Search Results (108)

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

In recent years, UAV (unmanned aerial vehicle)-UGV (unmanned ground vehicle) collaborative systems have played a crucial role in emergency disaster rescue. To improve rescue efficiency, heterogeneous network and task chain methods are introduced to cooperatively develop rescue sequences within a short time for collaborative systems. However, current methods also overlook resource overload for heterogeneous units and limit planning to a single task chain in cross-platform rescue scenarios, resulting in low robustness and limited flexibility. To this end, this paper proposes Gemini, a cascaded dual-agent deep reinforcement learning (DRL) framework based on the Heterogeneous Service Network (HSN) for multiple task chains planning in UAV-UGV collaboration. Specifically, this framework comprises a chain selection agent and a resource allocation agent: The chain selection agent plans paths for task chains, and the resource allocation agent distributes platform loads along generated paths. For each mission, a well-trained Gemini can not only allocate resources in load balancing but also plan multiple task chains simultaneously, which enhances the robustness in cross-platform rescue. Simulation results show that Gemini can increase rescue effectiveness by approximately 60% and improve load balancing by approximately 80%, compared to the baseline algorithm. Additionally, Gemini’s performance is stable and better than the baseline in various disaster scenarios, which verifies its generalization. Full article

Maritime vision sensing can be applied to maritime unmanned systems to perform search and rescue (SAR) missions under complex marine environments, as multiple unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are able to conduct vision sensing through the air, the water-surface, and underwater. However, in these vision-based maritime SAR systems, collaboration between UAVs and USVs is a critical issue for successful SAR operations. To address this challenge, in this paper, we propose a long-endurance collaborative SAR scheme which exploits the complementary strengths of the maritime unmanned systems. In this scheme, a swarm of UAVs leverages a multi-agent reinforcement-learning (MARL) method and probability maps to perform cooperative first-phase search exploiting UAV’s high altitude and wide field of view of vision sensing. Then, multiple USVs conduct precise real-time second-phase operations by refining the probabilistic map. To deal with the energy constraints of UAVs and perform long-endurance collaborative SAR missions, a multi-USV charging scheduling method is proposed based on MARL to prolong the UAVs’ flight time. Through extensive simulations, the experimental results verified the effectiveness of the proposed scheme and long-endurance search capabilities. Full article

This paper proposes a two-stage stochastic non-cooperative game model to solve relief supplies procurement and distribution optimization of multiple rescue organizations in crisis rescue. Rescue organizations with limited budgets minimize rescue costs through relief supply procurement, storage, and transportation in an uncertain environment. Under a mild assumption, we establish the existence and uniqueness of the equilibrium point and derive the optimality conditions by using the duality theory, characterizing the saddle point in the Lagrange framework. The problem is further reformulated as a constraint system governed by Lagrange multipliers, and its optimality is characterized by the Karush–Kuhn–Tucker condition. The economic interpretation of the multipliers as shadow prices is elucidated. Numerical experiments verify the effectiveness of the model in cost optimization in crisis rescue scenarios. Full article

Platelet mitochondria have recently been increasingly considered “co-principal” along with platelet growth factors to facilitate tissue regeneration in platelet-rich plasma therapy cooperatively. To develop a convenient method to test this potential, we examined mitochondrial transfer using a simple two-dimensional culture system. Living human platelets were prepared from PRP obtained from 12 non-smoking healthy male adults (age: 28–63 years) and suspended in medium. Platelet lysates were prepared from sonicated platelet suspensions in PBS. After treatment with ultraviolet-C irradiation, a mitochondrial respiration inhibitor, or a synchronized culture reagent, rat dental pulp-derived fibroblasts (RPC-C2A) were co-cultured with platelets or platelet lysates for 24 h. Mitochondrial transfer was evaluated by visualization using a fluorescent dye for mitochondria or an antibody against human mitochondria. Ultraviolet-C-irradiated cells substantially lost their viability, and treatment with living platelets, but not platelet lysates, significantly rescued the damaged fibroblasts. Fibroblast mitochondria appeared to increase after co-culture with resting platelets. Although more microparticles existed around the platelets on the fibroblast surface, the activated platelets did not show significant increases in any parameters of mitochondrial transfer. This simple co-culture system demonstrated mitochondrial transfer between xenogeneic cells, and this phenomenon should be considered as an additional implication in PRP therapy. Full article

Accurate self-localization and mutual state estimation are essential for autonomous aerial swarm operations in cooperative exploration, target tracking, and search-and-rescue missions. However, achieving reliable formation positioning in GNSS-denied environments remains a significant challenge. This paper proposes a UAV formation positioning system that integrates inertial navigation with data link-based relative measurements to improve positioning accuracy. Each UAV independently estimates its flight state in real time using onboard IMU data through an inertial navigation fusion method. The estimated states are then transmitted to other UAVs in the formation via a data link, which also provides relative position measurements. Upon receiving data link information, each UAV filters erroneous measurements, time aligns them with its state estimates, and constructs a relative pose optimization factor graph for real-time state estimation. Furthermore, a data selection strategy and a sliding window algorithm are implemented to control data accumulation and mitigate inertial navigation drift. The proposed method is validated through both simulations and real-world two-UAV formation flight experiments. The experimental results demonstrate that the system achieves a $76\%$ reduction in positioning error compared to using data link measurements alone. This approach provides a robust and reliable solution for maintaining precise relative positioning in formation flight without reliance on GNSS. Full article

The context of this study is the geolocation of signal emitters on the Earth’s surface through satellite platforms able to perform Angle of Arrival (AOA) measurements. This paper provides the theoretical framework to solve the optimization problem for the orbital deployment of the satellites minimizing the variance on the position error estimation with constraints on the line of sight (LOS). The problem is theoretically formulated for an arbitrary number of satellites in Low Earth Orbit (LEO) and target pointing attitude, focusing on minimizing the Position Dilution of Precision (PDOP) metric, providing a methodology for translating mission design requirements into problem formulation. An exemplary numerical application is presented for the operative case of the placement of a second satellite after a first one is launched. Simulation results are on angles of true anomaly, right ascension of the ascending node, and spacing angle, while accounting for orbital radius and emitter latitude. New insights on trends, parameter dependencies, and properties of symmetry and anti-symmetry are presented. The topic is of interest for new technological demonstrators based on CubeSats with AOA payload. Civil applications of interest are on interceptions of non-cooperative signals in activities of spectrum monitoring or search and rescue. Full article

This study aims to improve the accuracy of fire source detection, the efficiency of path planning, and the precision of firefighting operations in drone swarms during fire emergencies. It proposes an intelligent firefighting technology for drone swarms based on multi-sensor integrated path planning. The technology integrates the You Only Look Once version 8 (YOLOv8) algorithm and its optimization strategies to enhance real-time fire source detection capabilities. Additionally, this study employs multi-sensor data fusion and swarm cooperative path-planning techniques to optimize the deployment of firefighting materials and flight paths, thereby improving firefighting efficiency and precision. First, a deformable convolution module is introduced into the backbone network of YOLOv8 to enable the detection network to flexibly adjust its receptive field when processing targets, thereby enhancing fire source detection accuracy. Second, an attention mechanism is incorporated into the neck portion of YOLOv8, which focuses on fire source feature regions, significantly reducing interference from background noise and further improving recognition accuracy in complex environments. Finally, a new High Intersection over Union (HIoU) loss function is proposed to address the challenge of computing localization and classification loss for targets. This function dynamically adjusts the weight of various loss components during training, achieving more precise fire source localization and classification. In terms of path planning, this study integrates data from visual sensors, infrared sensors, and LiDAR sensors and adopts the Information Acquisition Optimizer (IAO) and the Catch Fish Optimization Algorithm (CFOA) to plan paths and optimize coordinated flight for drone swarms. By dynamically adjusting path planning and deployment locations, the drone swarm can reach fire sources in the shortest possible time and carry out precise firefighting operations. Experimental results demonstrate that this study significantly improves fire source detection accuracy and firefighting efficiency by optimizing the YOLOv8 algorithm, path-planning algorithms, and cooperative flight strategies. The optimized YOLOv8 achieved a fire source detection accuracy of 94.6% for small fires, with a false detection rate reduced to 5.4%. The wind speed compensation strategy effectively mitigated the impact of wind on the accuracy of material deployment. This study not only enhances the firefighting efficiency of drone swarms but also enables rapid response in complex fire scenarios, offering broad application prospects, particularly for urban firefighting and forest fire disaster rescue. Full article

The cooperative patrol control of multiple unmanned surface vehicles (Multi-USVs) in dynamic aquatic environments presents significant challenges in global coverage efficiency and system robustness. The study proposes a cooperative patrol control algorithm for multiple unmanned surface vehicles (Multi-USVs) based on a hybrid embedded task state information model and reward reshaping techniques, addressing global coverage challenges in dynamic aquatic environments. By integrating patrol, collaboration, and obstacle information graphs, the algorithm generates kinematically feasible control actions in real time and optimizes the exploration-cooperation trade-off through a dense reward structure. Simulation results demonstrate that the algorithm achieves 99.75% coverage in a 1 km × 1 km task area, reducing completion time by 23% and 74% compared to anti-flocking and partition scanning algorithms, respectively, while maintaining collision rates between agents (CRBAA) and obstacles (CRBAO) below 0.15% and 0.5%. Compared to DDPG, SAC, and PPO frameworks, the proposed training framework (TFMUSV) achieves 28% higher rewards with 40% smaller fluctuations in later training stages. This study provides an efficient and reliable solution for autonomous monitoring and search-rescue missions in complex aquatic environments. Full article

The cooperative control of multiple autonomous surface vehicles (ASVs) is a critical area of research due to its significant applications in maritime operations, such as search and rescue and environmental monitoring. However, challenges such as communication delays and dynamic topologies often hinder stable cooperative control in practical scenarios. This study addresses these challenges by developing a formation control method based on consensus theory, focusing on both formation control and time delay. First, a simplified ASV characteristic model is established, and a basic consensus control algorithm is designed and analyzed for stability, considering different communication topologies. Then, to handle delays, the formation control method is extended, and the stability of the revised algorithm is rigorously proven using the Lyapunov function. Simulation results demonstrate that the proposed control strategy effectively maintains formations, even in the presence of communication delays. In the end, comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed controller. Simulation results demonstrate that the proposed control strategy effectively maintains formations, even in the presence of communication delays, with a convergence time of approximately 100 s and a formation error stabilizing at around 7 m. This research lays a foundation for more reliable cooperative control systems for ships, with potential applications in a variety of maritime and autonomous systems. Full article

The presented article discusses the possibilities and methods of carrying out evacuation works in the event of an emergency associated with slope deformation in the built-up area of Šalgovík, Slovak Republic. From the point of view of extraordinary events, slope deformations are a negative phenomenon for every country. Besides the most serious natural disasters such as floods, landslides and earthquakes, slope deformations are in third place in terms of the extent of direct or indirect damage. Moreover, for the above reasons, the presented article discusses the possibilities of area evacuation in the event of an emergency in a given built-up area, where, as described in the article, it is a location that is susceptible to slope deformation. Given that it is a built-up area that is not stabilized for slope deformations and is also active, the article explains the activities of the Integrated Rescue System components in the event of an emergency in the said area. The aim was also to carry out a widespread evacuation, which has different characteristics than normal evacuations in the case of other emergencies since a large part of the territory with a certain number of inhabitants is affected. It should be noted that the evacuation of the said territory must be carried out in a rapid time frame so that the consequences for health and human life are minimal, which is explained in the present article. The activities the individual rescue services perform to carry out the evacuation will have to be conducted in a different way than normal, and for this reason, the cooperation and activities required are different from the activities normally carried out. Full article

Multi-robot systems (MRSs) are widely applied in the fields of joint search and rescue, exploration, and carrying. To achieve cooperative tasks and guarantee physical safety, the robots should avoid inter-robot collisions as well as robot–obstacle collisions. However, the collision/obstacle avoidance task usually conflicts with the given cooperative task, which poses a significant challenge for the achievement of multi-robot cooperative tasks. This paper provides a review of the state-of-the-art results in the collision/obstacle avoidance cooperative control of MRSs. Specifically, the latest developments of collision/obstacle avoidance cooperative control are summarized according to different planning strategies and classified into three categories: (1) offline planning; (2) receding horizon planning; and (3) reactive control. Furthermore, specific design solutions for existing reference/command governors are highlighted to demonstrate the latest research advances. Finally, several challenging issues are discussed to guide future research. Full article

With the widespread application of unmanned aerial vehicles (UAV) in surveying, disaster search and rescue, agricultural spraying, war reconnaissance, and other fields, coverage path planning is one of the most important problems to be explored. In this paper, a large-area coverage path planning (CCP) method based on vehicle–UAV collaboration is proposed. The core idea of the proposed method is adopting a divide-and conquer-strategy to divide a large area into small areas, and then completing efficient coverage scanning tasks through the collaborative cooperation of vehicles and UAVs. The supply points are generated and adjusted based on the construction of regular hexagons and a Voronoi diagram, and the segmentation and adjustment of sub-areas are also achieved during this procedure. The vehicle paths are constructed based on the classical ant colony optimization algorithm, providing an efficient way to traverse all supply points within the coverage area. The classic zigzag CCP method is adopted to fill the contours of each sub-area, and the UAV paths collaborate with vehicle supply points using few switching points. The simulation experiments verify the effectiveness and feasibility of the proposed vehicle–UAV collaboration CCP method, and two comparative experiments demonstrate that the proposed method excels at large-scale CCP scenarios, and achieves a significant improvement in coverage efficiency. Full article

Flapping wing micro aerial vehicles (FWMAVs) are recognized for their significant potential in military and civilian applications, such as military reconnaissance, environmental monitoring, and disaster rescue. However, the lack of takeoff and landing capabilities, particularly in landing behavior, greatly limits their adaptability to the environment during tasks. In this paper, the purple stem beetle (Sagra femorata), a natural flying insect, was chosen as the bionic research object. The three-dimensional reconstruction models of the beetle’s three thoracic legs were established, and the adhesive mechanism of the thoracic leg was analyzed. Then, a series of bionic design elements were extracted. On this basis, a hook-pad cooperation bionic deployable landing mechanism was designed, and mechanism motion, mechanical performance, and vibration performance were studied. Finally, the bionic landing mechanism model can land stably on various contact surfaces. The results of this research guide the stable landing capability of FWMAVs in challenging environments. Full article

The Global Positioning System (GPS) has revolutionized navigation in modern society. However, the susceptibility of GPS signals to interference and obstruction poses significant navigational challenges. This paper introduces a GPS-denied method based on scene image coordinates instead of real-time GPS signals. Our approach harnesses advanced image feature-recognition techniques, employing an enhanced scale-invariant feature transform algorithm and a neural network model. The recognition of prominent scene features is prioritized, thus improving recognition speed and precision. The GPS coordinates are extracted from the best-matching image by juxtaposing recognized features from the pre-established image database. A Kalman filter facilitates the fusion of these coordinates with inertial measurement unit data. Furthermore, ground scene recognition cooperates with its aerial counterpart to overcome specific challenges. This innovative idea enables heterogeneous collaboration by employing coordinate conversion formulas, effectively substituting traditional GPS signals. The proposed scheme may include military missions, rescues, and commercial services as potential applications. Full article