Search Results (92)

Space debris presents a growing operational risk to spacecraft, especially in low Earth orbit, where collisions can generate further debris and increase future collision probability. Active debris removal and in-orbit servicing require robust close-range perception, but on-board systems are constrained by power, memory, processing capability and the need for reliable real-time operation. This paper investigates convolutional object detection for on-board space debris detection using the SPARK 2022 spacecraft detection dataset. A YOLOv3 detector is fine-tuned and used to evaluate post-training compression through static quantisation and pruning. A lightweight architectural variant, YOLO-DWSC, is also introduced by replacing the YOLOv3-tiny backbone convolutions with depthwise separable convolutions while retaining the detection head. The full-precision YOLOv3 model achieves 0.972 mAP₅₀ and 0.884 mAP_50:95, while 8-bit static quantisation reduces model size from 405 MB to 102 MB with only a small reduction in mAP₅₀, although tighter localisation accuracy is more affected. YOLO-DWSC is much smaller and faster, reaching 256.4 FPS on the tested GPU at 43 MB, but with reduced accuracy. We present this work as a controlled case study rather than an attempt at state-of-the-art SPARK 2022 performance. The original challenge test labels were unavailable, and the experiments therefore use a class-balanced re-split of the labelled data. The results should consequently be interpreted as internally controlled comparisons of compression behaviour, not as leaderboard-comparable benchmark results. Pruning and a two-pass refinement method are also evaluated. The results indicate that simple compression methods can be useful for broad region-of-interest detection, but they also show that claims about on-board deployment require caution. Speed benefits are hardware- and runtime-dependent, and safety-critical proximity operations require evaluation criteria better aligned with full-object containment. Full article

The spacecraft reachable domain has become increasingly important for orbital game analysis due to growing on-orbit activities such as servicing, debris removal, and space situational awareness. This paper provides a comprehensive review of reachable domain theory and its applications in orbital games. A unified mathematical framework is established through three complementary classification dimensions: spatial attributes that distinguish absolute from relative reachable domains, temporal attributes that differentiate free-time from fixed-time reachable domains, and informational attributes that contrast deterministic and predictive reachable domains. Solution methods are systematically reviewed according to this taxonomy, covering analytical and semi-analytical methods, numerical optimization approaches, and geometric and sampling methods for spatial-scale reachable domains, as well as linearized ellipsoidal approximation, exact envelope determination, and fast analytical approximation for time-scale reachable domains. Applications are examined through three representative scenarios: one-on-one pursuit-evasion games, multi-agent cooperative games, and threat-avoidance and defense games. Key limitations of existing approaches are identified, including modeling fidelity, computational efficiency, and scalability under uncertainty. Future research directions are outlined to address these challenges. Full article

Accurate attitude measurement of non-cooperative space targets is essential for on-orbit servicing, active debris removal, and autonomous rendezvous missions. To address the challenges associated with unknown geometry, rapid tumbling motion, and the limited observability of single-view systems, this study proposes a cooperative multi-view measurement framework for three-dimensional reconstruction and attitude estimation. Multiple spacecraft are deployed to form a stable observation configuration, and multi-view image sequences are acquired to strengthen geometric constraints. A learning-based multi-view stereo reconstruction module is used to estimate depth information and reconstruct point clouds, which are further processed through iterative closest point (ICP) registration to derive inter-frame attitude variations. An extended Kalman filter (EKF) is then introduced to improve temporal consistency and suppress measurement noise. Validation is conducted in a numerical simulation using a simplified Fengyun-1 (FY-1) satellite model under a three-spacecraft cooperative fly-around scenario. The simulation results demonstrate that the proposed method achieves high-precision attitude estimation, with attitude errors below $0.3°$ and positional errors within $0.05\mathrm{m}$. Comparative experiments show that the method maintains stable measurement performance under varying observation distances and viewing configurations. The proposed framework provides a reliable and robust measurement solution for dynamic attitude determination of free-tumbling space targets. Full article

This study evaluates the multi-year taxonomic diversity and functional structure of beetle assemblages (Coleoptera) within Sub Arini Park, a historic urban green space in Sibiu, Romania. Following a preliminary baseline and methodological calibration phase in 2023, systematic monitoring was conducted during the 2024 and 2025 seasonal cycles utilizing standardized pitfall trapping across diverse park zones. We explicitly tested two hypotheses: (H1) that long-standing historic park management preserves a resilient and functional insect community structure, and (H2) that local spatial heterogeneity and microhabitat variations significantly drive species distribution. A total of 14,843 individuals belonging to 39 species were analyzed. While total abundance exhibited a slight decrease from 2024 (N = 7112) to 2025 (N = 6551), true diversity metrics (Hill numbers) revealed a significant increase in raw species richness (q = 0) from 30 to 39 species, alongside an enhanced equity of frequent species (Shannon diversity, q = 1, increased from 4.26 to 5.12). Functional guild analysis and multivariate PCA demonstrated a highly structured biocenotic distribution; specialist and hygrophilous species (e.g., Carabus variolosus Fabricius, 1787) were strictly constrained to high-humidity riparian corridors, whereas thermophilous generalists dominated open lawns under high anthropogenic stress. Our spatial analysis identified critical degradation within these heavily managed zones, specifically driven by intensive mowing, soil compaction, and organic debris removal. These findings confirm both hypotheses, revealing that the park operates as a heterogeneous mosaic of ecological refugia rather than a uniform habitat block. Crucially, this study provides a concrete, quantitative basis—derived from empirical thresholds of species richness, abundance shifts, and mapped microhabitat preferences—for implementing nature-based management strategies (such as establishing buffer zones with reduced mowing frequencies, limiting trampling, and retaining coarse woody debris) aimed at mitigating urban biodiversity loss and maintaining vital biological pest control services in Central–Eastern Europe. Full article

Tethered space net robots (TSNRs) offer a flexible and promising configuration for active space debris removal. This study investigates the formation tracking control of TSNR for debris capture in the presence of unknown bounded disturbances. To enhance tracking accuracy, an observer-embedded distributed Lyapunov-based model predictive control (DLMPC) framework is proposed. By integrating real-time estimation errors directly into the predictive model, the framework effectively mitigates the mismatch between predicted and actual system dynamics. In addition, a worst-case contraction constraint is developed to ensure recursive feasibility and robust stability. Numerical experiments demonstrate that the proposed method significantly improves formation tracking precision and enhances resilience against disturbances compared to standard DMPC and auxiliary control strategies. Full article

The advancement of on-orbit servicing and space debris removal missions has established high-precision visual perception for non-cooperative spacecraft as a key research focus. However, the availability of high-quality, diverse spacecraft image datasets is severely limited due to extreme on-orbit imaging conditions, data confidentiality, and morphological diversity of targets, significantly constraining the advancement of data-driven algorithms in this domain. To address this challenge, we propose a relative orbital motion-guided framework for generating multimodal visual data of spacecraft. The proposed method integrates an orbital dynamics model into the synthetic data generation pipeline to simulate typical relative motion patterns between the camera and the target in a realistic orbital environment, thereby generating image sequences characterized by continuous spatiotemporal evolution. Targeting four representative spacecraft—Tiangong, Spacedragon, ICESat, and Cassini—this work simultaneously generates a dataset comprising 8000 samples, each containing four strictly aligned modalities: RGB images, instance segmentation masks, depth maps, and surface normal maps, along with precise 6-degree-of-freedom (6-DoF) pose ground truth. Furthermore, an end-to-end physical image degradation model is developed to accurately simulate the complete imaging chain—from optical diffraction and aberrations to sensor sampling and noise—thereby effectively narrowing the domain gap between synthetic and real data. By addressing three key aspects—physical motion modeling, synchronous multimodal ground truth, and imaging degradation simulation—this work provides a crucial data foundation for training, testing, and validating data-driven on-orbit perception algorithms. Full article

Precise 3D reconstruction of non-cooperative space targets is a prerequisite for active debris removal and on-orbit servicing. However, this task is impeded by severe environmental challenges. Specifically, the limited dynamic range of visible light cameras leads to frequent overexposure or underexposure under extreme space lighting. Compounded by sparse textures and strong specular reflections, these factors significantly constrain reconstruction accuracy. While existing general-purpose feed-forward models such as MapAnything offer efficient inference, their geometric recovery capabilities degrade sharply when facing significant domain shifts. To address these issues, this paper proposes an enhanced 3D reconstruction framework tailored for the space environment named In-Orbit MapAnything. First, to mitigate data scarcity, we construct a high-quality space target dataset incorporating extreme illumination characteristics, which provides comprehensive auxiliary modalities including accurate camera poses and dense point clouds. Second, we propose the SatMap-Adapter module to mitigate feature degradation caused by severe specular reflections. This architecture employs a hierarchical cascade sampling strategy to align multi-level backbone features and utilizes a lightweight adaptive fusion module to dynamically integrate shallow photometric cues, intermediate structural information, and deep semantic features. Finally, we employ a weight-decomposed low-rank adaptation strategy to achieve parameter-efficient fine-tuning while strictly freezing the pre-trained backbone. Experimental results demonstrate that the proposed method decreases the absolute relative error and Chamfer distance by 15.23% and 20.02% respectively compared to the baseline MapAnything model, while maintaining a rapid inference speed. The proposed approach effectively suppresses reconstruction noise on metallic surfaces and recovers fine geometric structures, validating the effectiveness of our feature-enhanced framework in extreme space environments. Full article

The accumulation of space debris in near-Earth orbit, particularly in Low Earth Orbit (LEO), poses an increasing threat to satellite operations, communication infrastructures, and long-term space sustainability. As modern constellations expand and incorporate advanced satellite technologies, including sensing and wireless communications, artificial intelligence-of-things (AIoT), enabled payloads, and edge computing for on-orbit data processing, the risk profile grows. This paper reviews the current debris environment and existing sensing and monitoring techniques, highlights major collision events and deliberate debris-generating activities, and analyzes the role of both governmental and commercial satellite constellations in exacerbating and mitigating the challenges. Emerging space surveillance and tracking (SST) techniques, leveraging radar, optical sensors, and interferometric SAR for enhanced intelligence, surveillance, and reconnaissance (ISR), are highlighted alongside software-defined networking (SDN) approaches and cloud communication technology that enable coordinated debris-avoidance maneuvers. Key international regulatory frameworks, tracking architectures, and mitigation measures, including alignment with ISO 24113 standards, advanced TT&C capabilities, and evolving active debris removal technologies, are examined. The study emphasizes the necessity of a global, interoperable ecosystem that integrates AI/ML (artificial intelligence and machine learning)-driven situational awareness, secure SATCOM links with AJ/LPI/LPD (anti-jamming/low probability of interception/low probability of detection) characteristics, and collaborative protocols among space agencies, commercial operators, and regulatory bodies to ensure the sustainable use of orbital space for future generations. Full article

Space robots operate in unconventional environments, which places specific demands on their mechanical, actuation, and control systems. They need to address a variety of challenges in future space exploitation and exploration, such as in-orbit deployment, active debris removal, or servicing operations. Using robots for such applications presents a unique challenge, as a high level of autonomy is required, and the manipulator’s motion affects the position and orientation of the spacecraft. The article presents basic theoretical statements regarding redundancy in space robotics. Various methods for overcoming difficulties in designing, using, and controlling a space robot are considered. Specialized control algorithms based on the null space of the Jacobian matrix and zero reaction maneuvers (ZRMs) are discussed. The review is limited to space robots with one or more arms and does not include mobile and humanoid robots. Furthermore, the primary motion planning algorithms for these systems are evaluated. Redundant space robots are categorized based on their degrees of freedom, number of arms, operational efficiency, primary objectives, and application areas and the most commonly used algorithms for planning movements. The advantages and disadvantages of both redundant and hyper-redundant space robots are analyzed. The objective of this review is to provide a comprehensive overview of the current state and prospects for the development of redundant robots for operation in space conditions. Full article

The global space economy, valued at approximately USD 400–630 billion (depending on definitional scope), is projected to expand rapidly, crossing USD 1 trillion as early as 2032 and reaching up to about USD 1.8 trillion by 2035. This growth has been driven by a surge (a roughly twelvefold increase) in satellite launches over the past decade, transforming Earth’s orbits into an increasingly congested domain plagued by space debris. The proliferation of space junk poses an escalating threat to orbital sustainability, yet effective governance mechanisms remain limited. This paper examines why conventional solutions for managing common-pool resources (command-and-control regulation, Pigouvian taxes, private property rights, allocation of tradable permits, and horizontal governance regimes) are not fully effective or are difficult to implement in addressing the orbital debris problem. Using a system dynamics perspective, the study qualitatively maps hypothesized feedback mechanisms shaping orbital expansion and space debris accumulation. It suggests that, under the assumed causal structure, reinforcing growth loops associated with geopolitical rivalry and commercial cost reductions linked to the New Space paradigm currently dominate over delayed balancing effects arising from the finite nature of orbital space, whose regenerative capacity is progressively degraded. There exists a threshold of exploitation beyond which orbital space effectively behaves as a non-renewable resource. The analysis suggests that, without binding international coordination, meaningful intervention may require the occurrence of a catalyzing crisis—e.g., a localized cascade of orbital object collisions that could transform stakeholder perceptions and enables active debris removal deployment. Full article

A small amount of mass is generally ejected with high exhaust velocities from the surface of materials irradiated by intense laser pulses, so that a net impulse is generated on the target because of momentum conservation. This phenomenon proved to be a potential solution to generate thrust on far objects, with promising application in space debris removal and control of nanosatellites. Among the different tested strategies, the deposition on the surface of the target of a layer transparent to laser radiation results in a considerable increase in the generated impulse, due to the confinement of the expansion of the ablation plume. In this work impulse generation was measured, using aluminum as target, and PVC, SiO₂, TiO₂ and CNCs (cellulose nanocrystals) as confinement layers with thickness $0.3\sim 5 \mathsf{\mu }\mathrm{m}$. The results show that the generated impulses increase with the thickness of the ejected confinement layer. Additionally, the kinetic energy of the confinement layer, for a given material, does not depend on its thickness, but it is affected by the energy dissipation paths during the interaction with the laser pulse, where the strength of substrate–film adhesion and the Young’s modulus of the latter are shown to play an important role. Full article

The rapid accumulation of space debris poses a serious threat to operational spacecraft, with the capture and removal of rapidly tumbling non-cooperative targets being a primary challenge. Non-contact electromagnetic de-tumbling technology is a promising solution due to its enhanced safety. This paper addresses the issue of torque modeling and validation in the electromagnetic de-tumbling process for a specific configuration involving a magnetic dipole and a spherical shell under a symmetrically distributed magnetic field. Based on the principles of electromagnetic induction, an approximate analytical expression for the electromagnetic eddy current torque on a rotating spherical shell within a dipole magnetic field is first derived. A high-fidelity finite element model is then established, which reveals a systematic discrepancy between the initial theoretical model and numerical simulation results. A distance-dependent power-law correction factor is introduced to calibrate the theoretical model, significantly improving its accuracy and reducing the average error to 1.5 percent. Finally, a ground-based experimental platform is designed and implemented. The experimental results demonstrate that the corrected approximate analytical model agrees well with the empirical data, verifying its validity and accuracy under the given conditions and providing a reliable theoretical basis for the design of future space debris de-tumbling controllers. Full article

In this paper, we present a Unified Framework for cross-domain Space drone Pose Estimation (UF-SPE), addressing the simulation-to-reality gap that limits the deployment of deep learning models in real space missions. The proposed UF-SPE framework integrates offline domain generalization with online unsupervised domain adaptation. During offline training, the model relies exclusively on synthetic images. It employs advanced augmentation techniques and a multi-task architecture equipped with Domain Shifting Uncertainty modules to improve the learning of domain-invariant features. In the online phase, normalization layers are fine-tuned using unlabeled real-world imagery via entropy minimization, allowing for the system to adapt to target domain distributions without manual labels. Experiments on the SPEED+ benchmark demonstrate that the UF-SPE achieves competitive accuracy with just 12.9 M parameters, outperforming the comparable lightweight baseline method by 37.5% in pose estimation accuracy. The results validate the framework’s efficacy and efficiency for robust cross-domain space drone pose estimation, indicating promise for applications such as on-orbit servicing, debris removal, and autonomous rendezvous. Full article

End-effectors are becoming increasingly vital in orbital space missions, performing increasingly complex operational tasks. Current on-orbit missions primarily utilize net systems and rigid grippers as manipulators. However, the dynamic analysis of the net system is complicated, and its reliability is insufficient. Moreover, rigid grippers are not impact-resistant, which can lead to the target either rebounding or sustaining damage. This paper designs an adaptive gripper for rapid passive grasping. Adjusting the initial setup of the gripper by altering the cables allows for different degrees of trigger sensitivity to be achieved. The gripper presented in this paper integrates a bistable mechanism with a dual-mode actuation system, achieving performance metrics such as a $16.7\mathrm{ms}$ activation time and a $5.42\mathrm{m}/\mathrm{s}$ capture speed. This combination of rapid passive and controllable active grasping demonstrates a novel and effective solution with significant potential for dynamic on-orbit service and debris removal missions. Full article

Accurate pose estimation of non-cooperative space objects is crucial for applications such as satellite maintenance, space debris removal, and on-orbit assembly. However, monocular pose estimation methods face significant challenges in environments with limited visibility. Different from the traditional pose estimation methods that use images from a single band as input, we propose a novel deep learning-based pose estimation framework for non-cooperative space objects by fusing visible and infrared images. First, we introduce an image fusion subnetwork that integrates multi-scale features from visible and infrared images into a unified embedding space, preserving the detailed features of visible images and the intensity information of infrared images. Subsequently, we design a robust pose estimation subnetwork that leverages the rich information from the fused images to achieve accurate pose estimation. By combining these two subnetworks, we construct the Visible and Infrared Fused Pose Estimation Framework (VIPE) for non-cooperative space objects. Additionally, we present a Bimodal-Vision Pose Estimation (BVPE) dataset, comprising 3,630 visible-infrared image pairs, to facilitate research in this domain. Extensive experiments on the BVPE dataset demonstrate that VIPE significantly outperforms existing monocular pose estimation methods, particularly in complex space environments, providing more reliable and accurate pose estimation results. Full article