Autonomous Flight Research Articles

Speed-First: An Aggressive Gradient-Based Local Planner for Quadrotor Faster Flight

Autonomous flight for quadrotors is maturing with the development of real-time local trajectory planning. However, the current local planning method is too conservative to waste the agility of the quadrotors. So in this paper, we have focused on aggressive local trajectory planning and proposed a gradient-based planning method to rapidly plan faster executable trajectories while ensuring it is collision-free. A distance gradient information generation strategy is proposed, which finds a collision-free Hybrid-A* path to replace the control points in obstacles for safety and creates the distance gradient used in the back-end optimization. Besides, we present a novel and aggressive time span cost term to tackle unfeasibility and improve the overall trajectory speed. Extensive simulations and real-world experiments are tested to validate our method. The results show that our proposed method generates a more aggressive trajectory with a shorter planning time and a faster flight speed than the classical gradient-based method.

Design and Development of Flapping Wing UAV

Flapping wing air vehicles are unmanned miniature aircraft’s that draw inspiration from birds, bats and insects. They utilize continuous flapping wing motion to generate aerodynamic forces and moments. The fluid structure interaction analysis of biological and artificial flapping flyers is extremely complicated thus an efficient unsteady aerodynamic model is required in such time consuming problems as optimal flapping wing simulation. This project deals with designing a flapping wing air vehicle which includes applying inertial impulsive force to UAV in such a way that the flapping wing air vehicle reaches almost constant flight speed after removing the initial impulsive forces. The flow dynamics are different of the flapping wing air vehicles with that of the fixed wing UAV and this project deals with the utilization of the flapping wing in the UAV to overcome the challenges faced by a fixed wing UAV like maneuverability in remote areas, sound produced and identification during mission. Structural and aerodynamic characteristics of a bird in flight offer benefits over typical propeller or rotor driven unmanned air vehicle (UAV). Hence this study deals with the designing of an flapping wing UAV to overcome these drawbacks of fixed wing UAV.

Realization of High Pressure Turbine Blades of a Small Turbo Fan Engine through Investment Casting Process

Turbo-fan gas turbine engines, or simply turbo-fan engines (STFEs), are used for powering both manned aircrafts and unmanned air vehicles (UAVs). The high pressure turbine section of such engines use Ni-base superalloy cast rotor blades because of their good mechanical properties at high operating temperatures. The present paper provides the technical/technological details of the various aspects associated with development and manufacturing of high pressure turbine blades (HPTBs) for utilization in an indigenously-developed small turbo-fan engine. Vacuum investment casting process has been adopted for realization of the above equiaxed components using a Ni-base superalloy

Toward the Light-weighted, Attachable, and Automated Control-enabled Gimbal Design for a Personal Weapon

A gimbal system is a mechanical apparatus that offers multiple degrees of freedom motions. Conventional gimbal motions are roll, pitch, and yaw (3 axes) or pan tilt motions (2 axes) in angles. In applications, these gimbal systems, in conjunction with cameras and other sensors, are used in footage recording for airplanes, helicopters, and UAVs (unmanned air vehicles) or as a handheld device for pictures. These applications are aligned with image tracking, surveillance, or even target tracking and engagement in more industry or military aspects.

Autonomous flight insurance method of unmanned aerial vehicles Parot Mambo using semantic segmentation data

Autonomous navigation of unmanned aerial vehicles (UAVs) has become in the past decade an extremely attracting topic, also due to the increasing availability of affordable equipment and open-source control and processing software environments. This demand has also raised a strong interest in developing accessible experimental platforms to train engineering students in the rapidly evolving area of autonomous navigation. In this paper, we describe a platform based on low-cost off-the-shelf hardware that takes advantage of the Matlab/Simulink programming environment to tackle most of the problems related to UAV autonomous navigation. More specifically, the subject of this paper is the autonomous control of the flight of a small UAV, which must explore and patrol an indoor unknown environment. Objectives: to analyse the existing hardware platforms for autonomous flight indoors, choose a flight exploration scenario of unknown premises, to formalize the procedure for obtaining a model of knowledge for semantic classification of premises, to formalize obtaining distance to obstacles using data camera horizontally employment and building on its barrier map. Namely, we use the method of image segmentation based on the brightness threshold, a method of training the semantic segmentation network, and computer algorithms in probabilistic robotics for mobile robots. We consider both the case of navigation guided by structural visual information placed in the environment, e.g., contrast markers for flight (such as path marked by a red tape), and the case of navigation based on unstructured information such as recognizable objects or human gestures. Basing on preliminary tests, the most suitable method for autonomous in-door navigation is by using\ object classification and segmentation, so that the UAV gradually analyses the surrounding objects in the room and makes decisions on path planning. The result of our investigation is a method that is suitable to allow the autonomous flight of a UAV with a frontal video camera. Conclusions. The scientific novelty of the obtained results is as follows: we have improved the method of autonomous flight of small UAVs by using the semantic network model and determining the purpose of flight only at a given altitude to minimize the computational costs of limited autopilot capabilities for low-cost small UAV models. The results of our study can be further extended by means of a campaign of experiments in different environments.

Simultaneous UAV having actively sweep angle morphing wing and flight control system design

PurposeThe purpose of this paper is to improve autonomous flight performance of an unmanned aerial vehicle (UAV) having actively sweep angle morphing wing using simultaneous UAV and flight control system (FCS) design.Design/methodology/approachAn UAV is remanufactured in the ISTE Unmanned Aerial Vehicle Laboratory. Its wing sweep angle can vary actively during flight. FCS parameters and wing sweep angle are simultaneously designed to optimize autonomous flight performance index using a stochastic optimization method called as simultaneous perturbation stochastic approximation (SPSA). Results obtained are applied for flight simulations.FindingsUsing simultaneous design process of an UAV having actively sweep angle morphing wing and FCS design, autonomous flight performance index is maximized.Research limitations/implicationsAuthorization of Directorate General of Civil Aviation in Turkey is crucial for real-time UAV flights.Practical implicationsSimultaneous UAV having actively sweep angle morphing wing and FCS design process is so beneficial for recovering UAV autonomous flight performance index.Social implicationsSimultaneous UAV having actively sweep angle morphing wing and FCS design process achieves confidence, high autonomous performance index and simple service demands of UAV operators.Originality/valueComposing a novel approach to improve autonomous flight performance index (e.g. less settling and rise time, less overshoot meanwhile trajectory tracking) of an UAV and creating an original procedure carrying out simultaneous UAV having actively sweep angle morphing wing and FCS design idea.

Thermal monitoring of live-line power transmission lines with an infrared camera mounted on an unmanned aerial vehicle

This study proposes a thermal monitoring method in outdoor environments to inspect the health state of overhung transmission lines (TLs) with an infrared camera mounted on an unmanned aerial vehicle (UAV). The suggested method features three characteristics. First, the thermal energy of TLs measured by an infrared camera is converted into accurate temperature accounting for the conversion principle of the infrared camera. Characteristic parameters in the conversion principle, including emissivity, transmissibility, and reflectability, are systematically quantified through experiments. Second, a simple yet effective postprocessing method is proposed to eliminate thermal energy emitted from other heat sources in outdoor environments. Third, an automatic line-tracking method is proposed to focus on TLs for reliable thermal imaging during patrol inspection. The proposed method with an infrared camera deployed on a UAV is field-tested through an autonomous flight and gimbal control. A systemic analysis on field tests demonstrates that the proposed method overcomes several bottlenecks in current technologies and reveals several important considerations regarding thermally monitoring overhung TLs in outdoor environments. This insightful discovery supports developing a fully autonomous solution for accurate outdoor thermal imaging despite the many challenges that remain.

An Intelligence Attitude Controller Based on Active Disturbance Rejection Control Technology for an Unmanned Helicopter

To solve the problems of complex parameter tuning of traditional active disturbance rejection controller (ADRC) and low adaptive ability of extended state observer, this paper designs a novel intelligent attitude controller based on active disturbance rejection control technology. The adaptive radial basis function neural network can improve the adaptability of the extended state observer, and the fuzzy control can adjust the parameters of the state error nonlinear control law online. The intelligent ADRC controller can eliminate the influence of internal and external disturbances on the unmanned helicopter, and reduce the difficulty of controller parameter tuning. Based on the intelligent ADRC controller, this paper designs an intelligent attitude control system for a small unmanned helicopter and completes the attitude control test. The corresponding test results demonstrate that the intelligent attitude controller has excellent anti-disturbances, robustness and tracking ability, and the control effect is better than that of the traditional ADRC controller.

RAPID: Approximate Pipelined Soft Multipliers and Dividers for High Throughput and Energy Efficiency

The rapid updates in error-resilient applications along with their quest for high throughput have motivated designing fast approximate functional units for Field-Programmable Gate Arrays (FPGAs). Studies that proposed imprecise functional techniques are posed with three shortcomings: first, most inexact multipliers and dividers are specialized for Application-Specific Integrated Circuit (ASIC) platforms. Second, state-of-the-art (SoA) approximate units are substituted, mostly in a single kernel of a multi-kernel application. Moreover, the end-to-end assessment is adopted on the Quality of Results (QoR), but not on the overall gained performance. Finally, existing imprecise components are not designed to support a pipelined approach, which could boost the operating frequency/throughput of, e.g., division-included applications. In this paper, we propose RAPID, the first pipelined approximate multiplier and divider architecture, customized for FPGAs. The proposed units efficiently utilize 6-input Look-up Tables (6-LUTs) and fast carry chains to implement Mitchell's approximate algorithms. Our novel error-refinement scheme not only has negligible overhead over the baseline Mitchell's approach but also boosts its accuracy to 99.4% for arbitrary size of multiplication and division. Experimental results demonstrate the efficiency of the proposed pipelined and non-pipelined RAPID multipliers and dividers over accurate counterparts. Moreover, the end-to-end evaluations of RAPID, deployed in three multi-kernel applications in the domains of bio-signal processing, image processing, and moving object tracking for Unmanned Air Vehicles (UAV) indicate up to 45% improvements in area, latency, and Area-Delay-Product (ADP), respectively, over accurate kernels, with negligible loss in QoR.

Distributed cooperative obstacle avoidance and formation reconfiguration for multiple quadrotors: Theory and experiment

With the increasing demand for the application of multiple quadrotors, improving their autonomous flight ability in unknown environments has become one of the research focuses in the robot field. In this paper, a distributed obstacle avoidance controller is proposed with three elaborate control terms: obstacle avoidance term, inter-robot collision avoidance term, and formation reconstruction. Firstly, an online local obstacle avoidance algorithm is designed based on the obstacle avoidance behavior of the pigeon flock and potential field theory. Then, inspired by the spring-damped system, an inter-robot collision avoidance algorithm is designed based on Hooke's law with damping. Furthermore, based on the consensus theory and local neighboring information, a distributed formation reconstruction controller is designed. Finally, the simulation and quantitative analysis are carried out in Gazebo simulation platform. For practical application, an outdoor experimental platform with six quadrotors is constructed to verify the practicability of the proposed method. The results show that the three control terms are compatible with each other to achieve obstacle avoidance and can achieve formation control for quadrotors.

GNSS-Denied Semi-Direct Visual Navigation for Autonomous UAVs Aided by PI-Inspired Inertial Priors

This article proposes a method to diminish the horizontal position drift in the absence of GNSS (Global Navigation Satellite System) signals experienced by the VNS (Visual Navigation System) installed onboard a UAV (Unmanned Air Vehicle) by supplementing its pose estimation non-linear optimizations with priors based on the outputs of the INS (Inertial Navigation System). The method is inspired by a PI (Proportional Integral) control loop, in which the attitude and altitude inertial outputs act as targets to ensure that the visual estimations do not deviate past certain thresholds from their inertial counterparts. The resulting IA-VNS (Inertially Assisted Visual Navigation System) achieves major reductions in the horizontal position drift inherent to the GNSS-Denied navigation of autonomous UAVs. Stochastic high-fidelity Monte Carlo simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results and to analyze their sensitivity to the terrain type overflown by the aircraft. The authors release the C++ implementation of both the navigation algorithms and the high-fidelity simulation as open-source software.

Autonomous Maneuver Decision-Making of UCAV with Incomplete Information in Human-Computer Gaming

In human-computer gaming scenarios, the autonomous decision-making problem of an unmanned combat air vehicle (UCAV) is a complex sequential decision-making problem involving multiple decision-makers. In this paper, an autonomous maneuver decision-making method for UCAV that considers the partially observable states of Human (the adversary) is proposed, building on a game-theoretic approach. The maneuver decision-making process within the current time horizon is modeled as a game of Human and UCAV, which significantly reduces the computational complexity of the entire decision-making process. In each established game decision-making model, an improved maneuver library that contains all possible maneuvers (called the continuous maneuver library) is designed, and each of these maneuvers corresponds to a mixed strategy of the established game. In addition, the unobservable states of Human are predicted via the Nash equilibrium strategy of the previous decision-making stage. Finally, the effectiveness of the proposed method is verified by some adversarial experiments.

What kind of gully can develop into benggang?

Benggang is a special landform with serious erosion damage, resulting in intensive soil erosion and land degradation. However, few studies have been conducted on prediction and critical conditions of benggang formation. This study analyzed the topography, hydrogeological conditions, number and size of benggang in Yuankengshui watershed (Guangdong, China) by extracting a digital orthophoto map (DOM) and digital surface models (DSMs) based on unmanned air vehicle (UAV) aerial images. The benggang number was found to increase first and then decrease with the increase of elevation, slope gradient and slope length, in contrast to a decrease with the increase of benggang catchment area. The elevation distribution is consistent between the small watershed and regional benggang, with the maximum benggang density in the elevation range of 200–250 m. The maximum benggang number is in the area with a slope gradient and slope length of 25-26° and 61–70 m, respectively, and the average benggang catchment area is 0.3 ha. Benggang formation and development was mainly affected by benggang catchment area and slope drop, whose correlation coefficients with benggang area and collapsing wall height were 0.957, 0.633 and 0.581, 0.568, respectively (P < 0.01, n = 296), enabling the establishment of their relationship with benggang development, which can be used to estimate whether the hydrological condition of the slope meets the conditions for benggang formation. Collapsing wall height is an important indicator of benggang formation, and under the conditions that the predicted value of the developed gully depth is greater than 3 m and the granite weathering crust is more than 10 m, the gully in such an area can develop into benggang. Meanwhile, benggang is shown to differ from gully in catchment area, lithology demand, shape, dominant erosion type, and erosion modulus. Therefore, benggang and gully should be treated differently when adopting control measures. This study may enrich our understanding and prediction of benggang development.

CCTseg: A cascade composite transformer semantic segmentation network for UAV visual perception

Semantic segmentation could obtain the pixel level classification of surrounding environments which is an essential task for autonomous vehicles and mobile robots visual perception. Most existing semantic segmentation networks were focused on the visual perception of autonomous vehicles. Little attention is paid to the semantic segmentation for UAV (Unmanned Aerial Vehicle) visual perception, which is crucial to UAV autonomous flight and landing spot searching. Compared with views from autonomous vehicles, the UAV-based views were more challenging for the semantic segmentation task due to images captured by UAV containing large-scale variation of objects size caused by different altitude and angle. The existing semantic segmentation networks for the visual perception of autonomous vehicles are generally inadequate to effectively extract the representative features of UAV images which required contain context information and local information simultaneously. A cascade composite transformer-based semantic segmentation network is proposed in this study for UAV visual perception. A cascade composite encoder is designed which consists of three transformer-based feature extraction backbones and cascade fused low-level features, middle-level features and high-level features to achieve better feature representation capacity. The spatial enhanced transformer block is implemented as the basic feature extraction block of each backbone to make the extracted features contain context information of environments and local information of objects. A symmetric rhombus decoder is proposed to integrate multi-stage features and make fully utilise of middle stage features which contained abundance of useful information, thus accurately pixel level prediction could be obtained in this way. Ablation studies and comparison experiments for the proposed CCTseg have been conducted on two public UAV imagery datasets suitable for UAV autonomous flight and landing spot observing. Experimental results have demonstrated the effectiveness of the proposed network structure and the superiority of proposed network over other state-of-the-art methods for the semantic segmentation of UAV visual perception.

Fast and High-Quality Monocular Depth Estimation with Optical Flow for Autonomous Drones

Recent years, autonomous drones have attracted attention in many fields due to their convenience. Autonomous drones require precise depth information so as to avoid collision to fly fast and both of RGB image and LiDAR point cloud are often employed in applications based on Convolutional Neural Networks (CNNs) to estimate the distance to obstacles. Such applications are implemented onboard embedded systems. In order to precisely estimate the depth, such CNN models are in general so complex to extract many features that the computational complexity increases, requiring long inference time. In order to solve the issue, we employ optical flow to aid in-depth estimation. In addition, we propose a new attention structure that makes maximum use of optical flow without complicating the network. Furthermore, we achieve improved performance without modifying the depth estimator by adding a perceptual discriminator in training. The proposed model is evaluated through accuracy, error, and inference time on the KITTI dataset. In the experiments, we have demonstrated the proposed method achieves better performance by up to 34% accuracy, 55% error reduction and 66% faster inference time on Jetson nano compared to previous methods. The proposed method is also evaluated through a collision avoidance in simulated drone flight and achieves the lowest collision rate of all estimation methods. These experimental results show the potential of proposed method to be used in real-world autonomous drone flight applications.

Development of flapping wing robot and vision-based obstacle avoidance strategy.

Due to the flight characteristics such as small size, low noise, and high efficiency, studies on flapping wing robots are being actively conducted. In particular, the flapping wing robot is in the spotlight in the field of search and reconnaissance. Most of the research focuses on the development of flapping wing robots rather than autonomous flight. However, because of the unique characteristics of flapping wings, it is essential to consider the development of flapping wing robots and autonomous flight simultaneously. In this article, we describe the development of the flapping wing robot and computationally efficient vision-based obstacle avoidance algorithm suitable for the lightweight robot. We developed a 27 cm and 45 g flapping wing robot named CNUX Mini that features an X-type wing and tailed configuration to attenuate oscillation caused by flapping motion. The flight experiment showed that the robot is capable of stable flight for 1.5 min and changing its direction with a small turn radius in a slow forward flight condition. For the obstacle detection algorithm, the appearance variation cue is used with the optical flow-based algorithm to cope robustly with the motion-blurred and feature-less images obtained during flight. If the obstacle is detected during straight flight, the avoidance maneuver is conducted for a certain period, depending on the state machine logic. The proposed obstacle avoidance algorithm was validated in ground tests using a testbed. The experiment shows that the CNUX Mini performs a suitable evasive maneuver with 90.2% success rate in 50 incoming obstacle situations.

AI-powered inspections of facades in reinforced concrete buildings

Worldwide there are plenty of aged Reinforced Concrete (RC) buildings in need of thorough inspections. Cracks, delamination, stains, leakages, debonding and moisture ingressions are common defects found in RC structures. Such problems are typically diagnosed through qualitative assessment of visual and thermal photographs (data) by certified inspectors. However, qualitative inspections are very tedious, time-consuming and costly. This paper presents an alternative novel approach to drastically increase efficiency by decreasing the data collection and analysis time. Data collection for the inspection of facades is undertaken with Unmanned Aerial Vehicles (UAVs) either through an autonomous pre-programmed flight or through a human-piloted flight. Data analysis is performed by implementing up-to-date AI-powered algorithms to automatically detect defects on visual and thermal photographs. All the recognised defects and thermal anomalies are labelled on the building facade for comprehensive evaluation of the asset. This paper reports that the implementation of AIpowered inspections can save up to 67% of the time spent and 52% of the cost in comparison to the most commonly adopted practice in the industry with an average accuracy of 90.5% and 82% for detection of visual defects and thermal anomalies, respectively.

Design and Application of a UAV Autonomous Inspection System for High-Voltage Power Transmission Lines

As the scale of the power grid continues to expand, the human-based inspection method struggles to meet the needs of efficient grid operation and maintenance. Currently, the existing UAV inspection system in the market generally has short endurance power time, high flight operation requirements, low degree of autonomous flight, low accuracy of intelligent identification, slow generation of inspection reports, and other problems. In view of these shortcomings, this paper designs an intelligent inspection system based on self-developed UAVs, including autonomous planning of inspection paths, sliding film control algorithms, mobile inspection schemes and intelligent fault diagnosis. In the first stage, basic data such as latitude, longitude, altitude, and the length of the cross-arms are obtained from the cloud database of the power grid, while the lateral displacement and vertical displacement during the inspection drone operation are calculated, and the inspection flight path is generated independently according to the inspection type. In the second stage, in order to make the UAV’s flight more stable, the reference-model-based sliding mode control algorithm is introduced to improve the control performance. Meanwhile, during flight, the intelligent UAV uploads the captured photos to the cloud in real time. In the third stage, a mobile inspection program is designed in order to improve the inspection efficiency. The transfer of equipment is realized in the process of UAV inspection. Finally, to improve the detection accuracy, a high-precision object detector is designed based on the YOLOX network model, and the improved model increased the mAP0.5:0.95 metric by 2.22 percentage points compared to the original YOLOX_m for bird’s nest detection. After a large number of flight verifications, the inspection system designed in this paper greatly improves the efficiency of power inspection, shortens the inspection cycle, reduces the investment cost of inspection manpower and material resources, and successfully fuses the object detection algorithm in the field of high-voltage power transmission lines inspection.

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

Unmanned air vehicles (UAVs) have been regularly employed in modern wars to conduct different missions. Instead of addressing mission planning and route planning separately, this study investigates the issue of joint mission and route planning for a fleet of UAVs. The mission planning determines the configuration of weapons in UAVs and the weapons to attack targets, while the route planning determines the UAV's visiting sequence for the targets. The problem is formulated as an integer linear programming model. Due to the inefficiency of CPLEX on large scale optimization problems, an effective learning-based heuristic, namely, population based adaptive large neighborhood search (P-ALNS), is proposed to solve the model. In P-ALNS, seven neighborhood structures are designed and adaptively utilized in terms of their historical performance. The effectiveness and superiority of the proposed model and algorithm are demonstrated on test instances of small, medium and large sizes. In particular, P-ALNS achieves comparable solutions or as good as those of CPLEX on small-size (20 targets) instances in much shorter time.

Robust Reduced Order Thau Observer With the Adaptive Fault Estimator for the Unmanned Air Vehicles

Developing fault detection and diagnoses algorithms for the unmanned air vehicles such as the quadrotors is challenging since they are intrinsically non-linear, time-varying, unstable, and uncertain. This paper develops a reduced order Thau observer by only considering the uncertain rotational dynamics, which are re-constructed as the dominant linear and non-linear for the design purpose. Therefore, the proposed Thau observer is just third order and can reveal a rotational state estimation error in the presence of the quadrotor faults. This paper also equips the proposed Thau observer with a simple online adaptive fault estimation law, which is able to recognize up to two faulty actuators instantly using the estimated rotational state error. Lyapunov analysis confirms the error convergence in both the Thau observer states and the adaptive fault estimates. In addition, this paper constructs a batch type least-squares projection approach to quantify the magnitude percentages of the actuator failures. Moreover, to show the feasibility of the proposed algorithm, this paper extensively analyses the fault detection and diagnosis results performed in the simulation and real-time environments. Finally, to demonstrate the superiority of the proposed algorithm, it is compared with a recent Kalman filter based quadrotor fault estimation research under the equal conditions.