Search Results (717)

Non-contact vital signs detection of the survivors based on bio-radar to identify their life states is significant for field search and rescue. However, when transportation is interrupted, rescue workers and equipment are unable to arrive at the disaster area promptly. In this paper, we report a hovering airborne radar for non-contact vital signs detection to overcome this challenge. The airborne radar system supports a wireless data link, enabling remote control and communication over distances of up to 3 km. In addition, a novel framework based on blind source separation is proposed for vital signals extraction. First, range migration caused by the platform motion is compensated for by the envelope alignment. Then, the respiratory waveform of the human target is extracted by the joint approximative diagonalization of eigenmatrices algorithm. Finally, the heartbeat signal is recovered by respiratory harmonic suppression through a feedback notch filter. The field experiment results demonstrate that the proposed method is capable of precisely extracting vital signals with outstanding robustness and adaptation in more cluttered environments. The work provides a technical basis for remote high-resolution vital signs detection to meet the increasing demands of actual rescue applications. Full article

This essay focuses on the Greek adoptees’ search for identity and on the agrafa, or the “unwritten” territories, into which this search penetrates. The Greek adoptees represent an underresearched case study of the postwar intercountry adoption movement (1950–1975). Creating a narrative of the self is key to the adoptees’ identity formation, but their personal narrative is often undermined by stereotypes and denunciations that stunt its development. The research presented here has been guided by questions that interrogate the verdict-making or “sentencing” associated with the adoptees’ identity-shaping process: their sentencing to subjugation by stock opinions, the denouncing of their alternative viewpoints about “rescue” adoptions, and the verdict of their entrapment in feel-good master narratives. This essay also explores broader research questions pertaining to modes of interrogating “historic” adoptions from Greece. It is concerned with the why rather than with the how or the who of the oldest, post-WWII intercountry adoption flows. In what forums and genres (narrative, visual, journalistic, scholarly) are Greek adoption facts and legacies articulated, mediated, and/or materialized? How do memories, both positive and negative, underpin current projects of self-identification and transformation? What are the adoptees’ preferred outlets to speak about embodied experiences, and are those satisfactory? Based on a mixed methods approach, the essay ties these steps in identity growth to the Adoptee Consciousness Model, illustrating the five phases of consciousness that the adoptees may experience throughout their lives. Full article

Language-guided multimodal fusion, which integrates information from both visible and infrared images, has shown strong performance in image fusion tasks. In low-light or complex environments, a single modality often fails to fully capture scene features, whereas fused images enable robots to obtain multidimensional scene understanding for navigation, localization, and environmental perception. This capability is particularly important in applications such as autonomous driving, intelligent surveillance, and search-and-rescue operations, where accurate recognition and efficient decision-making are critical. To enhance the effectiveness of multimodal fusion, we propose a text-guided infrared and visible image fusion network. The framework consists of two key components: an image fusion branch, which employs a cross-domain attention mechanism to merge multimodal features, and a text-guided module, which leverages the CLIP model to extract semantic cues from image descriptions containing visible content. These semantic parameters are then used to guide the feature modulation process during fusion. By integrating visual and linguistic information, our framework is capable of generating high-quality color-fused images that not only enhance visual detail but also enrich semantic understanding. On benchmark datasets, our method achieves strong quantitative performance: SF = 2.1381, Qab/f = 0.6329, MI = 14.2305, SD = 0.8527, VIF = 45.1842 on LLVIP, and SF = 1.3149, Qab/f = 0.5863, MI = 13.9676, SD = 94.7203, VIF = 0.7746 on TNO. These results highlight the robustness and scalability of our model, making it a promising solution for real-world multimodal perception applications. Full article

This study proposes a hierarchical framework with task priority perception for mission planning, to enhance multi-UAV coordination in maritime emergency search and rescue. By establishing a hierarchical decoupling optimization mechanism, the complex multi-region coverage problem is decomposed into two stages: task allocation and path planning. First, a coverage voyage estimation model is constructed based on regional geometric features to provide basic data for subsequent task allocation. Second, an improved multi-objective, multi-population grey wolf optimizer (IM2GWO) is designed to solve the task allocation problem; this integrates adaptive genetic operations and the multi-population coevolutionary mechanism. Finally, a globally optimal coverage path is generated based on the improved dynamic programming (DP). Simulation results indicate that the proposed method effectively reduces total task duration while boosting overall coverage benefits through the aggregation of high-value regions. IM2GWO demonstrates statistically superior performance with respect to the Pareto front distribution index across all test scenarios. Meanwhile, the path planning module based on DP can effectively reduce the overall coverage path cost. Full article

In response to the challenges faced by unmanned aerial vehicles (UAV) in cluttered environments such as forests, ruins, and pipelines, this study introduces a ground–air amphibious UAV specifically designed for personnel search and rescue in complex environments. By innovatively designing and applying a separation cage structure, the UAV’s capabilities for ground movement and aerial flight have been enhanced, effectively overcoming the limitations of traditional single-mode robots operating in narrow or obstacle-dense areas. This design addresses the occlusion issue of sensing components in traditional caged UAVs while maintaining protection for both the UAV itself and the surrounding environment. Additionally, through the innovative design of an H-shaped quadcopter frame skeleton structure, the UAV has gained the ability to perform steady-state aerial flight while also better adapting to the separation cage structure, achieving a reduced energy consumption and significantly improving its operational capabilities in complex environments. The experimental results demonstrate that the UAV prototype, weighing 1.2 kg with a 1 kg payload capacity, achieves a 40 min maximum endurance under full payload conditions at the endurance speed of 10 m/s while performing real-time object detection. The system reliably executes multimodal operations, including stable takeoff, landing, aerial hovering, directional maneuvering, and terrestrial locomotion with coordinated steering control. Full article

The integration of diverse robotic platforms with varying payload capacities is a critical challenge in swarm robotics and autonomous systems. This paper presents a robust, modular framework designed to manage and coordinate heterogeneous swarms of autonomous vehicles, including terrestrial, aerial, and aquatic platforms. Built on the Robot Operating System (ROS) and integrated with C++ and ArduPilot, the framework enables real-time communication, autonomous decision-making, and mission execution across multi-domain environments. Its modular design supports seamless scalability and interoperability, making it adaptable to a wide range of applications. The proposed framework was evaluated through simulations and real-world experiments, demonstrating its capabilities in collision avoidance, dynamic mission planning, and autonomous target reallocation. Experimental results highlight the framework’s robustness in managing UAV swarms, achieving 100% collision avoidance success and significant operator workload reduction, in the tested scenarios. These findings underscore the framework’s potential for practical deployment in applications such as disaster response, reconnaissance, and search-and-rescue operations. This research advances the field of swarm robotics by offering a scalable and adaptable solution for managing heterogeneous autonomous systems in complex environments. Full article

This research presents the development of a novel object detection algorithm designed to identify humans in natural outdoor environments using minimal computational resources. The proposed system, SARGAS, combines a custom convolutional neural network (CNN) classifier with MOG2 background subtraction and partial affine transformations for camera stabilization. A secondary CNN refines detections and reduces false positives. Unlike conventional supervised models, SARGAS is trained in a partially unsupervised manner, learning to recognize feature patterns without requiring labeled data. The algorithm achieved a recall of 93%, demonstrating strong detection capability even under challenging conditions. However, the overall accuracy reached 65%, due to a higher rate of false positives—an expected trade-off when maximizing recall. This bias is intentional, as missing a human target in search and rescue applications carries a higher cost than producing additional false detections. While supervised models, such as YOLOv5, perform well on data resembling their training sets, they exhibit significant performance degradation on previously unseen footage. In contrast, SARGAS generalizes more effectively, making it a promising candidate for real-world deployment in environments where labeled training data is limited or unavailable. The results establish a solid foundation for further improvements and suggest that unsupervised CNN-based approaches hold strong potential in object detection tasks. Full article

Deep learning with unmanned aerial vehicles (UAVs) is transforming maritime search and rescue (SAR) by enabling rapid object identification in challenging marine environments. This study benchmarks the performance of YOLO models for maritime SAR under diverse weather conditions using the SeaDronesSee and AFO datasets. The results show that while YOLOv7 achieved the highest mAP@50, it struggled with detecting small objects. In contrast, YOLOv10 and YOLOv11 deliver faster inference speeds but compromise slightly on precision. The key challenges discussed include environmental variability, sensor limitations, and scarce annotated data, which can be addressed by such techniques as attention modules and multimodal data fusion. Overall, the research results provide practical guidance for deploying efficient deep learning models in SAR, emphasizing specialized datasets and lightweight architectures for edge devices. Full article

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

Unmanned Aerial Vehicles (UAVs) face significant challenges in global path planning within complex terrains, as traditional algorithms (e.g., A*, PSO, APF) struggle to balance computational efficiency, path optimality, and safety. This study proposes a Voronoi–A* fusion algorithm, combining Voronoi-vertex-based rapid trajectory generation with A* supplementary expansion for enhanced performance. First, an adaptive DEM layering strategy divides the terrain into horizontal planes based on obstacle density, reducing computational complexity while preserving 3D flexibility. The Voronoi vertices within each layer serve as a sparse waypoint network, with greedy heuristic prioritizing vertices that ensure safety margins, directional coherence, and goal proximity. For unresolved segments, A* performs localized searches to ensure complete connectivity. Finally, a line-segment interpolation search further optimizes the path to minimize both length and turning maneuvers. Simulations in mountainous environments demonstrate superior performance over traditional methods in terms of path planning success rates, path optimality, and computation. Our framework excels in real-time scenarios, such as disaster rescue and logistics, although it assumes static environments and trades slight path elongation for robustness. Future research should integrate dynamic obstacle avoidance and weather impact analysis to enhance adaptability in real-world conditions. Full article

Localization of mobile robots is crucial for deploying robots in real-world applications such as search and rescue missions. This work aims to develop an accurate localization system applicable to swarm robots equipped only with low-cost monocular vision sensors and visual markers. The system is designed to operate in fully open spaces, without landmarks or support from positioning infrastructures. To achieve this, we propose a localization method based on equilateral triangular formations. By leveraging the geometric properties of equilateral triangles, the accurate two-dimensional position of each participating robot is estimated using one-dimensional lateral distance information between robots, which can be reliably and accurately obtained with a low-cost monocular vision sensor. Experimental and simulation results demonstrate that, as travel time increases, the positioning error of the proposed method becomes significantly smaller than that of a conventional dead-reckoning system, another low-cost localization approach applicable to open environments. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas $ϵ$-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments. Full article

This paper aims to analyze the performance of a tilt-rotor fixed-wing RPAS (Remotely Piloted Aircraft System) using a segmented approach, focusing on a nominal mission for SAR (Search and Rescue) applications. The study employs optimization techniques tailored to each segment to meet power consumption requirements, and the results highlight the accuracy of the physical characterization, which incorporates nonlinear propulsive and aerodynamic models derived from wind tunnel test campaigns. Critical segments for this nominal mission, such as the vertical take off or the transition from vertical to horizontal flight regimes, are addressed to fully understand the performance response of the aircraft. The proposed framework integrates experimental models into trajectory optimization procedures for each segment, enabling a realistic and modular analysis of energy use and aerodynamic performance. This approach provides valuable insights for both flight control design and future sizing iterations of convertible UAVs (Uncrewed Aerial Vehicles). Full article

Accurate prediction of drifting trajectories is essential for improving the operational efficiency of maritime search and rescue (SAR), particularly within the complex geomorphological settings of island and reef regions, such as those in the South China Sea. This study investigates the drift characteristics of life rafts under varying loading conditions across both open-sea and island–reef regions. Comprehensive field experiments were conducted over 15 days in the waters around the Wanshan Archipelago, using advanced instruments to collect wind, current, and drift trajectory data. Based on these observations, two models—the AP98 leeway model and a BP neural network model—were developed and validated. The results show that the AP98 model performs better in open-sea conditions, whereas the BP neural network provides more accurate predictions in island and reef areas with complex environmental factors. A Monte Carlo simulation was also integrated to enhance the robustness of drift area predictions. These findings offer valuable insights into life raft drift behavior in complex marine environments and provide technical support for improving SAR operations in island–reef regions. Full article

Autonomous drones may be called on to perform search-and-rescue operations in environments without access to signals from the global navigation satellite system (GNSS), such as underground mines, subterranean caverns, or confined tunnels. While technology to perform such missions has been demonstrated at events such as DARPA’s Subterranean (Sub-T) Challenge, the hardware deployed for these missions relies on heavy and expensive sensors, such as LiDAR, carried by costly mobile platforms, such as legged robots and heavy-lift multicopters, creating barriers for deployment and training with this technology for all but the wealthiest search-and-rescue organizations. To address this issue, we have developed a custom four-rotor aerial drone platform specifically built around low-cost low-weight sensors in order to minimize costs and maximize flight time for search-and-rescue operations in GNSS-denied environments. We document the various issues we encountered during the building and testing of the vehicle and how they were solved, for instance a novel redesign of the airframe to handle the aggressive yaw maneuvers commanded by the FUEL exploration framework running onboard the drone. The resulting system is successfully validated through a hardware autonomous flight experiment performed in an underground environment without access to GNSS signals. The contribution of the article is to share our experiences with other groups interested in low-cost search-and-rescue drones to help them advance their own programs. Full article