Browse Topic: Unmanned aerial vehicles
With the complexity of chemical warfare threats and the diversification of battlefield environments, traditional toxic agent detection methods are facing bottlenecks such as response delays, coverage blind spots, and personnel safety risks. This research focuses on the application of unmanned aerial vehicle (UAV) carried toxic agent sensor systems, aiming to analyze the methods of mounting and deploying the sensors on the UAVs, and to construct a rapid response, high-precision, and highly resistant toxic agent monitoring system. Its significance lies in two aspects: 1. Tactical value: It breaks through the time and space limitations of manual reconnaissance, realizes real-time dynamic perception and early warning of toxic agent contamination, and provides key decision-making support for battlefield command; 2. Application expansion: The research results can be transferred to counter-terrorism, nuclear, biological, and chemical emergency response fields, providing theoretical support and engineering paradigms for the development of unmanned and intelligent chemical defense equipment.
This paper presents the design, implementation, and validation of an aerial-launch FPV (First-Person View) drone system that was developed to provide a complex environment with flexible deployment and precise delivery capabilities. The integrated system is composed of a hybrid VTOL carrier aircraft, a number of FPV drones, and an aerial mounting / release equipment. Using the AYK-250 platform, the carrier has a vertical take-off and landing function and long-time endurance. In terms of the FPV drones, it is built upon the high performance MARK4 5-inch frame that has high agility and high payload. The release module uses a single-hook point structure with a limit stop. The FPV drones are released stably, and the separation is reliable in flight. Comprehensive flight tests proved all workflows completely, involving carrier take-off, cruise with drones mounted, sequential aerial launch, and subsequent autonomous attitude recovery and route tracking by the FPV drones. The test results confirm the system’s capability for reliable launch from an aerial platform coupled with precise guidance, establishing a credible technical solution for expanding the practical applications of FPV drones in distributed tasks. Results show that our system can be launched via an aerial platform with an accurate guide and is a viable technological solution to spread FPF Drones for operational strategies in a more distributed way.
Aerodynamicists around the globe are developing mechanisms and structures inspired by nature that enable variable camber morphing (VCM) for aerodynamic surfaces. The implementation of the VCM mechanism in an airplane wing enhances the performance and stability during various flight segments. The present review article is focused mainly on the up-to-date VCM methods in a qualitative as well as quantitative approach that are specific to Aircraft/unmanned aerial vehicle (UAV) wing configurations. Initial literature discussions are confined to the conventional mechanisms that enable VCM in different aircraft configurations and the added aerodynamic advantages such as lift enhancement, drag reduction, boundary layer separation, and flow control. However, those designs need either external shape optimization or internal structural refinements to ensure the factor of safety (FoS). The modern aviation industry is also focused on bioinspired technology because of the adaptive flying capabilities and stall-delay characteristics. Therefore, a review of bioinspired VCM methods that are assessed based on the aerodynamic potentials is sequentially organized in the article. Additionally, considerations are motivated by the application of various compliant structural patterns for VCM in the aircraft industry. The discussion indicates the prospective benefits of morphing toward the future of the Green Aviation industry.
This paper, for the first time, applies the Divine Religions Algorithm (DRA) to three-dimensional UAV path planning. Targeting the complex terrain of urban-mountain mixed environments, we propose a novel method that incorporates multiple enhancements, including A* initialization, single-point disturbance mutation, and adaptive weighting. First, the A* algorithm is employed to generate high-quality initial paths, serving as the skeleton of the population. Innovative mechanisms such as terrain-adaptive disturbances and dynamic weight adjustment are integrated to achieve both efficiency and robustness in path optimization. Comparative experiments with Genetic Algorithm (GA) and Crowned Porcupine Optimization (CPO) show that the improved DRA algorithm exhibits significant advantages in terms of path length, safety margin, average altitude variation, average turning angle, and overall cost function. It consistently obtains superior paths and achieves faster convergence. The results demonstrate that the proposed approach provides an efficient, adaptive, and practical intelligent optimization tool for UAV path planning in urban-mountain mixed or similarly complex environments, offering promising prospects for engineering applications.
This paper proposes a UAV combat simulation method integrating AFSIM and DoDAF to address the complexity of UAV combat systems. DoDAF establishes a multi-view architecture mode to clarify logical relationships between UAVs and weapon systems, laying a structured foundation. AFSIM implements dynamic simulation of combat processes by mapping DoDAF’s static architecture to its dynamic elements, simulating UAV maneuver, situation awareness, and strikes. A UAV search-and-strike mission scenario test shows the method accurately simulates collaborative behavior in target searching, tracking, and engaging. This method features a high degree of standardization and normalization, providing a foundation for the evaluation of UAV combat effectiveness and strategy optimization.
When quadrotor unmanned aerial vehicles (UAVs) operate in urban low-altitude airspace, especially within complex environments, their sensor perception signals are highly susceptible to blockages, deviations, and the inclusion of high-frequency noise. These factors, in turn, induce nonlinear variations in the UAVs’ flight mechanical properties, giving rise to abnormal flight stability issues such as attitude jitter, altitude fluctuations, and trajectory deviations. To address these challenges, this paper puts forward a method aimed at enhancing the positional accuracy of quadrotor UAVs, which is based on Extended Kalman Filter (EKF) multi-sensor fusion. In conjunction with the redundant configuration of sensors, a proportional-integral controller is specifically designed to allow optical flow sensors to compensate for the speed data generated by inertial sensors. Building on the EKF method, a comprehensive data fusion model is established, encompassing both position and speed states. Leveraging the MATLAB platform, trajectory flight simulations are conducted, utilizing multi-sensor data fused via EKF, with the sensor suite including GPS, IMU, Optical Flow sensors, and Barometers. The simulation results demonstrate that this proposed method can effectively mitigate the adverse impacts of environmental interference and sensor noise on the positional accuracy of quadrotors. By continuously correcting position information and accurately estimating position states, it significantly improves the UAVs’ flight position accuracy. This research outcome lays a robust and theoretically sound foundation for in-depth investigations on critical issues related to general aviation applications, such as the safe and efficient autonomous flight, adaptive and reliable intelligent navigation, and ultra-precise and mission-critical operations of quadrotor UAVs, thereby significantly contributing to the sustained and innovative advancement of the field.
Multi-UAV cooperative localization can utilize information fusion between nodes to improve localization accuracy and performance on the target. Distributed state fusion estimation methods have been heavily studied in recent years, but the final estimates in the research results do not converge towards the global optimum. This paper aims to make the state estimates of each individual in the UAV formation for the target converge and converge to reliable values. In this paper, we study a multi-UAV cooperative tracking method based on adaptive weighted fusion, which first evaluates the importance of each node in the UAV formation and the reliability of the local filtering estimation results, and then assigns the weights according to the reliability of the UAV’s local state estimation of the target in the whole at the current moment. Finally, this paper verifies through simulation experiments that the method can not only accomplish the state tracking of the target, but also that the state estimates of each node in the network converge to more accurate state estimates.
Aiming at the problems of model uncertainty, external disturbances and high-frequency chattering of traditional sliding mode control in complex working conditions for quadrotor unmanned aerial vehicles, this paper proposes a control strategy based on fractional-order sliding mode. The quadrotor UAV control system has problems such as parameter uncertainty, multi-input multi-output, and sensitivity to internal and external disturbances. Traditional PID control has certain limitations. Sliding mode control has the advantages of strong robustness and simple implementation. Fractional-order calculus has hereditary and memory properties. The combination of the two has better control performance for nonlinear systems. To further improve the trajectory tracking performance of quadrotor UAVs, a fractional-order sliding mode controller is designed based on fractional-order theory and traditional sliding mode control. Finally, multiple experiments are conducted in Matlab/Simulink, including trajectory tracking, parameter perturbation, and anti-interference simulation experiments. The control results of various controllers are compared and analyzed to verify the effectiveness of the fractional-order sliding mode control method designed in this paper.
Rigorous validation of SAE Levels 3 and 4 autonomous systems increasingly relies on simulation. However, the simulation-reality gap remains a challenge for human-in-the-loop assessments. This study empirically quantifies the behavioral fidelity of the Car-Learning-to-Act (CARLA) simulator by recreating specific real-world traffic scenarios using the high-precision exiD drone dataset. Twenty-five participants performed a series of maneuvers, including lane changes and time-critical cut-ins. Their performance was analyzed using Dynamic Time Warping (DTW), driver profiling, and Time-to-Collision (TTC) metrics. The findings reveal a clear distinction between relative and absolute behavioral validity. In strategic decision-making tasks, the simulation demonstrated remarkably high temporal fidelity. DTW analysis explained 94% of the trajectory variance. Participants initiated lane changes with an average lag of -9 frames (0.36 s) compared to naturalistic references. These results indicate that, despite the absence of peripheral optical flow, the simulator successfully elicits temporally correlated decision-making patterns suitable for assessing strategic driver intent. However, physical execution in reactive scenarios revealed significant absolute discrepancies. Although the high Pearson correlation (r ≈ 0.89) in velocity profiles proves that drivers recognize and react to hazards with realistic timing, their physical inputs were exaggerated. Participants displayed digital, over-modulated braking responses and maintained a negative safety bias of -11.26 m, a deviation attributed to the lack of vestibular g-force feedback and geometric minification. Furthermore, distinct driver profiles emerged. Risk-oriented participants exhibited a gaming effect by neglecting safety margins. In conclusion, while CARLA is highly valid for testing the temporal logic of driver interactions, absolute dynamics require calibration functions, such as force-feedback (pedal) tuning and visual deceleration cues like camera shake, to compensate for sensory limitations before it can be used for safety-critical validation.
Labor shortages and supply chain volatility are putting additional pressure on warehouse operations to be faster and more adaptable. “In this environment, real-time visibility becomes foundational. Physical AI enables warehouses to operate with a continuously updated understanding of their environment, allowing them to respond quickly to disruptions and optimize performance,” Joseph Mirabile, Vice President of Operations at Gather AI, a Pittsburgh-based startup developing drone-powered inventory solutions.
In the two months since Microvision bought Luminar and acquired key tech and talent, the sensor company has been busy. In that time, they've merged key lidar units from each company and created a perception software stack to run it in a convincing demo of its ADAS and autonomous capabilities. The company is also pushing innovative lidar tech into the defense drone and antidrone markets, already working with a German defense supplier that works with NATO member countries.
The uncrewed aerial vehicle (UAV) market is advancing at extraordinary speed, reshaping both commercial and defense aviation. From small tactical systems operating at the edge of the battlefield to high-altitude uncrewed platforms conducting strategic surveillance, UAVs are now critical assets across a wide range of mission environments. Their capabilities continue to expand - carrying more sensors, flying longer missions, and navigating more contested environments. Yet this rapid innovation brings with it growing engineering pressure. UAVs are expected to be lighter, more autonomous, more modular, and more adaptable, all while maintaining near-flawless reliability. This is where heritage becomes decisive. In an era that rewards speed, heritage provides the hard-earned engineering wisdom that ensures systems do not just fly but perform predictably, repeatedly, and safely under real-world conditions.
Bird accidental collision with overhead transmission lines poses a threat to the ecology of rare bird populations. This article analyzes the warning measures to prevent birds from accidental collisions at home and abroad. In response to the low efficiency of manual installation and the poor static warning effect in preventing birds from accidental collisions with overhead transmission lines, the visual characteristics of birds are analyzed. A drone-based automatic installation flash-type bird accidental collision warning device is proposed, which includes a fixture, a disc, and a luminous circuit. The fixture can be carried and installed on the overhead line by a drone and can be easily disassembled. The disc adopts eye-catching colors and has a hollow structure to reduce wind resistance load. The luminous circuit includes solar panels, charge and discharge control circuits, flicker control circuits, batteries, and luminous components. The drone suspension warning device test was conducted, and the results showed that the device can be easily suspended from the overhead line by the drone.
Developing a comprehensive autonomy solution for the Army's current and future aircraft fleet requires a robust computational and perception capability for decision-making across the entire flight envelope without a pilot. This also requires a flight control system and infrastructure capable of executing autonomous decisions in complex mission environments. Ongoing development of automation and autonomy, utilizing a wide range of perception sensors, has been conducted on platforms such as Sikorsky's S-70 and the Army's UH-60Mx aircraft. This work builds upon previous efforts and leverages ongoing collaborations with industry, the Department of War (DoW), and the Defense Advanced Research Projects Agency (DARPA) to advance autonomous capabilities for both optionally piloted and uncrewed aircraft.
The safe integration of Unmanned Aerial Vehicles (UAVs) into shared airspace necessitates robust conflict detection and avoid (DAA) methods that scale effectively with multiple dynamic intruders. Geometric methods, such as those in the DO-365 standard, are provably safe for pairwise encounters but become intractable in dense environments. Conversely, applying kinodynamic motion planners designed for static obstacles to dynamic scenarios leads to unstable behavior, characterized by excessive re-planning and oscillatory motion, as they lack a predictive model of intruder trajectories. This paper introduces a closed-loop planning framework based on the Closed-Loop Rapidly-exploring Random Tree* (CL-RRT*) algorithm to prevent Loss of Well-Clear (LoWC) in multi-intruder scenarios. Our approach integrates a closed-loop dynamics model to guarantee dynamically feasible trajectories and incorporates a spatiotemporal planning strategy. A time-to-come metric is propagated from the tree root to all nodes, enabling prediction of the state and time at future trajectory points. Predicted states are continuously evaluated against known intruder trajectories (from ADS-B or perception system) using the formal DO-365 well-clear criteria, checking each point against the Hazard Area Zone (HAZ) via Horizontal Miss Distance (HMD) and Distance-Modification-for-Tau (DMOD) metrics. Simulations demonstrate that the proposed planner successfully generates safe and feasible trajectories that prevent LoWC in complex multi-intruder scenarios.
Autonomous Inspection via small Unmanned Aircraft Systems (sUAS) is increasingly utilized across industrial use cases such as inspection of bridges, buildings, construction sites, roadways, transmission lines, pipes, wind turbines and power systems (1). In principle, the system workflow of inspection; identification and characterization of defects; and mapping in space is very similar across industries. Boeing and Proxim (A Near Earth Autonomy Company) have partnered to pursue this technology in the Aerospace and Defense industry for General Visual Inspection (GVI) of airframes predominantly in a maintenance setting. This activity began by deploying Proxim’s Autonomous Aircraft Inspection (AAI) technology and Boeing's Automated Damage Detection Software (ADDS) on Boeing C-17 Globemaster III at Joint Base Pearl Harbor-Hickam. It has expanded to offer U.S. Department of War (DoW) and Commercial customers aircraft-agnostic enhanced exterior GVI capability at point of need by leveraging unique ADDS AI algorithm in support of both home station and deployed operations. This paper gives an overview to industry developments in Autonomous Inspection, and the development AAI/ADDS technologies.
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