Browse Topic: Sensors and actuators
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.
Hemisphere resonant gyroscope (HRG) is a new type of vibration gyroscope with high precision, high reliability, and long lifespan. Improving the temperature stability of a hemispherical resonant gyroscope (HRG) has profound implications for navigation and guidance systems as well as airborne sensor technology. By optimizing temperature compensation algorithms or improving material thermal properties, the angular velocity measurement error caused by temperature drift can be significantly reduced, thereby improving the long-term positioning reliability of navigation systems in extreme temperature fluctuation scenarios. This article starts with the structure of the hemispherical resonant gyroscope, studies the temperature characteristics of the hemispherical resonator through formula theory, verifies and analyzes the temperature characteristics of the hemispherical resonant gyroscope through experiments, and designs a temperature compensation scheme. Through experimental data analysis, the root mean square error of hemispherical gyroscope drift was reduced from 1.451066 ° /h to 0.383937 ° /h after temperature compensation. This compensation scheme can effectively improve the output accuracy of the hemispherical resonant gyroscope and reduce the output drift under the condition of gyroscope temperature changes.
The detection of free space plays a fundamental role in ensuring the safe and efficient operation of heavy-duty vehicles, particularly in environments where the available area to maneuver is severely constrained, such as construction zones, rest areas, or loading docks. An accurate estimation of free space is essential to prevent collisions, maintaining operational continuity and minimizing vehicle downtime. As observed from the reviewed literature, despite the large number of proposed free-space detection methods, there is no concise and established definition about how free space should be determined, represented, and inferred, nor agreement on the semantic classes to be considered. This heterogeneity complicates systematic comparison and benchmarking across approaches. This paper presents a structured survey and methodological analysis of recent free-space detection and semantic segmentation approaches across automotive LiDAR-, camera-, and radar-based perception systems, as well as multimodal sensor fusion. The review spans classical geometric and occupancy-based techniques together with deep-learning methods, along with datasets commonly used for evaluation. The main contributions are (i) a structured taxonomy and comparative analysis of existing free-space definitions and detection strategies, categorized by their assumptions, representation forms, and sensing modalities; and (ii) a unified and application-independent definition of free space together with the required semantic classes. These contributions aim to provide a consistent conceptual foundation to support future research and to aid the systematic evaluation of upcoming free-space detection systems.
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated driving functions. To address this limitation, we deployed a software-defined vehicle architecture to the concept. This paper introduces the novel e/e-architecture and software stack enabling the Peoplemover 2+2 to initiate its first shuttle service at the German Aerospace Center (DLR e.V.) in Stuttgart. We further detail the deployed multi-modal sensor suite, comprising modern solid-state LiDARs and a 4D imaging radar, which were carefully selected to meet the operational design domain requirements while also serving as a versatile research platform for future advanced perception studies. Finally, we analyze the SDV-based modular software stack, which facilitates rapid application development through straightforward switching between commercial, open-source, and in-house software domains, and supports parallel execution of domain-specific functions across all three software sources.
Gyroscopic effects split circumferential traveling-wave resonances of rotating structures into forward and backward branches. This work first analyzes the splitting in the co-rotating (Lagrangian) frame to provide physical intuition for the evolution of the two branches with spin speed. A transformation to the inertial (Eulerian) frame is then derived, showing that the observed frequencies are shifted by a kinematic Doppler-like term that acts with opposite sign on the forward and backward waves, leading to different Campbell-diagram slopes depending on the observation frame. The resulting framework is validated experimentally on a freely rotating, unloaded tire using two complementary sensing modalities: wireless on-tire accelerometers (co-rotating view) and a scanning laser Doppler vibrometer (inertial view). A frequency-domain SVD-based identification (FDD/ODS-SVD) is used to extract poles and deformation patterns over a range of spin speeds, enabling Campbell diagrams in both frames. The application of the proposed transformation maps the co-rotating branches onto the inertial observations, yielding consistent forward/backward splitting between the two measurement systems.
Emergency evacuation slides (EVAC slides) are critical safety devices used on aircraft to enable rapid egress during emergencies. While these slides provide a quick and reliable escape route, communication between separated slides during evacuation remains a challenge. Often, during raft deployment over water, slides may drift apart impeding communication among evacuees and rescue personnel potentially compromising safety. Existing aircraft EVAC systems lack integrated wireless communication relying on visual or voice signals that are unreliable in chaotic conditions. This paper explores the integration of wireless IoT technology into EVAC slide systems to facilitate inter-slide communication and monitor critical parameters such as slide air pressure and the floating weight of stranded passengers through embedded sensors. It proposes the adoption of Long Range (LoRa) modulation technology for wireless communication chosen for its low-power, long-range performance and license-free operation in emergency evacuation scenarios. In addition, the usage of this proposed technology can be further extended to locate the aircraft when other existing locating mechanisms fail.
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.
Researchers at the University of California, Irvine, and New York’s Columbia University have embedded transistors in a soft, conformable material to create a biocompatible sensor implant that monitors neurological functions through successive phases of a patient’s development.
Using an inexpensive electrode coated with DNA, MIT researchers have designed disposable diagnostics that could be adapted to detect a variety of diseases, including cancer or infectious diseases such as influenza and HIV.
Robot Arm Tracking Control refers to the control of robot end effectors following a prescribed trajectory as their movement in robotic systems. The work presents a combination of Kalman Filter Based Dynamic System Tracking with Reinforcement Learning Based Trajectory Planning. These two aspects of tracking and planning help the robotic manipulator dynamically track a target that is located on an arbitrary moving path. In particular, by using Kalman filtering to estimate the position of a moving target and to compensate for sensor noise and sparse sampling, we take high-precision estimation values of each point’s coordinates along the target trajectory as a reliable basis to build a policy network using reinforcement learning. Based on it, the robot manipulator could produce effective motion planning under its own dynamic capabilities and physical constraint limit. Comprehensive simulation results illustrate advantages of the new algorithm against the classical control method, confirm that the novel technique achieves better performance both in accuracy and computation efficiency. Also, this mixed control system can deal with complex moving path for track target object. Even when meet different obstacle and not sure measurement, it still works well with other moving obstacle in many conditions. This can be strong to face other dynamic obstacle even if have different situation with changing obstacle and uncertain data. It shows that this paper works as an attempt toward optimal solution to combine the model-based technique together with data-driven approach aiming to support real-time, highly accurate, adaptive prediction is based control technique, promising applications into industry and promoting more improved works related.
In response to the problems of urban traffic congestion and the limited expansion of infrastructure, this paper conducts two core research focusing on the intelligent chassis system of split-type flying vehicle. Firstly, an autonomous navigation strategy for the intelligent chassis module is proposed based on chassis module Navigation 2 architecture, which fuses LIDAR and IMU positioning to plan paths using the A* global planning algorithm on a global cost map, and update the local cost map in real time with sensor data. It is orchestrated by the BT Navigator using a behavior tree, with failures handled by the Recovery Server, to achieve autonomous driving across multiple waypoints. In simulation and closed-field experiments, the system can stably reach the preset target points. The positioning accuracy and trajectory tracking performance can meet the design requirements. Secondly, a mechanical slide rail-type docking structure adapted to the split flying vehicle architecture is designed. Deformation analysis under the representative working conditions are evaluated through finite element software. The test results show that the maximum deformation of this docking structure under typical load is significantly lower than the docking tolerance and positioning repeatability requirements. The structural stiffness and stability meet the design indicators. The above work indicates that the proposed autonomous navigation strategy and the docking structure for the intelligent chassis can effectively support the modular operation of “air trunk & ground terminal” mode, providing a scientific basis for the functional integration and system reliability research of split-type flying vehicles.
The outwash flow of a VTOL aircraft in near-ground operation is a serious risk for surrounding objects or personnel, and has been investigated for a long time. The current paper contributes to this topic by revisiting quantitative outwash measurement techniques and evaluation strategies suitable for full-scale flight experiments. An array of purpose-built ultrasonic anemometers, a pitot tube rake, and fiber-film sensors were applied during outwash tests with a hovering Eurocopter EC135, complemented by accompanying numerical simulations with the Vorticity Transport Model (VTM). A focus is set on an analysis of the flow unsteadiness, revealing large fluctuations at low frequencies which require careful data post-processing. The fluctuations limit the application of pitot tubes to measure the mean flow due to their angular sensitivity, and ultrasonic anemometers are recommended as a particularly suitable and convenient measurement method with a sufficient frequency response. Furthermore, the experimental and numerical results are compared to semi-empirical outwash models, and the importance of simultaneous measurements of the ambient wind conditions is demonstrated.
The present work develops a computational framework for simulating the two-way coupled ship-helicopter dynamic interface using large-eddy simulation. The Simple Frigate Shape 2 geometry is modeled using the immersed boundary method, and baseline simulations under both uniform inflow and neutral atmospheric boundary layer (ABL) conditions are validated against wind tunnel measurements for two wind-over-deck angles. Rotor modeling techniques, including the actuator line model (ALM) and actuator disk model (ADM), are verified and validated across several configurations: the Knight and Hefner rotor, ONERA HAD-1 propeller, and NASA Dragonfly Phase B* coaxial rotor. The lower-fidelity ADM captures wake characteristics consistent with the ALM with up to a 9× speedup. The ADM maintains strong agreement with experimental and numerical results for integrated performance metrics and is suitable for two-way coupled simulations. The developed framework is applied to a rotor-obstacle configuration based on a GARTEUR 22 experimental survey and subsequently to a fully-coupled ship-rotor-ABL system.
This paper presents two distinct Lagrangian models developed for efficient rotorcraft inflow prediction: an Extended Vortex Ring Wake (EVRW) model and a Viscous Vortex Particle Method Actuator Disk (VVPM-AD). The EVRW model represents wake vorticity as circular rings with a harmonic distribution of bound circulation, thereby avoiding explicit coordinate-by-coordinate calculations. In contrast, the VVPM-AD discretizes shed vorticity into an independent Lagrangian particle cloud. This formulation naturally captures wake roll-up and viscous effects via a meshless Large Eddy Simulation solver, eliminating the need for empirical core-radius formulations. Validated against experimental data, these separate modeling tracks provide a scalable toolkit that balances computational speed with simulation fidelity.
In September 2025 a test was conducted at the NASA Ames Research Center's Outdoor Aerodynamics Research Facility to characterize the outwash flow from a hovering rotor. The studied configurations included variations in rotor height above the ground plane as well as variations in rotor speed, thrust, and trim state. Outwash flow was measured using a rake of hot-film anemometers mounted to a semi-autonomous instrumentation cart. Flow measurements were conducted for a single rotor azimuth at distances between 0.6 and 4.0 diameters from the rotor center. In addition to hot-film anemometry, additional flow data was captured using a Background Oriented Schlieren Velocimetry technique that provides a detailed measurement over time of flow velocity in an image plane upstream of the hot-film sensors. The downwash and outwash produced by a lifting rotor in the presence of a ground plane can pose significant risks to life and property in the area of operations. Rotor wake interactions exhibited by modern, multi-rotor aircraft designs can amplify these flows and increase the risk. The rotorcraft community is therefore motivated to better understand rotor downwash and outwash to establish safe operating limits in the vertiport environment.
This study develops an efficient framework coupling the Lattice Boltzmann Method with the Actuator Line Method to evaluate the unsteady downwash/outwash of eVTOL aircraft. By incorporating a modified Prandtl loss function with geometric smearing correction, the framework accurately predicts velocity profiles and preserves unsteady vortices at lower computational costs than conventional RANS-based simulations. Analyzing three distinct eVTOL configurations sized for identical payload missions reveals that higher disk loading and multi-rotor interactions generate highly asymmetric, localized jet-like outwash structures, contrasting with the symmetric ground-level footprint of single rotor designs. Utilizing a 95th percentile velocity metric, transient peak hazards breach the regulated vertiport Safety Area boundary, extending up to 1.65 times the prescribed baseline limit. These findings demonstrate that time-averaged metrics underestimate physical hazards, highlighting the necessity for future guidelines to mandate configuration specific evaluations utilizing high-resolution unsteady flow data and robust statistical processing.
Full state feedback offers theoretically guaranteed multi-axis stability, making it superior to conventional PID controllers. There is however one drawback, a full state controller has a mathematical difficulty if the B matrix is not square and thus not invertible. This is the case for helicopters with 6 degrees of freedom and 4 inceptors. Variations of linear quadratic regulators are a work around, however complexity dramatically increases. Best would be a direct solution to the original problem. This is the breakthrough result of this paper. This paper documents an approach which removes the analysis roadblock by partitioning the 6 x 6 system "A" matrix into two groups of 4 x 4 matrices. The 4x4 matrices are individually stabilized with full state gain matrices. One matrix is designated “Driver Matrix” which provides actuator commands. The other matrix is designated "Reference Matrix" which provides references. The two matrices are coupled together by requiring that the driver matrix follow references generated by the reference matrix. With each matrix individually stabilized, the coupled combination is also stabilized. Computation of flight dynamics states (u, v, w, p, q, r) is shared between the matrices. Initial results are very encouraging, showing an originally sluggish, heavy lift helicopter having now concise decoupled responses to pitch and roll commands. Stability derivatives are recomputed during flight allowing coverage over the whole flight envelope. A handling qualities task has been defined to relocate a 40 ft standard seaborne container directed by a pilot in a ground control station. Cooper Harper ratings of this task have demonstrated favorable Level 1 handling qualities if use is made of an automated lateral repositioning command.
This paper introduces a robust supervised machine learning framework for estimating helicopter gross weight during the takeoff phase. The methodology leverages high-fidelity datasets from Airbus's global in-service fleet to ensure a reliable training foundation. At the core of the approach is a long short-term memory recurrent neural network, supported by a patented data-curation pipeline designed to maintain high data integrity. To align with rigorous aviation safety standards, the study outlines a learning assurance process compliant with EASA guidelines, specifically addressing safety assessment objectives for machine learning. A central innovation is the characterization and monitoring of the model's operational design domain through multidimensional functional principal component analysis. By projecting high-dimensional, non-linear sensor data into a manageable tabular subspace, this approach enables the definition of safety envelopes using explainable and efficient classical methods. Validated against diverse real-world flight profiles, the framework demonstrates high predictive accuracy, marking a significant milestone toward deploying the model on airborne targets for safety-critical functions such as condition-based maintenance.
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