In substation lightning rod meter reading data taking, the classical object detection model is not suitable for deployment in substation monitoring hardware devices due to its large size, large number of parameters, and slow detection speed, while is difficult to balance detection accuracy and real-time requirements with the existing lightweight object detection model. To address this problem, this paper constructs a lightweight object detection algorithm, YOLOv5-Meter Reading Lighting (YOLOv5-MRL), based on the improved YOLOv5 model’s speed while maintaining accuracy. Then, the YOLOv5s are pruned based on the convolutional kernel channel soft pruning algorithm, which greatly reduces the number of parameters in the YOLOv5-MRL model while maintaining a certain accuracy loss. Finally, in order to facilitate the dial reading, the dial external circle fitting method is proposed to calculate the dial reading using the circular angle algorithm. The experimental results on the self-built dataset show that the YOLOv5-MRL object detection model achieves a mean average precision of 96.9%, a detection speed of 5 ms/frame, and a model weight size of 5.5 MB, making it better than other advanced dial reading models.

Motion estimation is a major issue in applications of Unmanned Aerial Vehicles (UAVs). This paper proposes an entire solution to solve this issue using information from an Inertial Measurement Unit (IMU) and a monocular camera. The solution includes two steps: visual location and multisensory data fusion. In this paper, attitude information provided by the IMU is used as parameters in Kalman equations, which are different from pure visual location methods. Then, the location of the system is obtained, and it will be utilized as the observation in data fusion. Considering the multiple updating frequencies of sensors and the delay of visual observation, a multi-rate delay-compensated optimal estimator based on the Kalman filter is presented, which could fuse the information and obtain the estimation of 3D positions as well as translational speed. Additionally, the estimator was modified to minimize the computational burden, so that it could run onboard in real time. The performance of the overall solution was assessed using field experiments on a quadrotor system, compared with the estimation results of some other methods as well as the ground truth data. The results illustrate the effectiveness of the proposed method.

In this paper, the problem of non-overshooting tracking control (NOTC) for a class of nonlinear systems with full-state constraints (FSCs) is studied. Firstly, this paper introduces the mapping constraint function to solve the FSC control problem and transform the controlled system into a new nonlinear system. Then, to obtain a closed-loop system that can solve the expression of tracking error, this paper transforms the n-order system into a system in which only the n-th subsystem is nonlinear by coordinate transformation, that is, subsystem 1 to subsystem $n-1$ are linear. Finally, according to the closed-loop system (CLS), the expressions of the first state of CLSs with $n=1$, $n=2$, $n=3$, and $n\ge 4$ are solved, respectively. By analyzing these expressions, a wider range of conditions with NOTC are obtained. This algorithm obtains more conditions with non-overshooting. Compared with the existing results, the algorithm in this paper reduces the conservatism. Finally, the algorithm is applied to the single-link robot system, and the effectiveness of the algorithm is verified. That is, the algorithm in this paper not only makes all signals of the CLS bounded, but also makes the overshoot of the system zero.

While MADRL has demonstrated significant potential in Unmanned Aerial Vehicle (UAV) swarm control, traditional architectures often rely on fixed-dimensional observation spaces. This rigid structural constraint severely limits the swarm’s adaptability in dynamic environments, particularly when facing sudden topological changes such as node failures or dynamic reinforcements. To overcome these limitations, this paper proposes an end-to-end UAV swarm motion control framework incorporating a state-modulated Graph Attention Network (GAT). By modeling the swarm as a dynamic interaction graph, the proposed method dynamically aggregates neighbor features using attention weights modulated by the agents’ real-time kinematic states. Furthermore, a virtual structure combined with an auction mechanism is introduced to achieve precise formation planning and target allocation. Evaluated in the Genesis 3D physics engine, the proposed Prioritized Experience Replay (PER)-MADDPG-Graph Attention Network (GAT) algorithm exhibits superior robustness and spatial adaptability. Extensive experiments, including dynamic node reduction and addition scenarios, confirm that the proposed framework seamlessly maintains swarm configurations without catastrophic policy degradation, outperforming baseline MADRL methods in both convergence speed and control precision.

This work addresses coverage formation control for the Multi-Rover System (MRS) in extraplanetary environments such as Mars or the Moon, where Global Navigation Satellite System (GNSS) signals are unavailable and uncertain self-localization significantly degrades coverage performance. Existing coverage strategies predominantly assume perfect localization, which is unrealistic for GNSS-denied planetary surfaces. This paper presents a novel multi-rover coverage formation control algorithm that combines guaranteed Voronoi partitioning with belief space planning. The core contributions are (i) a guaranteed Voronoi partitioning framework that provides deterministic bounds on true coverage cells under localization uncertainty; (ii) a dual-layer optimization architecture integrating Extended Kalman Filter-based self-localization with centroid-error minimization; and (iii) a belief space planning approach that predicts system state evolution and infers optimal control inputs over a receding horizon. Simulation results under multiple density distributions and rover configurations demonstrate faster convergence and lower coverage cost compared to conventional Lloyd-based methods. By extending existing coverage strategies, our approach supports rapid and adaptive deployment of multi-rover networks in GNSS-limited environments, providing a promising solution for large-scale planetary exploration.

Underwater images captured by sensors suffer from low contrast and blurry details due to the interference of light absorption and scattering in underwater scenes. Good visibility restoration is often desired for practical processing applications. Current image enhancement methods often rely on prior assumptions to reconstruct a clear image without considering the inherent correlation of underwater image degradation, introducing unconsiderable enhancement results. Thus, this paper proposes an underwater enhancement method based on multiple enhanced layers fusion and transmission-driven color restoration, named EFCR, which consists of three key modules: a pixel-based transmission computation (PTC), a multiple-enhanced-layers fusion (MELF), and a transmission-driven color restoration (TCR). First, PTC designs a linear transformation to adjust the saturation and estimates the transmission based on the mapping relationship between the transmission, the brightness, and the saturation, preventing the transmission from being under-estimated. Then, MELF extracts the original details from the luminance channel and enhances these desired details based on the estimated transmission. Meanwhile, adaptive histogram equalization is used to improve the global brightness. Finally, TCR further analyzes the inherent correlation between the transmission and the image degradation, and constructs a compensation factor to adaptively correct the attenuated a and b channels of Lab space, producing a good enhancement result with reasonable brightness and natural colors. Extensive experiments on three underwater image datasets demonstrate the effectiveness and robustness of the proposed method in underwater image restoration. Especially, the average $\overline{r}$ and Blur values of our method at most incline and decline by 99.87% and 10.22%, respectively, which shows our method has obvious advantages in edge enhancement and haze removal. Moreover, our method provides helpful support for color restoration and image salient detection, and also shows good generalization capability for enhancing outdoor hazy images.