Search Results (37)

Regarding the distortion correction problem of large field of view wide-angle cameras commonly used in railway visual inspection systems, this paper proposes a novel online calibration method for non-specially made cooperative calibration objects. Based on the radial distortion divisor model, first, the spatial coordinates of natural spatial landmark points are constructed according to the known track gauge value between two parallel rails and the spacing value between sleepers. By using the image coordinate relationships corresponding to these spatial coordinates, the coordinates of the distortion center point are solved according to the radial distortion fundamental matrix. Then, a constraint equation is constructed based on the collinear constraint of vanishing points in railway images, and the Levenberg–Marquardt algorithm is used to found the radial distortion coefficients. Moreover, the distortion coefficients and the coordinates of the distortion center are re-optimized according to the least squares method (LSM) between points and the fitted straight line. Finally, based on the above, the distortion correction is carried out for the distorted railway images captured by the camera. The experimental results show that the above method can efficiently and accurately perform online distortion correction for large field of view wide-angle cameras used in railway inspection without the participation of specially made cooperative calibration objects. The whole method is simple and easy to implement, with high correction accuracy, and is suitable for the rapid distortion correction of camera images in railway online visual inspection. Full article

The space flexible netted pocket capture system provides a flexible and stable solution for capturing non-cooperative space objects. This paper investigates the control problem for the capture of non-cooperative targets undergoing motion. A dynamic model of the capturing net is established based on the absolute nodal coordinate formulation (ANCF) and equivalent plate–shell theory. A contact collision force model is developed using a spring–damper model. Subsequently, a feedforward controller is designed based on the estimated collision force from the dynamic model, aiming to compensate for the collision effects between the target and the net. By incorporating the collision estimation data, an extended state observer is designed, taking into account the collision estimation errors and the flexible uncertainties. A sliding mode feedback controller is then designed using the fast terminal sliding mode control method. Finally, simulation analysis of target capture under different motion states is conducted. The results demonstrate that the spacecraft system’s position and attitude average flutter amplitudes are less than ${10}^{-2}$ m and ${10}^{-2}$ deg. In comparison to standard sliding mode control, the designed controller reduces the attitude jitter amplitude by an order of magnitude, thus demonstrating its effectiveness and superiority. Full article

The increasing presence of non-cooperative targets poses significant challenges to the space environment and threatens the sustainability of aerospace operations. Accurate on-orbit perception of such targets, particularly those without cooperative markers, requires advanced algorithms and efficient system architectures. This study presents a hardware–software co-design framework for the pose estimation of non-cooperative targets. Firstly, a two-stage architecture is proposed, comprising object detection and pose estimation. YOLOv5s is modified with a Focus module to enhance feature extraction, and URSONet adopts global average pooling to reduce the computational burden. Optimization techniques, including batch normalization fusion, ReLU integration, and linear quantization, are applied to improve inference efficiency. Secondly, a customized FPGA-based accelerator is developed with an instruction scheduler, memory slicing mechanism, and computation array. Instruction-level control supports model generalization, while a weight concatenation strategy improves resource utilization during convolution. Finally, a heterogeneous DSP–FPGA system is implemented, where the DSP manages data pre-processing and result integration, and the FPGA performs core inference. The system is deployed on a Xilinx X7K325T FPGA operating at 200 MHz. Experimental results show that the optimized model achieves a peak throughput of 399.16 GOP/s with less than 1% accuracy loss. The proposed design reaches 0.461 and 0.447 GOP/s/DSP48E1 for two model variants, achieving a 2× to 3× improvement over comparable designs. Full article

Orbit determination for non-cooperative low Earth orbit (LEO) objects undergoing continuous low-thrust maneuvers remains a significant challenge, particularly for large satellite constellations like Starlink. This paper presents a method that integrates the unscented transformation into a batch filtering framework with an optimized rho-minimum sigma points sampling strategy. The proposed approach uses a reduced dynamics model that considers Earth’s non-spherical gravity and models the combined effects of low-thrust and atmospheric drag as an equivalent along-track acceleration. Numerical simulations under different measurement noise levels, initial state uncertainties, and across multiple satellites confirm the method’s reliable convergence and favorable accuracy, even in the absence of prior knowledge of the along-track acceleration. The method consistently converges within 10 iterations and achieves 24 h position predictions with root mean square errors of less than 3 km under realistic noise conditions. Additional validation using a higher-fidelity model that explicitly accounts for atmospheric drag demonstrates improved accuracy and robustness. The proposed method can provide accurate orbit knowledge for space situational awareness associated with continuously maneuvering Starlink satellites. Full article

Space object detection, as the foundation for ensuring the long-term safe and stable operation of spacecraft, is widely applied in a variety of close-proximity tasks such as non-cooperative target monitoring, space debris avoidance, and spacecraft mission planning. To strengthen the detection capabilities for non-cooperative spacecraft and space debris, a method based on You Only Look Once Version 11 (YOLO11) is proposed in this paper. On the one hand, to tackle the issues of noise and low contrast in images captured by spacecraft, bilateral filtering is applied to remove noise while preserving edge and texture details effectively, and image contrast is enhanced using the contrast-limited adaptive histogram equalization (CLAHE) technique. On the other hand, to address the challenge of small object detection in spacecraft, loss-guided online data augmentation is proposed, along with improvements to the YOLO11 network architecture, to boost detection capabilities for small objects. The experimental results show that the proposed method achieved $99.0\%$ mAP50 (mean Average Precision with an Intersection over Union threshold of 0.50) and $92.6\%$ mAP50-95 on the SPARK-2022 dataset, significantly outperforming the YOLO11 baseline, thereby validating the effectiveness of the proposed method. Full article

Excessively pursuing the comfort of the indoor environment in buildings may increase the energy consumption of operating equipment. A non-cooperative game strategy to solve the above-mentioned problem is proposed in this paper, in which multi-comfort and economic objectives are treated as equal virtual gamers. Firstly, several kinds of electrical equipment in buildings are modeled. Secondly, a visual comfort index is established by measuring the approach, followed by the construction of multi-dimensional comfort expression, including thermal, water, and air quality in indoor environments. Then, based on game theory, the non-cooperative game model of a single entity is built by using economic and multi-comfort objectives as virtual players to avoid subjectivity in multi-objective optimization. To ensure the existence of a Nash equilibrium, the Nikaido–Isoda function is employed to reformulate the payoff function, with strategy spaces allocated based on power differences. Finally, the optimization strategy is solved by using a particle swarm optimization algorithm. The simulation results show that the proposed solution increased comfort by 31.45% and reduced economic costs by 3.89% in comparison to the multi-objective optimization algorithm. Full article

Hyper-redundant space manipulators (HRSMs), with their extensive degrees of freedom, offer a promising solution for complex space operations such as on-orbit assembly and manipulation of non-cooperative objects. A critical challenge lies in achieving stable and effective grasping configurations, particularly when dealing with irregularly shaped objects in microgravity. This study addresses these challenges by developing a segmented hybrid impedance control architecture tailored to multi-point contact scenarios. The proposed framework reduces the contact forces and enhances object manipulation, enabling the secure handling of irregular objects and improving operational reliability. Numerical simulations demonstrate significant reductions in the contact forces during initial engagements, ensuring stable grasping and effective force regulation. The approach also enables precise trajectory tracking, robust collision avoidance, and resilience to external disturbances. The complete non-linear dynamics of the HRSM system are derived using the Kane method, incorporating both the free-space and constrained motion phases. These results highlight the practical capabilities of HRSM systems, including their potential to grasp and manipulate obstacles effectively, paving the way for applications in autonomous on-orbit servicing and assembly tasks. By integrating advanced control strategies and robust stability guarantees, this work provides a foundation for the deployment of HRSMs in real-world space operations, offering greater versatility and efficiency in complex environments. Full article

This study addresses the motion and inertia parameter estimation problem of a torque-free, tumbling, non-cooperative space object (target) under long-term occlusions. To solve this problem, we employ a data-driven Gaussian process (GP) to simulate sensor measurements. In particular, we implement the multi-output GP to predict the projection measurements of a stereo-camera system onboard a chaser spacecraft. A product kernel, consisting of two periodic kernels, is used in the GP models to capture the periodic trends from non-periodic projection data. The initial guesses for the periodicity hyper-parameters of the GP models are intelligently derived from fast Fourier transform (FFT) analysis of the projection data. Additionally, we propose an unscented Kalman filter–Gaussian process (UKF-GP) fusion algorithm for target motion and inertia parameter estimation. The predicted projections from the GP models and their derivatives are used as the pseudo-measurements for UKF-GP during long-term occlusion. Results from Monte Carlo (MC) simulations demonstrate that, for varying tumbling frequencies, the UKF-GP can accurately estimate the target’s motion variables over hundreds of seconds, a capability the conventional UKF algorithm lacks. Full article

Background: As primary care is an important infrastructure for the entire health system, the employment structure choices of family physicians—whether to work in a managed care organization or be self-employed, can impact all effectiveness parameters of healthcare: quality, access, health equity, patients’ experiences, and cost-effectiveness. The aim of this study is to assess the push and pull factors influencing family physicians’ employment choices. Methods: This study employed a qualitative approach to explore the experiences of family physicians (FPs) who choose to work a self-employment practice. We conducted semi-structured interviews with twenty-seven self-employed FPs in Israel, selected through purposive and snowball sampling. The interviews were conducted via Zoom, recorded, and transcribed verbatim. Data analysis followed thematic analysis framework. The analysis yielded 10 themes, which were organized into two categories: pull and push factors. Results: Pull factors, i.e., factors that attract family physicians to become self-employed, included professional self-fulfillment, higher income, professional and business autonomy, working with secretaries according to one’s personal choice, designing the space of the clinic, and flexibility in working hours. Push factors, i.e., factors that demotivated family physicians to work under managed care and pushed them to choose self-employment included low control over the work environment, workload, decreased professional and organizational autonomy, managerial pressures on quality measures, engagement in marketing activities, and tensions with non-medical staff in the Health Maintenance Organization. Conclusions: There are obvious implications of this work for Health Maintenance Organizations’ policy makers. Balancing managerial pressure and tensions between family physicians and non-medical administration and ensuring suitable working conditions increased physicians’ control over the work environment, and professional autonomy may decrease push factors and retain family physicians as Health Maintenance Organization-employed. Understanding pull factors may help to develop a strategy for maximizing cooperation with self-employed family physicians and reinforce physicians’ linkage to the healthcare system’s treatment objectives. Full article

China’s agricultural sector is transitioning from extensive management to intensive management, and land transfer brings about changes in land use and management methods, which may encourage the agricultural sector to enter a sustainable development track, but this mechanism has not been effectively proven. Using the SBM-GML index to construct a green total factor productivity index to measure the level of sustainable agricultural development in each province (or autonomous region or municipality directly under the central government) and provincial panel data from 2010 to 2022, we applied a panel interactive fixed-effects model to empirically test the impact of land transfer on sustainable agricultural development, with a focus on analyzing the heterogeneity and related mechanisms of this impact. The results indicate that (1) land transfer significantly promotes sustainable agricultural development, and this conclusion still held true after robustness tests such as controlling for regional omitted variables, replacing dependent variables, changing the sample size, IV estimation, and GMM estimation. (2) The mechanism testing found that land transfer mainly promotes sustainable agricultural development by increasing the desirable output, and has no significant effect on reducing non-point source pollution. At the same time, land transfer mainly improves the desirable output through factor allocation effects rather than scale operation effects, thereby promoting sustainable agricultural development. (3) The heterogeneity analysis found that the higher the quantile of agricultural development level is, the weaker the role of land transfer in promoting sustainable agricultural development, indicating that land transfer has a greater impact on areas with poor agricultural development foundations, and areas with poor agricultural development foundations are more likely to obtain sustainable development space through land transfer. The impact of different land transfer methods and land transfer objects on sustainable agricultural development was heterogeneous. Compared with non-market transfer methods such as exchange and transfer, market-oriented transfer methods such as leasing and equity had a more significant impact on sustainable agricultural development. Compared to transferring land to ordinary farmers, transferring land to new business entities such as family farms, professional cooperatives, and enterprises can significantly promote sustainable agricultural development. Full article

In this paper, we propose a Bayesian Optimization (BO)-based strategy using the Gaussian Process (GP) for feature detection of a known but non-cooperative space object by a chaser with a monocular camera and a single-beam LIDAR in a close-proximity operation. Specifically, the objective of the proposed Space Object Chaser-Resident Assessment Feature Tracking (SOCRAFT) algorithm is to determine the camera directional angles so that the maximum number of features within the camera range is detected while the chaser moves in a predefined orbit around the target. For the chaser-object spatial incentive, rewards are assigned to the chaser states from a combined model with two components: feature detection score and sinusoidal reward. To calculate the sinusoidal reward, estimated feature locations are required, which are predicted by Gaussian Process models. Another Gaussian Process model provides the reward distribution, which is then used by the Bayesian Optimization to determine the camera directional angles. Simulations are conducted in both 2D and 3D domains. The results demonstrate that SOCRAFT can generally detect the maximum number of features within the limited camera range and field of view. Full article

With the rapid development of urban air traffic, Unmanned Aerial Vehicles (UAVs) are gradually being widely used in cities. Since UAVs are prohibited over important places in Urban Air Mobility (UAM), such as government and airports, it is important to develop air–ground non-cooperative UAV surveillance for air security all day and night. In the paper, an all-time UAV detection algorithm based on visible images during the day and infrared images at night is proposed by our team. We construct a UAV dataset used in urban visible backgrounds (UAV–visible) and a UAV dataset used in urban infrared backgrounds (UAV–infrared). In the daytime, the visible images are less accurate for UAV detection in foggy environments; therefore, we incorporate a defogging algorithm with the detection network that can ensure the undistorted output of images for UAV detection based on the realization of defogging. At night, infrared images have the characteristics of a low-resolution, unclear object contour, and complex image background. We integrate the attention and the transformation of space feature maps into depth feature maps to detect small UAVs in images. The all-time detection algorithm is trained separately on these two datasets, which can achieve 96.3% and 94.7% mAP50 on the UAV–visible and UAV–infrared datasets and perform real-time object detection with an inference speed of 40.16 FPS and 28.57 FPS, respectively. Full article

Ensuring pedestrian safety is one of the most significant challenges for autonomous driving systems in urban scenarios due to the non-cooperative and unpredictable nature of pedestrian movements. To tackle this problem, firstly, we propose a collision avoidance strategy based on entropy-increasing risk perception in a vehicle–pedestrian reaction space. Our approach combines a limited range of reaction space regions with entropy to quantify the risk of pedestrian–vehicle collision. Then, multi-vehicle candidate trajectories are generated using the path and speed sequence method, and the uncertain states of pedestrians are predicted based on the social force model and Markov model accordingly. Finally, to determine the optimal collision avoidance trajectory, we use quantitative reaction-space entropy as a new “cost function” to measure potential risk and perform multi-objective trajectory optimization based on the elitist non-dominated-sorting genetic algorithm region-focused (NSGA-RF) approach. Simulation results show that our proposed strategy can enhance the safety of the planned trajectory interaction between vehicles and pedestrians for autonomous driving under normal and emergency conditions. Full article

The accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possibly unknown, resident space objects. Safety concerns with manned missions and lag times with ground-based control necessitate complete autonomy. This requires robust characterization of the target’s geometry. In this article, we present an approach for mapping geometries of satellites on orbit based on 3D Gaussian splatting that can run on computing resources available on current spaceflight hardware. We demonstrate model training and 3D rendering performance on a hardware-in-the-loop satellite mock-up under several realistic lighting and motion conditions. Our model is shown to be capable of training on-board and rendering higher quality novel views of an unknown satellite nearly 2 orders of magnitude faster than previous NeRF-based algorithms. Such on-board capabilities are critical to enable downstream machine intelligence tasks necessary for autonomous guidance, navigation, and control tasks. Full article

Accurately estimating the pose of spacecraft is indispensable for space applications. However, such targets are generally non-cooperative, i.e., no markers are mounted on them, and they include no parts for operation. Therefore, the detection and measurement of a non-cooperative target is very challenging. Stereovision sensors are important solutions in the near field. In this paper, a rectangular natural feature recognition and pose measurement method for non-cooperative spacecraft is proposed. Solar panels of spacecraft were selected as detection objects, and their image features were captured via stereo vision. These rectangle features were then reconstructed in 3D Cartesian space through parallelogram fitting on the image planes of two cameras. The vertexes of rectangle features were detected and used to solve the pose of a non-cooperative target. An experimental system was built to validate the effectiveness of the algorithm. The experimental results show that the average position measurement error of the algorithm is about 10 mm and the average attitude measurement error is less than 1°. The results also show that the proposed method achieves high accuracy and efficiency. Full article