical resonance frequency detection in dual-inertia servo systems, this paper proposes an online identification method for mechanical resonance frequency using a low-pass filter and cascaded second-order generalized integrator—frequency-locked loop (LPF-CSOGI-FLL). Initially, the cascaded second-order generalized integrator—frequency-locked loop (CSOGI-FLL) is employed to eliminate the interference of direct current (DC) bias in resonance frequency identification. From a dual-stage structural perspective, the first second-order generalized integrator (SOGI-FLL) acts as a band-pass pre-filter to extract the mechanical resonance signal from the signal to be tested. The second SOGI-FLL generates a signal with equal amplitude and frequency to the mechanical resonance and obtains the frequency of the resonance signal through the frequency-locked loop. Subsequently, a low-pass filter (LPF) is applied to the frequency feedback loop of the second-stage SOGI-FLL, effectively reducing the oscillation of the estimated frequency. Finally, combining the CSOGI-FLL with an LPF forms a novel structure, namely, LPF-CSOGI-FLL. The results demonstrate that the proposed method significantly improves the detection accuracy of mechanical resonance frequency under various conditions. Compared to traditional offline techniques, this method overcomes the impact of resonance frequency drift and enhances system stability.

Underground utility tunnels face corrosion, cracks, and leakage after long-term use, endangering urban safety. Traditional methods have strong subjectivity, high miss rates, and poor real-time performance, failing refined management needs. This paper proposes an attention-enhanced YOLOv11 rather than YOLOv10 because its C3k2 backbone and dynamic anchor head already surpass YOLOv10 by 1.8% mAP for pipeline defect detection in utility tunnels. It uses homomorphic filtering to improve low-light image quality; replaces the last two C3k2 modules of the original YOLOv11 with a Multi-Scale Feature Aggregation Module to capture micro-cracks via expanded receptive fields; introduces a bidirectional weighted feature pyramid network in the neck (with C2PSA/BRA attention) for cross-scale feature fusion and background suppression, which yields both fine-grained micro-crack sensitivity and global false-target suppression; and adopts DIoU loss in the detection head to reduce slender defect localization errors. Experiments on 5000 utility tunnel defect images show the improved algorithm achieves 93.2% precision, 92.4% recall, and 92.6% mAP—outperforming the original YOLOv11, Faster R-CNN, and YOLOv5. Ablation experiments confirm module effectiveness, cutting relative error by 75% compared with the baseline. This algorithm can accurately identify multiple types of defects in complex utility tunnel environments, providing technical support for the safe and efficient operation and maintenance of urban infrastructure.

To mitigate the challenges of false positives and undetected small-scale defects in fabric inspection, this study proposes an advanced fabric defect detection system that leverages an optimized YOLOv9 algorithm. First, redundant computations are reduced by introducing DualConv to replace standard convolution. Second, the LSKA attention mechanism is incorporated to increase the weight of important features, thereby enhancing the accuracy of small target detection and improving the generalization ability. Additionally, the focal modulation network is employed to replace the fast spatial pyramid module, mitigating the loss of detailed information caused by the feature pooling operation. Furthermore, the conventional feature pyramid network is replaced with bidirectional feature pyramid network, which is utilized for efficient feature fusion, thereby enhancing multiscale feature representation and improving detection accuracy. Finally, the bounding box loss function is optimized by introducing the shape-IoU loss function, which facilitates more rapid model convergence and significantly improves detection accuracy. Experiments conducted on a fabric defect dataset demonstrate that the proposed algorithm yields a 6.7% increase in mAP@0.5 and a 14.7% improvement in mAP@0.5–0.95, while simultaneously reducing the model’s total parameters by 17.8% and computational FLOPs by 14.4%, compared with those of the original algorithm. The improved YOLOv9 model significantly enhances the precision and accuracy of defect detection while maintaining inference speed (55.8 FPS) that meets industrial requirements.