Search Results (489)

As lunar exploration missions advance, the need for safe and sustainable in situ energy systems has become increasingly critical. This study investigates the thermoelectric performance of Bi₂Te₃-based thermoelectric materials under the natural temperature variations on the lunar surface, aiming to illustrate the potential of thermoelectric generation technology in power supply for a crewed moon base. A numerical approach was employed to assess the energy conversion behavior and optimize the geometric design of a thermoelectric module couple consisting of a P-leg and N-leg. The results indicate that Bi₂Te₃-based modules exhibit promising functionality under cryogenic conditions, highlighting their potential as an in situ power source during the long lunar night. Furthermore, geometric optimization was shown to significantly enhance the overall thermoelectric performance. The present study illustrates that TEG technology offers a viable pathway toward reliable energy generation in extreme lunar environments, supporting future mission sustainability. Full article

Lunar dust remains one of the most critical unresolved challenges to long-duration lunar missions. Its sharp, abrasive, and electrostatically charged particles are easily inhaled and can penetrate deep into the lungs, reaching the bloodstream and the brain. Despite airlocks and HEPA filtration systems, dust will inevitably infiltrate lunar habitats and threaten astronaut health. We present a novel patent protected helmet design. This system uses a multilayered, synergistic mitigation approach combining mechanical and electrostatic defenses. The mechanical system delivers HEPA-filtered, ionized air across the user’s face, while the electrostatic barrier repels charged particles away from the respiratory zone. These two systems work together to prevent dust from entering the user’s breathing space. Designed for use inside lunar habitats, this helmet represents a potential solution to an unaddressed, life-threatening problem. It allows astronauts to eat, talk, and sleep while maintaining a protected respiratory zone and provides targeted inhalation-level protection in an environment where dust exposure is otherwise unavoidable. This concept is presented at Technology Readiness Level 2 (TRL 2) to prompt early engagement and feedback from the scientific and engineering communities. Full article

Spaceflight imposes unique physiological stressors that profoundly disrupt immune regulation, including impaired lymphocyte activation, latent viral reactivation, and chronic low-grade inflammation. While structured exercise is the cornerstone countermeasure for musculoskeletal and cardiovascular health, current protocols rarely integrate immune endpoints into their design. This review aims to synthesize current evidence on the immunological effects of exercise in spaceflight and propose a novel framework for immune-focused physical activity guidelines tailored to long-duration missions. Evidence indicates that exercise intensity and modality critically determine immune outcomes. Acute strenuous exercise may transiently suppress immunity via cortisol and reactive oxygen species pathways, whereas chronic moderate-to-vigorous training enhances immune surveillance, reduces systemic inflammation, and supports T-cell and NK-cell function. Exerkines such as IL-15, IL-7, and irisin emerge as central mediators of exercise-induced immunomodulation, with potential applications for spaceflight countermeasures. Incorporating immune health into exercise guidelines represents a necessary paradigm shift for astronaut care. A structured framework—emphasizing aerobic, resistance, and HIIT modalities; moderate-to-vigorous intensity; daily training; immune biomarker monitoring; and integration with nutrition and sleep—can enhance resilience against infection, viral reactivation, and cancer risk. Immune-focused countermeasures will be essential to safeguard astronaut health and ensure mission success on future deep-space expeditions. Full article

This paper presents a resilient and energy-efficient framework for jamming mitigation in cluster-based wireless sensor networks (WSNs), addressing a critical vulnerability in hostile or interference-prone environments. The proposed approa ch integrates dynamic cluster reorganization, adaptive MAC-layer behavior, and multipath routing strategies to restore communication capabilities and sustain network functionality under jamming conditions. The framework is evaluated across heterogeneous topologies using Zigbee and Bluetooth Low Energy (BLE); both stacks were validated in a physical testbed with matched jammer and traffic conditions, while simulation was used solely to tune parameters and support sensitivity analyses. Results demonstrate significant improvements in Packet Delivery Ratio, end-to-end delay, energy consumption, and retransmission rate, with BLE showing particularly high resilience when combined with the mitigation mechanism. Furthermore, a comparative analysis of routing protocols including AODV, GAF, and LEACH reveals that hierarchical protocols achieve superior performance when integrated with the proposed method. This framework has broader applicability in mission-critical IoT domains, including environmental monitoring, industrial automation, and healthcare systems. The findings confirm that the framework offers a scalable and protocol-agnostic defense mechanism, with potential applicability in mission-critical and interference-sensitive IoT deployments. Full article

This paper introduces a power-driven systems engineering methodology for the early-phase design of highly miniaturized satellites: PlanarSats. We derive an analytical framework linking power requirements, contingency policies, solar-cell performance, and subsystem integration to determine the absolute minimum satellite size. Through idealized and detailed case studies, we explore the trade-offs inherent in subsystem selection and integration constraints. Sensitivity analysis identifies critical factors affecting minimum area and operational envelopes. Our framework provides a clear tool for balancing functionality, reliability, and physical limits in next-generation ultra-small satellite missions. Full article

Hybrid underwater gliders (HUGs) combine buoyancy-driven gliding with propeller-assisted propulsion, offering extended endurance and enhanced mobility for complex underwater missions. However, precise depth control remains challenging due to system uncertainties, environmental disturbances, and inadequate adaptability of conventional control methods. This study proposes a novel optimized line-of-sight active disturbance rejection control (OLOS-ADRC) strategy for HUG depth tracking in the vertical plane. First, an Optimized Line-of-Sight (OLOS) guidance dynamically adjusts the look-ahead distance based on real-time cross-track error and velocity, mitigating error accumulation during path following. Second, a Tangent Sigmoid-based Tracking Differentiator (TSTD) enhances the disturbance estimation capability of the Extended State Observer (ESO) within the Active Disturbance Rejection Control (ADRC) framework, improving robustness against unmodeled dynamics and ocean currents. As a critical step before costly sea trials, this study establishes a high-fidelity simulation environment to validate the proposed method. The comparative experiments under gliding and hybrid propulsion modes demonstrated that OLOS-ADRC has significant advantages: the root mean square error (RMSE) for depth tracking was reduced by 83% compared to traditional ADRC, the root mean square error for pitch angle was decreased by 32%, and the stabilization time was shortened by 14%. This method effectively handles ocean current interference through real-time disturbance compensation, providing a reliable solution for high-precision HUG motion control. The simulation results provide a convincing foundation for future field validation in oceanic environments. Despite these improvements, the study is limited to vertical plane control and simulations; future work will involve full ocean trials and 3D path tracking. Full article

This review explores recent advancements in morphing structures for Unmanned Ariel Vehicles (UAVs), focusing on mechanical designs and control strategies of quadrotors that enable real-time geometric reconfiguration. Morphing mechanisms, ranging from closed-loop linkages to bioinspired and compliant structures, are evaluated in terms of adaptability, actuation simplicity, and flight stability. Control approaches, including model predictive control, reinforcement learning, and sliding mode control, are analyzed for their effectiveness in handling dynamic morphology. The review also highlights key morphing wing concepts such as GNATSpar and Zigzag Wingbox, which enhance aerodynamic efficiency and structural flexibility. A novel concept featuring an inverted slider-crank mechanism (ISCM) is introduced, enabling dual-mode UAV operation for both aerial and terrestrial missions, which is particularly useful in scenarios like wildfire suppression where stability and operation longevity are crucial. This study emphasizes the importance of integrated design approaches that align mechanical transformation with adaptive control. Critical gaps in real-world testing, swarm coordination, and scalable morphing architectures are identified, suggesting future research directions for developing robust, mission-adaptive UAV systems. Full article

Addressing the real-time control challenges within large-scale, complex resource-constrained project scheduling, this paper investigates control strategies to ensure the on-time initiation of critical task nodes during the execution of aviation cluster mission plans in the presence of disturbances. Conventional resource-constrained project scheduling problem (RCPSP) models typically treat task start times as the primary decision variables, overlooking the intrinsic link between task duration and resource allocation. Moreover, their reliance on intelligent optimization algorithms struggles to simultaneously balance solution accuracy and computational efficiency, thus failing to meet the demands of precise, real-time control. This paper proposes a real-time project schedule control system with the primary objective of preventing delays in critical tasks. The system aims to maximize the remaining anti-disturbance capacity under resource constraints, and establishes five control constraints tailored to the practical problem’s characteristics. The limitations of traditional approaches mainly lie in the fact that they take the start time of each task as the decision variable. When the scale of task quantity in the project is large, the decision dimension increases exponentially; meanwhile, the start times of various tasks are interdependent, leading to extremely complex constraint relationships. To overcome the limitations of traditional methods, this paper introduces a precise control method based on the Critical Path Transform Tree (CPTT). This method takes task duration as the decision variable, calculates the start time of each task using a recursive formula, and integrates expert heuristic knowledge to transform the dynamic network schedule from a “black box” to a “gray box” model. It effectively addresses the technical challenge of reverse mapping in the recursive formula, ultimately realizing precise and real-time control of the project schedule. The simulation results show that while maintaining high solution accuracy, the computational efficiency of the proposed control method is significantly improved to 1.6 s—compared with an average of 6.9 s for the adaptive differential evolution algorithm—thus verifying its effectiveness and practicality in real-time control applications. Full article

The development of efficient and reliable trajectory-planning strategies for swarms of unmanned aerial vehicles (UAVs) is an increasingly important area of research, with applications in surveillance, search and rescue, smart agriculture, defence operations, and communication networks. This article provides a comprehensive and critical review of the various techniques available for UAV swarm trajectory planning, which can be broadly categorised into three main groups: traditional algorithms, biologically inspired metaheuristics, and modern artificial intelligence (AI)-based methods. The study examines cutting-edge research, comparing key aspects of trajectory planning, including computational efficiency, scalability, inter-UAV coordination, energy consumption, and robustness in uncertain environments. The strengths and weaknesses of these algorithms are discussed in detail, particularly in the context of collision avoidance, adaptive decision making, and the balance between centralised and decentralised control. Additionally, the review highlights hybrid frameworks that combine the global optimisation power of bio-inspired algorithms with the real-time adaptability of AI-based approaches, aiming to achieve an effective exploration–exploitation trade-off in multi-agent environments. Lastly, the article addresses the major challenges in UAV swarm trajectory planning, including multidimensional trajectory spaces, nonlinear dynamics, and real-time adaptation. It also identifies promising directions for future research. This study serves as a valuable resource for researchers, engineers, and system designers working to develop UAV swarms for real-world, integrated, intelligent, and autonomous missions. Full article

Maintaining physiologically controlled conditions during the transport of biological experiments remains a long-standing but under-addressed challenge in spaceflight operations. Pre-launch thermal or mechanical stress induce artefacts that compromise the interpretation of biological responses to space conditions. Existing transport systems are limited to basic heating of small sample containers and lack the capability to power and protect full experimental hardware during mission-critical phases. A modular transport incubator was developed and validated that combines active thermal regulation, battery-buffered power management, and mechanical protection in a compact, field-deployable platform. It enables autonomous environmental conditioning of complex biological payloads and continuous operation of integrated scientific instruments during ground-based transport and recovery. Validation included controlled experiments under sub-zero ambient temperatures, demonstrating rapid warm-up, stable thermal regulation, and uninterrupted autonomous performance. A steady-state finite difference thermal model was experimentally validated across 21 boundary conditions, enabling predictive power requirement estimation for mission planning. Field deployments during multiple MAPHEUS^® sounding rocket campaigns confirmed functional robustness under wind, snow, and airborne recovery scenarios. The system closes a critical infrastructure gap in spaceflight logistics. Its validated performance, modular architecture, and proven operational readiness establish it as an enabling platform for standardized, reproducible ground handling of biological payloads and experiment hardware. Full article

Terrain-referenced navigation (TRN) provides an alternative navigation method for environments with limited GPS availability. This paper proposes a vision-based TRN framework that employs stereo imagery and a rotation-invariant iterative closest point (ICP) algorithm to align reconstructed elevation maps with a terrain elevation database. In contrast to conventional ICP, which is sensitive to camera intrinsic errors, the proposed approach improves robustness at high altitudes. Its feasibility and effectiveness are demonstrated through full-scale flight tests using a Cessna aircraft equipped with an IMU, camera, and barometric altimeter. The results show that the proposed method consistently enhances positioning accuracy and robustness compared with a filter-based approach, particularly under challenging high-altitude conditions where image resolution is reduced. The algorithm proved capable of maintaining reliable performance across varying flight altitudes, demonstrating its robustness under high-altitude conditions. This study establishes the novelty of integrating rotation-invariant ICP with vision-based TRN and provides real-world validation through actual flight testing. The findings offer valuable implications for future research and potential applications in unmanned aerial vehicles and long-range guided systems, where passive and GPS-independent navigation is critical for mission success. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in environmental monitoring, logistics, and precision agriculture. Efficient and reliable path planning is particularly critical for UAV systems operating in 3D continuous environments with multiple obstacles. However, single-UAV systems are often inadequate for such environments due to limited payload capacity, restricted mission coverage, and the inability to execute multiple tasks simultaneously. To overcome these limitations, multi-UAV collaborative systems have emerged as a promising solution, yet coordinating multiple UAVs in high-dimensional 3D continuous spaces with complex obstacles remains a significant challenge for path planning. To address these challenges, this paper proposes a reinforcement-learning-driven multi-strategy continuous ant colony optimization algorithm, QMSR-ACO_R, which incorporates a Q-learning-based mechanism to dynamically select from eight strategy combinations, generated by pairing four constructor selection strategies with two walk strategies. Additionally, an elite waypoint repair mechanism is introduced to improve path feasibility and search efficiency. Experimental results demonstrate that QMSR-ACO_R outperforms seven baseline algorithms, reducing average path cost by 10–60% and maintaining a success rate of at least 33% even in the most complex environments, whereas most baseline algorithms fail completely with a success rate of 0%. These results highlight the algorithm’s robustness, adaptability, and efficiency, making it a promising solution for complex multi-UAV path planning tasks in obstacle-rich 3D environments. Full article

This paper addresses the technical requirements and challenges encountered in the design and development of a customised power electronics board for a stratospheric balloon payload. This board includes power conversion and distribution to critical components (e.g., FPGAs and a ±4 kV power supply), as well as the pressurised enclosure designed to house these components along with other essential electronics. These systems were part of two scientific instruments onboard SUNRISE III, a high-altitude solar observatory launched in July 2024 from ESRANGE (Kiruna, Sweden), with a floating trajectory over the Arctic Circle. The SUNRISE III mission, based on a stratospheric balloon, was carried out by an international consortium of research institutions from Germany, Spain, Japan, and the United States, and in collaboration with NASA’s CSBF and the Swedish Space Corporation. Furthermore, this work presents telemetry data from the pressure sensing system of the electronic unit, as well as voltage and current measurements from the power electronics board outputs. These data were recorded during the floating phase of the mission, up to the balloon’s arrival in northern Canada after a successful week of scientific operations. Full article

Autonomous navigation in subterranean mines is hindered by deformable terrain, dust-laden visibility, and densely packed, safety-critical machinery. We propose a systems-oriented navigation framework that embeds semantic priorities into reactive planning for energy-aware autonomy in Robot Operating System (ROS). A lightweight Convolutional Neural Network (CNN) detector fuses RGB-D and LiDAR data to classify obstacles like humans, haul trucks, and debris, writing risk-weighted virtual LaserScans to the local planner so obstacles are evaluated by relevance rather than geometry. By integrating class-specific inflation layers in costmaps within a cyber–physical systems architecture, the system ensures ISO-compliant separation without sacrificing throughput. In Gazebo experiments with three obstacle classes and 60 runs, high-risk clearance increased by 34%, collisions dropped to zero, mission time remained statistically unchanged, and estimated kinematic effort increased by 6% relative to a geometry-only baseline. These results demonstrate effective systems integration and a favorable safety–efficiency trade-off in industrial cyber–physical environments, providing a reproducible reference for scalable deployment in real-world unstructured mining environments. Full article

To address the challenges of autonomous pursuit-evasion in aerospace, particularly in achieving cross-domain generalizability and handling complex terminal constraints, this paper proposes a generalizable deep reinforcement learning (DRL) framework. The core of the method is a self-play Proximal Policy Optimization (PPO) architecture enhanced by two key innovations. First, a dynamics-agnostic curriculum learning (CL) strategy is employed to accelerate training and enhance policy robustness by structuring the learning process from simple to complex. Second, a transferable prediction-based reward function is designed to provide dense, forward-looking guidance, utilizing forward-state projection to effectively satisfy mission-specific terminal conditions. Comprehensive simulations were conducted in both multi-spacecraft and multi-drone scenarios. In the primary spacecraft validation, the proposed method achieved a 90.7% success rate, significantly outperforming baseline algorithms like traditional PPO and Soft Actor-Critic (SAC). Furthermore, it demonstrated superior robustness, with a performance drop of only 8.3% under stochastic perturbations, a stark contrast to the over 18% degradation seen in baseline methods. The successful application in a multi-drone scenario, including an obstacle-rich environment, confirms the framework’s potential as a unified and robust solution for diverse autonomous adversarial systems. Full article