Multi-agent pathfinding has been extensively studied by the robotics and artificial intelligence communities. The classical algorithm, conflict-based search (CBS), is widely used in various real-world applications due to its ability to solve large-scale conflict-free paths. However, classical CBS assumes discrete time–space planning and overlooks physical constraints in actual scenarios, making it unsuitable for direct application in unmanned aerial vehicle (UAV) swarm. Inspired by the decentralized planning and centralized conflict resolution ideas of CBS, we propose, for the first time, an optimal and efficient UAV swarm motion planner that integrates state lattice with CBS without any underlying assumption, named SL-CBS. SL-CBS is a two-layer search algorithm: (1) The low-level search utilizes an improved state lattice. We design emergency stop motion primitives to ensure complete UAV dynamics and handle spatio-temporal constraints from high-level conflicts. (2) The high-level algorithm defines comprehensive conflict types and proposes a motion primitive conflict detection method with linear time complexity based on Sturm’s theory. Additionally, our modified independence detection (ID) technique is applied to enable parallel conflict processing. We validate the planning capabilities of SL-CBS in classical scenarios and compare these with the latest state-of-the-art (SOTA) algorithms, showing great improvements in success rate, computation time, and flight time. Finally, we conduct large-scale tests to analyze the performance boundaries of SL-CBS+ID.

Sleep stage classification is of great importance in sleep analysis, which provides information for the diagnosis and monitoring of sleep-related conditions. To accurately analyze sleep structure under comfortable conditions, many studies have applied deep learning to sleep staging based on single-lead electrocardiograms (ECGs). However, there is still great room for improvement in inter-subject classification. In this paper, we propose an end-to-end, multi-scale, subject-adaptive network that improves the performance of the model according to the model architecture, training method, and loss calculation. In our investigation, a multi-scale residual feature encoder extracted various details to support the feature extraction of single-lead ECGs in different situations. After taking the domain shift caused by individual differences and acquisition conditions into consideration, we introduced a domain-aligning layer to confuse the domain. Moreover, to enhance the performance of the model, the multi-class focal loss was used to reduce the negative impact of class imbalance on the learning of the model, and the loss of sequence prediction was added to the classification task to assist the model in judging sleep stages. The model was evaluated on the public test datasets SHHS2, SHHS1, and MESA, and we obtained mean accuracies (Kappa) of 0.849 (0.837), 0.827 (0.790), and 0.868 (0.840) for awake/light sleep/deep sleep/REM stage classification, which confirms that this is an improved solution compared to the baseline. The model also performed outstandingly in cross-dataset testing. Hence, this article makes valuable contributions toward improving the reliability of sleep staging.

Multi-UAV search missions for prioritized targets under constrained communications suffer from weak communication-decision integration, limited global perception synchronization, and delayed mission response. This paper formulates multi-UAV collaboration search as a multi-objective optimization problem to balance communication overhead and search performance. A Cooperative Hierarchical Target Search under Constrained Communications (CHTS-CC) algorithm is proposed to address the problem. The algorithm incorporates a Cluster-Consistent Information Fusion with Event Trigger (CCIF-ET) method, which enables intra-cluster information fusion. When clusters connect, a single merge that applies joint weighting by cluster scale and uncertainty reduces communication overhead. Furthermore, a Dynamic Preemptive Task Allocation (DPTA) mechanism reallocates UAV resources based on target priority and estimated time of arrival (ETA), enhancing responsiveness to high-priority targets. Simulation results show that when all UAVs and communication links operate normally, CCIF-ET reduces total confirmation time by 8.73% compared to the uncoordinated baseline and maintains a 24.43% advantage during single-UAV failures. In scenarios with obstacles, failures, and dynamic targets, CHTS-CC reduced mission completion steps by 34.78%, 32.35%, and 55.45% compared to the non-allocation baseline. The average detection time for high-priority targets decreased by 28.48%, 29.41%, and 58.82%, respectively, demonstrating the effectiveness of the proposed algorithm.

Multi-agent pathfinding has been extensively studied in the robotics and artificial intelligence communities. The classical algorithm, Conflict-Based Search(CBS), is widely used in various real-world applications due to its ability to solve large-scale conflict-free paths. However, classical CBS assumes discrete time-space planning and overlooks physical constraints in actual scenarios, making it unsuitable for direct application in Unmanned Aerial Vehicle(UAV) swarm. Inspired by the decentralized planning and centralized conflict resolution ideas of CBS, we propose an optimal and efficient UAV swarm motion planner that integrates State Lattice with CBS, named SL-CBS. SL-CBS is a two-layer search algorithm: (1) The low-level search utilizes an improved State Lattice. We design emergency stop motion primitives to ensure complete UAV dynamics and handle spatio-temporal constraints from high-level conflicts. (2) The high-level algorithm defines comprehensive conflict types and proposes a motion primitive conflict detection method with linear-time complexity based on Sturm’s theory. Additionally, our modified Independence Detection(ID) technique is applied to enable parallel conflict processing. We validate the planning capabilities of SL-CBS in classical scenarios and compare it with State-Of-The-Art(SOTA) algorithms, showing great improvements in success rate, computation time, and flight time. Finally, we conduct large-scale tests to analyze the performance boundaries of SL-CBS+ID.

Rapid search for evasive targets using multiple Unmanned Aerial Vehicles (UAVs) presents significant challenges, as it requires real-time target-motion prediction, multi-agent coordination, and adherence to kinematic constraints. Existing cooperative search methods often assume non-adversarial target behavior or model target motion independently of UAV actions, which reduces their effectiveness against targets that actively evade based on UAV positions. To address these limitations, this study introduces the Cooperative Rapid Search Algorithm for Evasive Targets (CRS-AET). The proposed framework utilizes graph-theoretic modeling to represent spatial-temporal relationships among UAVs, targets, and environmental grids. A directional gradient-based motion prediction (DG-Prediction) method first estimates probable movement areas of dynamic targets within the graph-structured environment. An improved multi-round auction algorithm with graph-based utility propagation (IMRAA) then optimizes UAV resource allocation. Finally, Dubins-Constrained Trajectory Optimization (DC-RTO) is integrated within a distributed model predictive control (DMPC) scheme to ensure kinematic feasibility. Simulation results across three representative scenarios indicate that CRS-AET enables faster target detection, enhanced area coverage, and more efficient coordination than baseline methods. Hardware-in-the-loop (HIL) experiments further confirm the robustness and practical applicability of the framework in realistic operational environments.

In order to enhance the wind-resistance capability and achieve a lightweight design of high-altitude unmanned airships, this study proposes a rapid optimization method for a heterogeneous propeller propulsion system. This system integrates contra-rotating and ducted propellers to exploit their respective aerodynamic advantages. First, surrogate models of the contra-rotating propeller, contra-rotating motor, ducted propeller, and ducted motor were constructed using an optimal Latin hypercube sampling method based on the max–min criterion. Then, within the optimization framework, propeller–motor matching principles and energy balance constraints were incorporated to minimize the total weight of the propulsion and energy systems. A case study on a conventional high-altitude unmanned airship demonstrates that, under the same wind-resistance capability, the adoption of the heterogeneous propeller electric propulsion system reduces the total propulsion-and-energy system weight by 24.94%. This method integrates the advantages of contra-rotating and ducted propellers, thereby overcoming the limitations of conventional propulsion architectures. It provides a new approach for designing lightweight, efficient, and long-endurance propulsion systems for near-space high-altitude platforms.