Hu Chen

Planning algorithms are essential for reducing computational complexity in robotics and autonomous driving. While the Rapidly exploring Random Tree Star (RRT*) algorithm offers probabilistic completeness and asymptotic optimality, its practical efficiency is hampered by slow convergence, high initial path cost, and excessive invalid sampling due to uninformed tree expansion. To address these limitations, this study introduces the A-RRT* algorithm. The key improvements include: utilizing an A* path to define an adaptive sampling region for faster initial solution quality and convergence; incorporating a goal bias strategy to guide random node generation; and implementing a steering angle criterion during parent node reselection within a multi-iteration replanning framework to refine the path to global optimality. Simulation confirm that the proposed A-RRT* algorithm effectively enhances planning efficiency and path quality compared to the comparison algorithms. Specifically, it reduces the initial solution time by up to 55%, lowers the initial path cost by 4.5–12.5%, and achieves final path cost that is 1.6–9.8% shorter.

The You Only Look Once (YOLO) series of models, particularly the recently introduced YOLOv12 model, have demonstrated significant potential in achieving accurate and rapid recognition of electric power operation violations, due to their comprehensive advantages in detection accuracy and real-time inference. However, the current YOLO models still have three limitations: (1) the absence of a dedicated feature extraction for multi-scale objects, resulting in suboptimal detection capabilities for objects with varying sizes; (2) naive integration of spatial and channel attentions, which restricts the enhancement of feature discriminability and consequently impairs the detection performance for challenging objects in complex backgrounds; and (3) weak representation capability in low-level features, leading to insufficient accuracy for small-sized objects. To address these limitations, a novel YOLO model named DFA-YOLO is proposed, a real-time object detection model with YOLOv12n as its baseline, which makes three key contributions. Firstly, a dynamic weighted multi-scale convolution (DWMConv) module is proposed to address the first limitation, which employs lightweight multi-scale convolution followed by learnable weighted fusion to enhance feature representation for multi-scale objects. Secondly, a full-dimensional attention (FDA) module is proposed to address the second limitation, which gives a unified attention computation scheme that effectively integrates attention across height, width, and channel dimensions, thereby improving feature discriminability. Thirdly, a set of auxiliary detection heads (Aux-Heads) are introduced to address the third limitation and inserted into the backbone network to strengthen the training effect of labels on the low-level feature extraction module. The ablation studies on the EPOVR-v1.0 dataset demonstrate the validity of the proposed DWMConv module, FDA module, Aux-Heads, and their synergistic integration. Relative to the baseline model, DFA-YOLO achieves significant improvements in mAP@0.5 and mAP@0.5–0.95, by 3.15% and 4.13%, respectively, meanwhile reducing parameters and GFLOPS by 0.06M and 0.06, respectively, and increasing FPS by 3.52. Comprehensive quantitative comparisons with nine official YOLO models, including YOLOv13n, confirm that DFA-YOLO achieves superior performance in both detection precision and real-time inference, further validating the effectiveness of the DFA-YOLO model.

In order to solve the problem that steel surface defects are easily covered or submerged by other objects or noise, this paper proposes an open–closed transformation algorithm which can eliminate or weaken multiple noises. In the case of a small number of samples, this paper establishes a super-resolution generative adversarial neural network to achieve the enhancement of sample data. For avoiding unrealistic image defects caused by cuts or brightness variations, an enhancement method is given which incorporates the original defective high-frequency information into classical image fusion methods, such as rotation and error slicing. Experimental results show that the accuracy of the proposed denoising method reaches over 90%, which is more than 2.6% of that of the most primitive classification network. To compare with existing denoising methods, the denoising method proposed in this paper not only has higher accuracy, faster denoising speed, and stronger anti-interference ability, but also has better adaptation to the environment. This research will provide a new solution method for the denoising of multi-noise phenomena in multiple different environments.

Crack identification plays a vital role in preventive maintenance strategies during highway pavement maintenance. Therefore, accurate identification of cracks in highway pavement images is the key to highway maintenance work. In this paper, an improved U-Net network adopting multi-scale feature prediction fusion and the improved parallel attention module was put forward to better identify concrete cracks. Multiscale feature prediction fusion combines multiple U-Net features generated by intermediate layers for aggregated prediction, thus using global information from different scales. The improved parallel attention module is used to process the U-Net decoded output of multi-scale feature prediction fusion, which can give more weight to the target region in the image and further capture the global contextual information of the image to improve the recognition accuracy. Improving the bottleneck layer is used to improve the robustness of the model and prevent overfitting. Experiments show that the improved U-Net network in this paper has a significant improvement over the original U-Net network. The performance of the proposed method in this paper was investigated on two publicly available datasets (Crack500 and CFD) and compared with competing methods proposed in the literature. Using the Crack500 dataset, the method in this paper achieved the highest score in precision (89.60%), recall (95.83%), mIOU (83.80%), and F1-score (92.61%). Similarly, for the CFD dataset, the method in this paper achieved high values for precision (93.29%), mIOU (82.07%), recall (86.26%), and F1-score (89.64%). Thus, the method has several advantages for identifying cracks in highway pavements and is an ideal tool for practical work. In future work, identifying more crack types and model light-weighting are the key objectives. Meanwhile, this paper provides a new idea for road crack identification.

Path planning is an important aspect and component in the research of mobile-robot-related technologies. Many path planning algorithms are only applicable to static environments, while in practical tasks, the uncertainty in dynamic environments increases the difficulty of path planning and obstacle avoidance compared with static environments. To address this problem, this paper proposes an RRT*-FDWA algorithm. RRT* first generates a global optimal path, and then, when obstacles exist nearby, an FDWA algorithm fixes the local path in real time. Compared with other path planning algorithms, RRT*-FDWA can avoid local minima, rapidly perform path replanning, generate a smooth optimal route, and improve the robot’s maneuvering amplitude. In this paper, the effectiveness of the algorithm is verified through experiments in dynamic environments.

In order to solve the problem of automatically completing safety detection for construction sites and give an alert based on high-speed image streams, this paper proposes a violation of rules and regulations (VoRR) recognition method on a construction site and gives a matching method by automatically obtaining a few samples. The proposed safety detection method consists of five parts, which are redundant information reduction, classification, feature extraction, matching, inference rule and alarm alert. Compared with existing safety detection methods, the accuracy of the proposed method is increased by more than 9%. It not only has better performance, but also has more functions: reminding and alarming. For the subsequent establishment of an unmanned supervision system model on a construction site, this research will provide a new method of decision support, target detection, and recognition in multiple different scenarios.

Although some algebraic theories such as partial order set, lattice and their properties on fuzzy set had been studied, these algebraic theories on fuzzy automata (FA) have not been discussed at present. For saving the energy of the battery so as to make it play the best performance, this paper proposes first an algebraic system on a finite-state deterministic fuzzy automaton (FDFA), including the partial order set, lattice and its properties. Second, this paper constructs a detection model system to monitor the consumption of battery power, and the detection model consists of four parts, as well as including supervision function network and decision-making process. Third, this paper gives the whole dynamic process of consumption and consumption rate of battery power, as well as discusses the comparison of the detection accuracy of between the detection model and other detection methods in the battery power consumption by simulation. Through the homomorphism and the isomorphic mapping of the lattice, the partial state of the FDFA is studied to realize the function of the whole state. These research results will provide a theoretical reference, system modeling and application background for many departments such as control, detection, tracking, identification and so on.

With the arrival of big data age, the deep convolutional neural networks (DCNNs) with more hidden layers have a more complex network structure and more powerful ability than traditional machine learning methods for feature learning and feature expression. This paper first proposes a model of the DCNN to discuss the basic structure of model, convolutional feature extraction and learning algorithm of convolutional neural network; then, mainly introduces several aspects, that is, the construction of the typical network structure, the training methods, and the parameter settings of network model to be improved and optimized. Moreover, the network model is applied to the classification and recognition of Antarctic hydrological features and compared with some existing classification methods. The novelty of this paper mainly includes two aspects, i.e., the one is that the design and construction of the structure of deep neural network based on deep learning method are performed, namely, connection, weight, calculator, learning training of network, and other design. The other is classifying hydrological characteristics of Pritz Bay in Antarctica's images by the DCNN. The results show that the correct recognition rate of the model method constructed by this paper is the highest. Finally, some problems in the current research are briefly summarized and discussed, and the new direction in the future development is forecasted.