Flight Experiment Research Articles

Study on Improvement of Docking Mechanism for Docking–Undocking Drones in the Air and Docking Control Using an Onboard Camera

In this study, we developed a docking mechanism for drones that enables docking and undocking in the air for cargo transportation. This capability allows the drone configuration to be adapted depending on the cargo size and shape. We introduced a novel docking mechanism that incorporates magnetic adsorption and a newly engineered locking mechanism. We developed a drone detection system capable of estimating the relative position of a target drone by utilizing an onboard camera to identify the infrared LEDs on the target drone via image processing. Moreover, flight experiments, including detection, docking, and undocking, were performed using a mock drone to demonstrate the effectiveness of the developed system.

Development of Wall Hammering Inspection Systems Using Two-Wheeled Multi-Copters

In this study, we investigated inspection robots that can safely, accurately, and quickly perform hammer sound inspections on tile exterior walls and present new wall hammering inspection systems using two-wheeled multi-copters. Our results yielded the following five advantages. First, the proposed multi-copter can use its wheels not only to move freely on the walls of buildings but also to overcome obstacles on the walls. Therefore, almost any tile wall surface can be inspected for hammering sound. Second, the multi-copter was equipped with both hammer-shaped rods to hit the tile exterior wall and microphones to collect the hammer sound of the tile, while moving quickly on the wall surface. Third, the wall hammering inspection systems can be used safely with a work assistance mechanism using a wire even in densely populated areas, such as urban areas, because the multi-copter has high wind resistance against crosswinds by both pushing against the wall and operating by the wire. Fourth, we presented some automatic control systems that make the multi-copter operation easy during hammering inspections and demonstrated its usefulness through experiments. Fifth, we proposed useful methods for detecting floating tiles based on the hammering results, conducted some outdoor flight experiments on building exterior walls, and demonstrated that floating tiles can be determined with high accuracy.

Single-Use Aircraft Launch System with Small Helium Balloon for 1 kPa Flight Testing

A single-use aircraft launch system using a small helium balloon concept was proposed to elevate the likelihood of successful high-altitude flight tests above 30 km. The system, capable of carrying a payload of 0.5 kg, has been developed and reported. Completing the flight within a range of 92 km, which represents the maximum communication distance between the airborne flight system and the ground station for operational phase control, is imperative. Therefore, a straightforward method for simulating flight trajectories was outlined to determine launch windows and suitable locations to ensure flight completion within uninhabited areas. Simulation studies revealed that the shortest landing distance was obtained during summer at the selected launch site. Additionally, a parameter study was conducted to assess the influence of varying ascent velocities on the targeted flight test altitude, which depends on the payload weight and helium gas volume. Consequently, the parameter study results established the constraints for flight model design. A demonstration flight was conducted in 2023 to validate the feasibility of the system for high-altitude flight experiments. This flight successfully released the flight model at 31 km and recovered flight data before landing on the sea surface. The proposed system concept offers an expedited approach to aircraft design for operating in thin air. It enables component function tests and scaled model flights as an alternative to employing large high-altitude balloons for high-altitude experiments.

Performance evaluation and energy management strategy of drone methanol reforming fuel cell hybrid power system: A case study

The performance of hydrogen production by methanol steam reforming and high temperature proton exchange membrane fuel cells on drone lacks the comparison of experimental data, resulting in the overall performance improvement of the system is difficult to evaluate. Therefore, the energy management strategy of methanol reforming high temperature proton exchange membrane fuel cell hybrid power system and state machine is designed and optimized. The performance of this system is analyzed and compared with the flight experiment data of a proton exchange membrane fuel cell hybrid UAV. The results show that the SOC of the system can be stabilized between 70 and 80 during the long cruise phase. The test results show that the maximum hydrogen storage mass density of the hydrogen supply system is increased by 67.6%. The flight time of the system under economic cruise flight of the drone is improved by 29.1%–44.9% with different quality of fuel. Under the maximum flight speed of a certain wind speed, the system can meet the flight requirements and increase the flight time by 31.9% and 17.6% respectively under the wind speed of 1.5 m/s and 4 m/s. Its flight time has been greatly improved, and it has great potential as a drone system.

Thermal Monitoring of an Internal Combustion Engine for Lightweight Fixed-Wing UAV Integrating PSO-Based Modelling with Condition-Based Extended Kalman Filter

The internal combustion engines of long-endurance UAVs are optimized for cruises, so they are prone to overheating during climbs, when power requests increase. To counteract the phenomenon, step-climb maneuvering is typically operated, but the intermittent high-power requests generate repeated heating–cooling cycles, which, over multiple missions, may promote thermal fatigue, performance degradation, and failure. This paper deals with the development of a model-based monitoring of the cylinder head temperature of the two-stroke engine employed in a lightweight fixed-wing long-endurance UAV, which combines a 0D thermal model derived from physical first principles with an extended Kalman filter capable to estimate the head temperature under degraded conditions. The parameters of the dynamic model, referred to as nominal condition, are defined through a particle-swarm optimization, minimizing the mean square temperature error between simulated and experimental flight data (obtaining mean and peak errors lower than 3% and 10%, respectively). The validated model is used in a so-called condition-based extended Kalman filter, which differs from a conventional one for a correction term in section prediction, leveraged as degradation symptom, based on the deviation of the model-state derivative with respect to the actual measurement. The monitoring algorithm, being executable in real-time and capable of identifying incipient degradations of the thermal flow, demonstrates applicability for online diagnostics and predictive maintenance purposes.

Experimental evaluation of wind turbine wake turbulence impacts on a general aviation aircraft

Abstract. Continued development of wind farms near populated areas has led to rising concerns about the potential risk posed to general aviation aircraft when flying through wind turbine wakes. There is an absence of experimental flight test data available with which to assess this potential risk. This paper presents the results of an instrumented flight experiment in which a general aviation aircraft was flown through the wake of a utility-scale wind turbine at an operating wind farm. Wake passes were flown at different downwind distances from the turbine, and data were collected on the orientation disturbances, altitude and speed deviations, and acceleration loads experienced by the aircraft. Videos and pilot statements were also collected, providing qualitative information about the disturbances encountered in the wake. Results show that flight disturbances were small in all cases, with no difference observed between flight data inside and outside the wake at distances greater than six rotor diameters from the turbine. At distances closer than six rotor diameters, small load factor and orientation disturbances were noted but were commensurate with those experienced in light or moderate atmospheric turbulence. Overall, the loads and disturbances experienced were far smaller than those that would risk causing loss of control or structural damage.

Pilot turning behavior cognitive load analysis in simulated flight.

To identify the cognitive load of different turning tasks in simulated flight, a flight experiment was designed based on real "preliminary screening" training modules for pilots. Heart Rate Variability (HRV) and flight data were collected during the experiments using a flight simulator and a heart rate sensor bracelet. The turning behaviors in flight were classified into climbing turns, descending turns, and level flight turns. A recognition model for the cognitive load associated with these turning behaviors was developed using machine learning and deep learning algorithms. pnni_20, range_nni, rmssd, sdsd, nni_20, sd1, triangular_index indicators are negatively correlated with different turning load. The LSTM-Attention model excelled in recognizing turning tasks with varying cognitive load, achieving an F1 score of 0.9491. Specific HRV characteristics can be used to analyze cognitive load in different turn-ing tasks, and the LSTM-Attention model can provide references for future studies on the selection characteristics of pilot cognitive load, and offer guidance for pilot training, thus having significant implications for pilot training and flight safety.

Drag Reduction on the Basis of the Area Rule of the Small-Scale Supersonic Flight Experiment Vehicle Being Developed at Muroran Institute of Technology (Second Report)

Drag Reduction on the Basis of the Area Rule of the Small-Scale Supersonic Flight Experiment Vehicle Being Developed at Muroran Institute of Technology (Second Report)

Hypersonic Stability and Breakdown Measurements on the AFRL/AFOSR BOLT II Flight Geometry

The Air Force Research Laboratory/Air Force Office of Scientific Research (AFRL/AFOSR) introduced the Boundary Layer Turbulence (BOLT II) flight experiment to further the understanding and modeling of a hypersonic turbulent boundary layer on a geometry that features concave surfaces with swept leading edges. In support of the flight experiment, this paper identifies and documents the transition instabilities and quantifies the breakdown to turbulence on a 25%-scale BOLT II model in hypersonic flow. Experiments were conducted in the conventional Actively Controlled Expansion wind tunnel facility and the Mach 6 Quiet Tunnel located at the Texas A&M University National Aerothermochemistry and Hypersonics Laboratory. Global surface heating was viewed using infrared thermography with on-surface measurements made using high-frequency Kulite and PCB Piezotronics surface pressure transducers. Modal growth was observed among the sensors both upstream and downstream of the model. Off-surface measurements were made in the mixed-mode region utilizing constant-temperature hot-film anemometry in the Mach 6 Quiet Tunnel. Comparisons were made to the Direct Numerical Simulation (DNS) results from the University of Minnesota. Amplified content was observed between f=53 and 75 kHz in the root-mean-square voltage fluctuations of the hot-film measurements.

Anytime algorithm based on adaptive variable-step-size mechanism for path planning of UAVs

For autonomous Unmanned Aerial Vehicles (UAVs) flying in real-world scenarios, time for path planning is always limited, which is a challenge known as the anytime problem. Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out, making UAVs more adaptable to different mission scenarios. However, current anytime algorithms based on A* have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles. This paper proposes a novel anytime path planning algorithm, Anytime Radiation A* (ARaA*), which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient. The ARaA* features two main innovations: an adaptive variable-step-size mechanism and elliptic constraints based on waypoints. The former helps achieve fast path searching in various environments. The latter allows ARaA* to control the suboptimality bounds of paths and further enhance search efficiency. Simulation experiments show that the ARaA* outperforms Anytime Repairing A* (ARA*) and Anytime D* (AD*) in controlling suboptimality bounds and planning time, especially in environments with large concave obstacles. Final flight experiments demonstrate that the paths planned by ARaA* can ensure the safe flight of quadrotors.

NEFELI: A deep-learning detection and tracking pipeline for enhancing autonomy in advanced air mobility

Efficient detection and accurate collision estimation for non-cooperative aerial vehicles are crucial for the realization of fully autonomous aircraft and Advanced Air Mobility (AAM). This paper introduces NEFELI, a machine learning software utilizing optical sensors to detect and track non-cooperative aerial vehicles. NEFELI's detector employs an enhanced YOLOv5-large model, strengthened with a sliced inference step to enhance detection capabilities for distant, diminutive objects. Furthermore, NEFELI introduces several innovations in its tracking component. A key advancement lies in the creation and utilization of the first large-scale re-identification (Re-ID) dataset of aerial objects. This dataset is used to train the deep learning appearance (Re-ID) model of the tracking module and integrates appearance information into the detection and tracking pipeline, resulting in more robust and reliable tracking performance. Moreover, the tracking model combines the deep learning appearance model with a Kalman Filter-based motion model to address the challenge of precisely tracking distant aerial objects. Notably, an extensive comparative analysis that was conducted showed that NEFELI outperforms state-of-the-art detection and tracking models in terms of Higher Order Tracking Accuracy (HOTA) metric, ID switches, and Association Accuracy (AssA) by a wide margin. A crucial aspect of this work is NEFELI's software architecture design, which enables efficient implementation on a low SWaP (Size, Weight, and Power) edge Graphic Processing Unit (GPU). To further showcase NEFELI's generalization capabilities and edge implementation performance, real-world flight experiments with small UAVs were carried out. The experimental results demonstrate NEFELI's ability to detect and track small UAVs at distances of up to 145 m in real-time speed of 6.7 fps.

Accelerating Transient Aerothermal Simulations of the BOLT II Flight Experiment

Aircraft with short flight durations may not reach thermal steady state due to limited time for aeroheating to conduct throughout their internal structure. Consequently, predictions of the solid temperature field require time-accurate simulation of the transient heat conduction. In this work, the use of an explicit super-time-stepping scheme to accelerate the time-accurate solution of the heat equation has been explored. The experience with this technique is documented here because, to the authors’ knowledge, it is not widely used in the hypersonics simulation community. The performance of the super-time-stepping scheme was investigated by comparison to a standard explicit scheme and an implicit time-differencing scheme. Both the super-time-stepping scheme and the implicit time-differencing scheme demonstrate significant speedup over the standard explicit scheme, with comparable wall-clock performance. The solid solver has been coupled with an existing open-source fluid solver to perform conjugate heat transfer simulations. The efficacy of the developed solver in simulating hypersonic aerothermal problems is demonstrated through verification and validation studies. By applying the coupled solver to the aerothermal analysis of the BOLT II flight experiment, the effectiveness of the super-time-stepping scheme in mitigating the computational expense associated with transient conjugate heat transfer simulations is demonstrated.

In-Situ Turbulence and Particulate Measurements in Support of the BOLT II Flight Experiment

The results of a measurement campaign to characterize the freestream atmospheric turbulence intensity and particulate concentrations experienced by the Boundary Layer Transition (BOLT) II flight experiment are described. These measurements were obtained using custom high-altitude balloon-borne payloads using a modified commercial optical particle counter and a high-bandwidth coldwire thermometer and hotwire anemometer, designed for low cost and light weight, with telemetered data so that physical payload recovery is not necessary. Sixteen flights were carried out over a 10-day campaign, with viable data returned from 14 flights, including four centered on the BOLT II launch with 1-h cadence. Turbulence and particulate measurements are shown vs altitude for all 14 data sets, along with statistics derived from the measurements, quantifying the BOLT II atmospheric environment for use in assessing the impact of these freestream disturbances on the hypersonic-vehicle boundary layer.

Development of the BOLT-2 Roughness Experiment for Flight

A sounding rocket research flight called BOLT-2, which stands for Boundary Layer Turbulence 2, was initiated with the goal of studying hypersonic boundary-layer transition and turbulence. The BOLT-2 research vehicle is based on a three-dimensional geometry with concave surfaces and swept leading edges that provides two separate and distinct, also redundant, flowpaths for conducting measurements. One side of BOLT-2 is dedicated to smooth surface transition and turbulence, to better study the natural instability processes, whereas the other has been assigned to study forced transition and turbulence using discrete roughness trips. The present paper is intended to document the primary drivers and decisions made that lead up to finalizing the roughness-side experiment for the BOLT-2 flight.

Design of an active wing-folding biomimetic flapping-wing air vehicle

In nature, birds and bats dynamically alter their wing shapes to suit various flight environments and tasks. This paper focuses on the design and validation of a biomimetic flapping-wing aerial vehicle, named FlexiWing, which features a unique mechanism for active wing deformation. This mechanism allows the wings to adjust their shapes flexibly in response to flight demands, significantly enhancing attitude control and maneuverability.’ ‘This study began with an in-depth exploration of biomimetic principles, focusing particularly on how birds and bats achieve precise control during flight through active wing deformation. Subsequently, we present a detailed account of the design and fabrication process of the active folding biomimetic flapping-wing aerial vehicle, including the design of mechanical mechanisms and material selection. Utilizing lightweight nylon materials and hollow carbon fiber rods, we successfully constructed a mechanically foldable wing structure. To achieve precise control over the aircraft’s movement, an embedded control system was designed, comprising an onboard embedded flight controller and ground-based equipment. The onboard controller uses a high-performance ESP32-C3 processor and a JY901 inertial measurement unit to acquire real-time attitude information of the aircraft. The control system incorporates Wi-Fi communication technology, enabling operators to send commands via a remote control or personal computer to manage flight modes and attitudes. Ultimately, a series of flight experiments were conducted to validate the performance of FlexiWing. The results demonstrate that FlexiWing exhibits remarkable maneuverability and stability, capable of achieving high-precision attitude control through active wing folding, making it adaptable to complex environments and tasks.’

Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification

Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.

Visual Odometry in GPS-Denied Zones for Fixed-Wing Unmanned Aerial Vehicle with Reduced Accumulative Error Based on Satellite Imagery

In this paper, we present a method for estimating GPS coordinates from visual information captured by a monocular camera mounted on a fixed-wing tactical Unmanned Aerial Vehicle at high altitudes (up to 3000 m) in GPS-denied zones. The main challenge in visual odometry using aerial images is the computation of the scale due to irregularities in the elevation of the terrain. That is, it is not possible to accurately convert from pixels in the image to meters in space, and the error accumulates. The contribution of this work is a reduction in the accumulated error by comparing the images from the camera with satellite images without requiring the dynamic model of the vehicle. The algorithm has been tested in real-world flight experiments at altitudes above 1000 m and in missions over 17 km. It has been proven that the algorithm prevents an increase in the accumulated error.

A multi-sensor fusion SLAM algorithm for indoor aerial robots

Nowdays, existing Light Detection and Ranging (LiDAR) based unknown-space-exploration Simultaneous Localization and Mapping (SLAM) methods have the problems of unstable mapping in feature-degraded environments and poor estimation accuracy in the vertical direction. To solve these problems, this study presents a multi-sensor fusion SLAM algorithm for indoor aerial robots as well as Unmanned Aerial Vehicles (UAVs). The sensors used in the multi-sensor fusion algorithm include optical flow sensor, barometer, Inertial Measurement Unit (IMU), and LiDAR. The proposed algorithm is named LiDAR-IMU-optical flow odometry fusion algorithm (Lioo). We derived and established a tightly coupled multi-sensor fusion framework based on factor graph optimisation. The IMU-optical flow pre-integration factor is proposed, and the barometer factor to the algorithm is added innovatively. Engineering applications are also realised in this paper. Flight experiments demonstrated that the proposed algorithm achieves more precise mapping and localisation of UAVs in indoor environments.

Flashlight model: Integrating attention distribution and attention resources for pilots’ visual behaviour analysis and performance prediction

Pilot performance is almost the last line of defense in aircraft safety. Recent years have seen a surge in research aimed at utilizing eye-tracking technology to predict pilot performance, enhancing aviation safety margins. A decline in pilot performance is often attributed to either misdirected attention towards irrelevant tasks or inefficient information processing owing to limited attention resources. Previous research has shown that eye-tracking data can effectively capture these issues and provide accurate performance predictions. Nevertheless, the existing studies either focus on attention distribution or attention resources separately, neglecting the complex interactions between them. To address this gap, our study proposes a synthesized Flashlight model-based eye-tracking analysis for pilot performance prediction, integrating the two perspectives. Accordingly, the combined AOI-gaze metrics are proposed to offer a more nuanced analysis of information processing across specific Areas of Interest (AOIs), thereby enhancing the analysis of gaze metrics. We examined the efficacy of the combined AOI-gaze metrics in the Gradient-boosted decision trees(GBDT) model for pilot performance prediction and compared them with other widely used eye-tracking metrics in a simulated flight experiment case study. Moreover, we employed the SHapley Additive exPlanations (SHAP) method to identify the most influential eye-tracking measurements for pilots’ performance prediction. The result demonstrated that the selected eye-tracking measurements obtained the highest accuracy in performance prediction.

Real-time vision-inertial landing navigation for fixed-wing aircraft with CFC-CKF

Vision-inertial navigation offers a promising solution for aircraft to estimate ego-motion accurately in environments devoid of Global Navigation Satellite System (GNSS). However, existing approaches have limited adaptability for fixed-wing aircraft with high maneuverability and insufficient visual features, problems of low accuracy and subpar real-time arise. This paper introduces a novel vision-inertial heterogeneous data fusion methodology, aiming to enhance the navigation accuracy and computational efficiency of fixed-wing aircraft landing navigation. The visual front-end of the system extracts multi-scale infrared runway features and computes geo-reference runway image as observation. The infrared runway features are recognized efficiently and robustly by a lightweight end-to-end neural network from blurry infrared images, and the geo-reference runway is generated through projection of the runway’s prior geographical information and prior pose. The fusion back-end of the navigation system is the Covariance Feedback Control based Cubature Kalman Filter (CFC-CKF) framework, which tightly integrates visual observations and inertial measurements for zero-drift pose estimation and curbs the effect of inaccurate kinematic noise statistics. Finally, real flight experiments demonstrate that the algorithm can estimate the pose at a frequency of 100 Hz and fulfill the navigation accuracy requirements for high-speed landing of fixed-wing aircraft.