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Browse AllUnmanned aerial vehicles (UAVs) equipped with chemical contaminant sensors offer an innovative approach for rapid and safe remote chemical reconnaissance. However, the proliferation of diverse UAV platforms presents a critical challenge: how to select the optimal platform for a given mission scenario. This paper establishes a systematic suitability analysis framework for deploying chemical contaminant sensors on three mainstream UAV platforms-multirotor, fixed-wing, and vertical take-off and landing (VTOL) types. We analyze payload capacity, endurance, flight stability, environmental adaptability, and mission system integration. To reduce decision subjectivity, we develop an Analytic Hierarchy Process (AHP)-based quantitative evaluation model incorporating expert surveys and consistency tests to validate the weighting process. Beyond the evaluation framework, we explore key engineering challenges in platform-sensor integration, including aerodynamic interference, vibration and electromagnetic compatibility, and power management. We compare quantitative measurements and qualitative features across platforms in simulated mission scenarios. The resulting selection criteria provide a theoretical basis and a reproducible decision-support framework for developing chemical detection systems applicable to emergency response, environmental monitoring, and public safety.
To achieve the key control objectives of thrust regulation and inlet protection for a rocket-based combined cycle (RBCC) engine under wide flight envelope, high-speed operating conditions, this study is based on a nonlinear component-level RBCC engine model developed in MATLAB/Simulink. A state space model is established using a least squares system identification method. By incorporating actuator dynamics, an augmented system model is formulated, and a Differential Evolution (DE) optimized Linear Quadratic Regulator (LQR) multivariable closed-loop control strategy is proposed. Simulation results show that the proposed strategy significantly improves both response speed and disturbance-rejection capability. Compared with the conventional LQR controller, the maximum oscillation of the inlet stability margin is reduced from 11% to 4.4%, and the thrust settling time is shortened by 83%. These results verify the feasibility of the proposed control method under complex operating conditions.
This paper focuses on recursive estimation for stochastic systems under the round-robin (RR) protocol. In recursive estimation, it is assumed that the state evolution process of the signal is unknown, and the relevant estimation research is only carried out relying on the mean and covariance functions of the process. To effectively avoid the occurrence of transmission channel conflicts and ensure the fairness of data allocation during transmission, this paper chooses to adopt the RR protocol to schedule and manage data communication. Specifically, this scheduling rule stipulates that only one sensor is allowed to acquire the network communication opportunity within a fixed time period, and the entire scheduling process is executed cyclically in a preset fixed order. Based on the observable information available in the system, this paper further designs and proposes a recursive estimation algorithm by means of the innovation method. Finally, a numerical simulation experiment is given to verify the effectiveness and feasibility of the proposed estimation algorithm.
As green development has become a global unified strategy, major developed countries in the world have successively announced their ambition to achieve carbon neutrality in this century. The new energy vehicle and traction battery industry has ushered in a high-quality development moment, and the safety of traction batteries has also been widely valued worldwide. As the traction battery is mainly arranged at the bottom of the vehicle, enhancing the operational safety of new energy vehicles necessitates a breakthrough in performance testing for bottom-impact protection. It is essential to develop a robust evaluation methodology that can accurately and efficiently evaluate the extent of damage inflicted on the battery system following a bottom impact or collision. Based on the bottom impact experiment of a typical traction battery pack, this paper studies the damage morphology and damage mechanism of the bottom impact. By integrating finite element mechanical simulations, it provides a detailed analysis of the evolving mechanical states across different zones of the battery pack during the impact event. The findings contribute to enhancing safety protection strategies for traction batteries in bottom impact scenarios and offer crucial support for evaluating the post-impact safety performance of new energy vehicles.
Asynchronous federated learning (AFL) is essential for low-altitude networks due to its benefits in reducing communication bandwidth, lowering transmission delays, and preserving data privacy. However, the highly dynamic mobility of drones, combined with inherent heterogeneities in AFL, including variations in data volume, data distribution, and device capability, introduces severe challenges, such as inconsistent model update frequencies, biased local training, and inefficient global aggregation. Consequently, these issues lead to significant accuracy degradation and hinder practical deployment. In this paper, we propose a novel AFL framework tailored for low-altitude networks to address these challenges, which combines a multi-dimensional clustering mechanism with a multi-weight aggregation mechanism (MDC-MWA). To mitigate local model bias induced by data heterogeneity, the multi-dimensional clustering allows the cloud server to compute a comprehensive score for each drone based on multiple dimensions, including the label distribution, the gradient direction, and the local data proportion. It then employs K-Means clustering to partition drones into highly homogeneous subgroups, each managed by a dedicated roadside unit (RSU). Within each subgroup, after performing local updates, each drone synchronously uploads parameters to its RSU. Between these subgroups, to address inconsistent update frequencies caused by imbalanced computing capabilities, RSUs perform asynchronous training via the multi-weight aggregation mechanism, which updates the global model considering staleness weight and direction consistency weight. Experimental results demonstrate that the proposed method outperforms baseline approaches in highly heterogeneous scenarios, achieving a 4.74% absolute improvement in training accuracy on the CIFAR-10 dataset. This work provides a practical framework for distributed intelligent applications in dynamic and resource-constrained low-altitude networks.
As a core carrier of urban underground infrastructure, underground utility tunnels have an increasingly urgent demand for AAV swarm technology in operation and maintenance tasks such as internal pipeline inspection and fault diagnosis. However, the special environment of utility tunnels, characterized by GPS signal loss, narrow and enclosed space, and complex types of obstacles (including fixed pipeline supports and temporary construction components), poses severe challenges to AAV swarm operations. In this scenario, while maintaining the predetermined formation configuration, AAV swarms are required to complete three tasks simultaneously: accurately avoiding complex static obstacles in the tunnels, real-time evading other AAVs within the swarm to prevent intra-formation collisions, and strictly maintaining a safe distance from the tunnel walls. The superposition of these three constraints and the narrow operating space greatly increases the difficulty of swarm control. Traditional control methods often suffer from problems such as formation collapse, jerky obstacle avoidance, or wall scraping. To address the above pain points, this paper proposes an AAV swarm Model Predictive Control (MPC) method integrated with the Adaptive Perceptual Cauchy Mutation Pigeon-Inspired Optimization (APCMPIO) algorithm. This method optimizes the receding horizon parameters and constraint weights of model predictive control through the APCMPIO algorithm to achieve rapid response to complex dynamic scenarios, and embeds a dynamic safe distance threshold mechanism to balance formation integrity and individual safety. A simulation experiment was built for a utility tunnel inspection scenario with 6 AAVs. It significantly improves the operational adaptability and robustness of AAV swarms in constrained utility tunnel environments, providing reliable technical support for the swarm-based operation and maintenance of underground infrastructure.