Pneumonia is a respiratory infection that affects the lungs. The symptoms of viral and bacterial pneumonia are similar. In order to improve automatic detection efficiency regarding X-ray images of pneumonia, this paper, we propose a novel pneumonia detection method based on the Fast-YOLO network model. First, we re-annotated the open-source dataset of MIMIC Chest X-ray pneumonia, enhancing the model’s adaptability to complex scenes by incorporating Mixup, Mosaic, and Copy–Paste augmentation methods. Additionally, CutMix and Random Erasing were introduced to increase data diversity. Next, we developed a lightweight FASPA Fast Pyramid Attention Mechanism and designed the Fast-YOLO network based on this mechanism to effectively address the complex features in pneumonia X-ray images, such as low contrast and an uneven distribution of local lesions. The Fast-YOLO network improves upon the YOLOv11 architecture by replacing the C3k2 module with the FASPA attention mechanism, significantly reducing the network’s parameter count while maintaining detection performance. Furthermore, the Fast-YOLO network enhances feature extraction capabilities when handling scenes with geometric deformations, multi-scale features, and dynamic changes. It expands the receptive field, thereby balancing computational efficiency and accuracy. Finally, the experimental results demonstrate that the Fast-YOLO network, compared to traditional convolutional neural network methods, can effectively identify pneumonia regions and localize lesions in pneumonia X-ray image detection tasks, achieving significant improvements in FPS, precision, recall, mAP @0.5, and mAP @0.5:0.95. This confirms that Fast-YOLO strikes a balance between computational efficiency and accuracy. The network’s excellent generalization capability across different datasets has been validated, showing the potential to accelerate the pneumonia diagnostic process for clinicians and enhance diagnostic accuracy.

The precise localization of unmanned ground vehicles (UGVs) in industrial parks without prior GPS measurements presents a significant challenge. Simultaneous localization and mapping (SLAM) techniques can address this challenge by capturing environmental features, using sensors for real-time UGV localization. In order to increase the real-time localization accuracy and efficiency of UGVs, and to improve the robustness of UGVs’ odometry within industrial parks—thereby addressing issues related to UGVs’ motion control discontinuity and odometry drift—this paper proposes a tightly coupled LiDAR-IMU odometry method based on FAST-LIO2, integrating ground constraints and a novel feature extraction method. Additionally, a novel maintenance method of prior maps is proposed. The front-end module acquires the prior pose of the UGV by combining the detection and correction of relocation with point cloud registration. Then, the proposed maintenance method of prior maps is used to hierarchically and partitionally segregate and perform the real-time maintenance of the prior maps. At the back-end, real-time localization is achieved by the proposed tightly coupled LiDAR-IMU odometry that incorporates ground constraints. Furthermore, a feature extraction method based on the bidirectional-projection plane slope difference filter is proposed, enabling efficient and accurate point cloud feature extraction for edge, planar and ground points. Finally, the proposed method is evaluated, using self-collected datasets from industrial parks and the KITTI dataset. Our experimental results demonstrate that, compared to FAST-LIO2 and FAST-LIO2 with the curvature feature extraction method, the proposed method improved the odometry accuracy by 30.19% and 48.24% on the KITTI dataset. The efficiency of odometry was improved by 56.72% and 40.06%. When leveraging prior maps, the UGV achieved centimeter-level localization accuracy. The localization accuracy of the proposed method was improved by 46.367% compared to FAST-LIO2 on self-collected datasets, and the located efficiency was improved by 32.33%. The z-axis-located accuracy of the proposed method reached millimeter-level accuracy. The proposed prior map maintenance method reduced RAM usage by 64% compared to traditional methods.

Ultra-wideband (UWB) technology has been widely applied to indoor positioning due to its high-precision ranging. In practical indoor scenarios, inertial measurement units (IMUs) are commonly fused with UWB to suppress measurement errors caused by non-line-of-sight (NLOS) propagation. Frequent switching between line-of-sight (LOS) and NLOS conditions easily generates severe outliers in UWB ranging data. Furthermore, the conventional extended Kalman filter (EKF) lacks an adaptive adjustment mechanism for innovation covariance, which significantly reduces the positioning robustness of the fusion system. To address the above issues, this paper establishes a tightly coupled UWB/IMU positioning model for complex indoor scenarios and proposes an improved robust adaptive extended Kalman filter (IR-AEKF) algorithm. After calibrating UWB ranging errors, IMU bias, and scale factor errors, the system state equation and UWB ranging observation model are constructed under the tightly coupled fusion framework. On this basis, innovation statistics are leveraged to incorporate Huber-type weights and adaptive scaling factors that dynamically tune the observation noise covariance matrix. Meanwhile, a ranging outlier elimination threshold is set to suppress gross errors induced by NLOS interference. Experimental results demonstrate that the IR-AEKF can effectively suppress disturbances arising from abnormal ranging outliers. In dynamic positioning tests, the mean positioning errors along the X-axis and Y-axis are 0.032 m and 0.035 m, respectively. The IR-AEKF provides an effective technical solution for UWB/IMU fusion positioning in complex indoor environments.

To address the challenges of formation maintenance, collision avoidance, and cooperative navigation for multi-agent systems in shared environments, a spatio-temporal trajectory optimization method under global structural constraints and A attention-enhanced graph neural network (AE-GNN)-based cooperative navigation strategy are proposed. First, a differentiable formation similarity metric based on graph Laplacian theory is constructed to characterize the intrinsic geometric structure of formations, which is invariant to translation, rotation, and scale transformations. Based on this metric, a distributed trajectory optimization framework is developed by jointly considering formation maintenance, obstacle avoidance, inter-agent collision avoidance, and trajectory smoothness. Furthermore, an adaptive formation constraint adjustment mechanism based on environmental passage margin is introduced, allowing agents to relax rigid formation constraints in narrow passages and gradually recover the desired formation after obstacle avoidance. To improve cooperative decision-making in densely cluttered environments, an AE-GNN navigation method is proposed, where an attention mechanism dynamically assigns higher weights to critical neighboring agents during feature aggregation, enhancing cooperative information interaction and collision avoidance capability. Simulation and physical experiments validate the effectiveness of both proposed methods. The trajectory optimization method achieves a success rate of 97.5% in dense-obstacle formation-navigation simulations, while AE-GNN demonstrates effective cooperative navigation and collision avoidance in multi-agent scenarios.