Micro Air Vehicles Research Articles

Towards a Reduced Dependency Framework for Autonomous Unified Inspect-Explore Missions

The task of establishing and maintaining situational awareness in an unknown environment is a critical step to fulfil in a mission related to the field of rescue robotics. Predominantly, the problem of visual inspection of urban structures is dealt with view-planning being addressed by map-based approaches. In this article, we propose a novel approach towards effective use of Micro Aerial Vehicles (MAVs) for obtaining a 3-D shape of an unknown structure of objects utilizing a map-independent planning framework. The problem is undertaken via a bifurcated approach to address the task of executing a closer inspection of detected structures with a wider exploration strategy to identify and locate nearby structures, while being equipped with limited sensing capability. The proposed framework is evaluated experimentally in a controlled indoor environment in presence of a mock-up environment validating the efficacy of the proposed inspect-explore policy.

Passive attitude stabilization of ionic-wind-powered micro air vehicles

The ionic-wind-powered Micro Air Vehicles (MAVs) can achieve a higher thrust-to-weight ratio than other MAVs. However, this kind of MAV has not yet achieved controlled flight because of the unstable thrust produced by the ionic wind and the dynamic instability related to the small size. In this paper, a passive attitude stabilization method of the ionic-wind-powered MAV using air dampers is introduced. The key factors that influence the performance of the air dampers, including the layout, position, and area of the air dampers, are theoretically studied. The appropriate optimal position of the air dampers is also obtained by Monte Carlo stochastic simulations. Then the proposed passive attitude stabilization method is applied to the ionic-wind-powered MAVs of different wingspan (2 cm and 6.3 cm). Finally, the experimental results show that using the proposed method, attitude stabilization is achieved for the first time for the ionic-wind-powered MAV. Moreover, the altitude control of an ionic-wind-powered MAV with a wingspan of 6.3 cm is also demonstrated.

High actuation capability and smooth-deformation piezo morphing wing based on multi-layer parallel pre-compressed MFC bimorph

A single macro-fiber composite (MFC) unimorph or bimorph wing can only be applied to micro aerial vehicles (MAV) at speeds lower than 20 m/s owing to its relatively small output work. In this study, a high-actuation MFC pre-compressed bimorph actuator (MFC-PBA) multilayer parallel morphing wing is proposed for application in high-speed miniature unmanned aerial vehicles (mini-UAVs). The morphing wing adopts the proposed ribs with an arc groove and three parallel layers of MFC-PBAs to improve the output work and guarantee smooth morphing wing surface. The large deformation finite element (FE) models of a single MFC-PBA and its multilayer parallel morphing wing are established to evaluate the output deformation and output force. Subsequently, a prototype of the morphing wing is fabricated, and the actuation capabilities of the single MFC-PBAs and morphing wing are tested. The experimental and FE simulation results satisfactorily agree. The results show that at the peak driving voltage, with axial compression forces of 18 and 27 N applied to the side and middle MFC-PBAs, respectively, the deflection angle of the morphing wing reaches ±12°, which is twice that of the morphing wing without compression; and its blocking force is maintained at 1.682 N; thus, its output work has doubled. The multiphysics model of the proposed morphing wing under airflow is established to evaluate the deformation capability. The results show that the present morphing wing achieves a deflection of 5.04° on the side of the incoming flow direction, and a unit lift of 249.1 N/m at an air speed of 40 m/s and angle of attack of 15°. Consequently, the proposed morphing wing exhibits a high deformation capability. We expect the proposed morphing wing will be used for the flight control of mini-UAVs at higher speeds and larger sizes.

LightMAN: A Lightweight Microchained Fabric for Assurance- and Resilience-Oriented Urban Air Mobility Networks

Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm have led to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed Internet of drones (IoD) in smart cities has also brought up new concerns regarding performance, security, and privacy. The centralized framework adopted by conventional UAM networks is not adequate to handle high mobility and dynamicity. Moreover, it is necessary to ensure device authentication, data integrity, and privacy preservation in UAM networks. Thanks to its characteristics of decentralization, traceability, and unalterability, blockchain is recognized as a promising technology to enhance security and privacy for UAM networks. In this paper, we introduce LightMAN, a lightweight microchained fabric for data assurance and resilience-oriented UAM networks. LightMAN is tailored for small-scale permissioned UAV networks, in which a microchain acts as a lightweight distributed ledger for security guarantees. Thus, participants are enabled to authenticate drones and verify the genuineness of data that are sent to/from drones without relying on a third-party agency. In addition, a hybrid on-chain and off-chain storage strategy is adopted that not only improves performance (e.g., latency and throughput) but also ensures privacy preservation for sensitive information in UAM networks. A proof-of-concept prototype is implemented and tested on a micro-air–vehicle link (MAVLink) simulator. The experimental evaluation validates the feasibility and effectiveness of the proposed LightMAN solution.

Computation of the Aerodynamic Forces, Design and Development of an Orinthopter

An aircraft flying by flapping its wings is called as ornithopter. Ornithopters are called as flapping-wing models and usually built to the size of birds. The wings of the ornithopter generate lift as well as thrust. For the low Reynolds number range, flapping wings are the most efficient means of propulsion, which is why a lot of MAVs (micro aerial vehicles) have flapping wings. They also have the added advantage of better manoeuvrability, as natural flyers such as birds and insects that demonstrate superior flight skills. Ornithopters are used for military applications such as aerial reconnaissance and animal behavioural research since they are made to resemble birds. The present work focuses on the computation of the aerodynamic forces, design and fabrication of an ornithopter. A code is developed to compute the lift, thrust and drag forces generated by the wings using a theoretical model of equations. The design proposed in this study uses a transverse shaft gear system for the power train mechanism to flap the wings. The model was tested and optimizations were done for improving the flight.

Place Recognition with Memorable and Stable Cues for Loop Closure of Visual SLAM Systems

Visual Place Recognition (VPR) is a fundamental yet challenging task in Visual Simultaneous Localization and Mapping (V-SLAM) problems. The VPR works as a subsystem of the V-SLAM. VPR is the task of retrieving images upon revisiting the same place in different conditions. The problem is even more difficult for agricultural and all-terrain autonomous mobile robots that work in different scenarios and weather conditions. Over the last few years, many state-of-the-art methods have been proposed to solve the limitations of existing VPR techniques. VPR using bag-of-words obtained from local features works well for a large-scale image retrieval problem. However, the aggregation of local features arbitrarily produces a large bag-of-words vector database, limits the capability of efficient feature learning, and aggregation and querying of candidate images. Moreover, aggregating arbitrary features is inefficient as not all local features equally contribute to long-term place recognition tasks. Therefore, a novel VPR architecture is proposed suitable for efficient place recognition with semantically meaningful local features and their 3D geometrical verifications. The proposed end-to-end architecture is fueled by a deep neural network, a bag-of-words database, and 3D geometrical verification for place recognition. This method is aware of meaningful and informative features of images for better scene understanding. Later, 3D geometrical information from the corresponding meaningful features is computed and utilised for verifying correct place recognition. The proposed method is tested on four well-known public datasets, and Micro Aerial Vehicle (MAV) recorded dataset for experimental validation from Victoria Park, Adelaide, Australia. The extensive experimental results considering standard evaluation metrics for VPR show that the proposed method produces superior performance than the available state-of-the-art methods.

A Conditional Privacy Preserving Generalized Ring Signcryption Scheme for Micro Aerial Vehicles.

Micro Aerial Vehicles (MAVs) are a type of UAV that are both small and fully autonomous, making them ideal for both civilian and military applications. Modern MAVs can hover and navigate while carrying several sensors, operate over long distances, and send data to a portable base station. Despite their many benefits, MAVs often encounter obstacles due to limitations in the embedded system (such as memory, processing power, energy, etc.). Due to these obstacles and the use of open wireless communication channels, MAVs are vulnerable to a variety of cyber-physical attacks. Consequently, MAVs cannot execute complex cryptographic algorithms due to their limited computing power. In light of these considerations, this article proposes a conditional privacy-preserving generalized ring signcryption scheme for MAVs using an identity-based cryptosystem. Elliptic Curve Cryptography (ECC), with a key size of 160 bits, is used in the proposed scheme. The proposed scheme's security robustness has been analyzed using the Random Oracle Model (ROM), a formal security evaluation method. The proposed scheme is also compared in terms of computation cost, communication cost and memory overhead against relevant existing schemes. The total computation cost of the proposed scheme is 7.76 ms, which is 8.14%, 5.20%, and 11.40% schemes. The results show that the proposed scheme is both efficient and secure, proving its viability.

Design, Analysis and Prototyping of One-DoF Drive Mechanism for Flapping Wing Micro-aerial Vehicle Application

Design, Analysis and Prototyping of One-DoF Drive Mechanism for Flapping Wing Micro-aerial Vehicle Application

Derivative-Free Observability Analysis for Sensor Placement Optimization of Bioinspired Flexible Flapping Wing System.

Observability analysis of a bioinspired flexible flapping wing system provides a measure of how well the states of flexible flapping wing micro-aerial vehicles can be estimated from real-time measurements during high-speed flight. However, the traditional observability analysis approaches have trouble in terms of lack of quantitative analysis index, high computational complexity, low accuracy, and unavailability in stochastic systems with memory, including bioinspired flexible flapping wing systems. Therefore, a novel derivative-free observability analysis method is proposed here based on the generalized polynomial chaos expansion. By formulating a surrogate model to represent the relationship between the cumulative measurement and the random initial state, the observability coefficient matrix is calculated and the observability rank condition is stated. Consequently, several observability indices are proposed to quantity the observability of the system. Altogether, the proposed method avoids the disadvantages of the traditional approaches, especially in assessing the observability degree of each state and the effect of stochastic noise on observability. The validation of the proposed method is first provided by demonstrating the equivalence between the traditional and proposed methods and subsequently by comparing the observability of the Lorenz system calculated via three different approaches. Finally, the proposed method is applied on a bioinspired flexible wing system to optimize the placement of sensors, which is consistent with the natural configuration of campaniform sensilla on the wing of the hawkmoth.

Efficient and Consistent Two Key-Frame Visual-Inertial Navigation Using Matrix Lie Groups

Abstract This paper presents the design of a two key-frame visual-inertial navigation system (2KF-VINS) using a combined Lie group SE2(3) extended Kalman filter (EKF) design framework. The conventional 2KF-VINS filter is unobservable for translations along all three axes and rotation about the gravity direction. As a result, the filter suffers from estimation inconsistencies related to unobservable transformations of the estimation problem. The proposed combined Lie group SE2(3) framework remedies this issue by implicitly preserving the observability consistency property of the filter. Monte Carlo numerical simulations are used to validate the theoretical performance of the right−SE2(3) 2KF-VINS, along with experimental validation using the EuRoC micro aerial vehicle (MAV) dataset to evaluate the performance in real-world scenarios. Additionally, the proposed algorithm is compared with state-of-the-art MSCKF, RI-MSCKF, left−SO(3), and right−SO(3) 2KF-VINS versions with identical and realistic tuning parameters to validate the performance related to the accuracy, consistency, and computational speed of the method.

Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs.

This paper presents a system for behavioral, environmental, and physiological monitoring of working dogs using on-body and aerial sensors. The proof of concept study presented here includes two trained dogs performing nine scent detection tasks in an uncontrolled environment encompassing approximately two acres. The dogs were outfitted with a custom designed wearable harness to monitor their heart rate, activity levels and skin temperature. We utilized a commercially available micro-air vehicle to perform aerial sensing by tracking the terrain and movement of the dog in the outdoor space. The dogs were free to explore the space working at maximal speeds to complete a scent-based search-and-retrieval task. Throughout the experiment, the harness data was transferred to a base station via Wi-Fi in real-time. In this work, we also focused on testing the performance of a custom 3D electrode with application specific ergonomic improvements and adaptive filter processing techniques to recover as much electrocardiography data as possible during high intensity motion activity. We were able to recover and use 84% of the collected data where we observed a trend of heart rate generally increasing immediately after successful target localization. For tracking the dogs in the aerial video footage, we applied a state-of-the-art deep learning algorithm designed for online object tracking. Both qualitative and quantitative tracking results are very promising. This study presents an initial effort towards deployment of on-body and aerial sensors to monitor the working dogs and their environments during scent detection and search and rescue tasks in order to ensure their welfare, enable novel dog-machine interfaces, and allow for higher success rate of remote and automated task performance.

Robust and Efficient Velocity Estimation for MAVs With an RGB-D Camera

Micro Aerial Vehicles (MAVs) have become increasingly popular due to their wide applications in many areas. For autonomous navigation and safe control of MAVs, it is essential to have accurate and reliable velocity and position estimation. However, due to limited computational power and payload, it is still challenging for autonomous operation of MAVs in complex environments. In this paper, we propose a robust and efficient velocity estimation framework for MAVs with a single downward-facing RGB-D camera, which is able to provide metric velocity estimation in three dimensions as well as yaw rate in real-time without the fusion of additional sensors. Unlike traditional algorithms, our method uses kernel cross-correlators (KCCs) to efficiently determine optical flow for motion estimation, which does not rely on costly feature extraction and matching process. Moreover, we utilize depth images to estimate the vertical velocity of MAVs without the assumption of a flat ground. Autonomous flight tests on a quadrotor in complex environments demonstrate the robustness and efficiency of our method.

Battle the Wind: Improving Flight Stability of a Flapping Wing Micro Air Vehicle Under Wind Disturbance With Onboard Thermistor-Based Airflow Sensing

Flyers in nature equip different airflow sensing mechanisms to navigate through wind disturbances with remarkable flight stability. Embracing bio-inspiration, airflow sensing with conventional sensors has long been utilized in flight control for larger micro air vehicles (MAVs). Bio-inspired flapping wing MAVs (FWMAVs) have extremely limited power and payload, therefore implementing onboard airflow sensing has remained a challenge in spite of various attempts at miniaturized airflow sensor designs. This work characterizes the measurement performance of a lightweight off-the-shelf thermistor-based airflow sensor through comparison with a hot-wire probe. Wind tunnel tethered flight tests on a 31.3-gram FWMAV Delfly Nimble examine the onboard sensing performance at low flow speeds (up to 2 m/s), under the influence of flapping motion. This performance characterization further motivates a miniaturized re-design of the airflow sensor with over 40% size and weight reduction. The redesigned airflow sensor helps to realize the first flapping wing MAV free flight with onboard airspeed measurements, providing remarkable flight stability under wind speeds in the range of approximately 0.5 to 1.2 m/s. This embodied sensing configuration pushes the weight and power limit of miniaturized electronics for FWMAVs, providing an easy-to-integrate solution with good performance, and paving the way for more complex control of FWMAVs in dynamic conditions.

Frequency-Modulated Control for Insect-Scale Flapping-Wing Vehicles

Many insects utilize both amplitude and frequency modulation of their wingstrokes to control flight, a combination with potential advantages for flapping-wing micro-aerial vehicle (FWMAV) flight control as we attempt to mimic the agility of small biological fliers. However, frequency-modulated control is uncommon in insect-scale FWMAVs, and these vehicles have not yet demonstrated graceful, mid-wingstroke frequency transitions in flight. We propose a method to allow active frequency control as a primary control variable, and describe a method to achieve smooth frequency transitions in flight. We demonstrate that frequency, in combination with other flapping parameters, may be used to generate arbitrary, time-varying forces and torques. Additionally, we explore the advantages of frequency modulation in FWMAV flight, applying a frequency-based method to control the Harvard RoboBee during hovering flight.

Position and Attitude Tracking of MAV Quadrotor Using SMC-Based Adaptive PID Controller

A micro air vehicle (MAV) is physically lightweight, such that even a slight perturbation could affect its attitude and position tracking. To attain better autonomous flight system performance, MAVs require good control strategies to maintain their attitude stability during translational movement. However, the available control methods nowadays have fixed gain, which is associated with the chattering phenomenon and is not robust enough. To overcome the aforementioned issues, an adaptive proportional integral derivative (PID) control scheme is proposed. An adaptive mechanism based on a second-order sliding mode control is used to tune the parameter gains of the PID controller, and chattering phenomena are reduced by a fuzzy compensator. The Lyapunov stability theorem and gradient descent approach were the basis for the automated tuning. Comparisons between the proposed scheme against SMC-STA and SMC-TanH were also made. MATLAB Simulink simulation results showed the overall favourable performance of the proposed scheme. Finally, the proposed scheme was tested on a model-based platform to prove its effectiveness in a complex real-time embedded system. Orbit and waypoint followers in the platform simulation showed satisfactory performance for the MAV in completing its trajectory with the environment and sensor models as perturbation. Both tests demonstrate the advantages of the proposed scheme, which produces better transient performance and fast convergence towards stability.

Optimized Uncertainty and Disturbance Estimator Based Robust Controller for Micro Aerial Vehicle

Abstract The work presents optimized uncertainty and disturbance estimator (UDE) based robust controller to achieve the fixed wing micro-aerial vehicle (MAV) longitudinal and lateral stability. In the proposed control methodology, genetic algorithm (GA) is used to find the optimal value of UDE filter parameter (GAUDE) which is filter time constant. GA uses minimization of integral absolute time error (IATE) based fitness function. In this work, the proposed controller is GAUDE-based adaptive sliding mode control (SMC). The Lyapunov theory is used to establish the stability of the presented controller. The performance of proposed SMC-GAUDE controller is analyzed through comparative analysis using numerical simulations. The comparative analysis consists of the proposed controller performance evaluation with existing UDE-based and conventional controllers. The comparative study shows the faster response to attain desired states along with smooth and chattering free control efforts offered by SMC-GAUDE controller. The results present viability of the proposed controller. To show the robustness of the proposed controller, IATE performance index is evaluated. Also, the Monte Carlo simulations are done to highlight the efficacy of the proposed controller in the presence of parametric variations in MAV aerodynamic coefficients and velocity.

Semi-Direct Point-Line Visual Inertial Odometry for MAVs

Traditional Micro-Aerial Vehicles (MAVs) are usually equipped with a low-cost Inertial Measurement Unit (IMU) and monocular cameras, how to achieve high precision and high reliability navigation under the framework of low computational complexity is the main problem for MAVs. To this end, a novel semi-direct point-line visual inertial odometry (SDPL-VIO) has been proposed for MAVs. In the front-end, point and line features are introduced to enhance image constraints and increase environmental adaptability. At the same time, the semi-direct method combined with IMU pre-integration is used to complete motion estimation. This hybrid strategy combines the accuracy and loop closure detection performance of the feature-based method with the rapidity of the direct method, and tracks keyframes and non-keyframes, respectively. In the back-end, the sliding window mechanism is adopted to limit the computation, while the improved marginalization method is used to decompose the high-dimensional matrix corresponding to the cost function to reduce the computational complexity in the optimization process. The comparison results in the EuRoC datasets demonstrate that SDPL-VIO performs better than the other state-of-the-art visual inertial odometry (VIO) methods, especially in terms of accuracy and real-time performance.

Design of Non-Conventional Flight Control Systems for Bioinspired Micro Air Vehicles

This research focuses on the development of two bioinspired micro air vehicle (MAV) prototypes, based on morphing wings and wing grid wingtip devices. The morphing wings MAV tries to adapt the aerodynamics of the vehicle to each phase of flight by modifying the vehicle geometry, while the wing grid MAV aims to minimize the aerodynamic and weight penalty of these vehicles. This work focuses on the design methodology of the flight control system of these MAVs. A preliminary theoretical conceptual design was used to verify the requirements, wind tunnel tests were performed to determine aerodynamic characteristics, and suitable materials were selected. The hardware and software configuration designed for the control system, which fulfills the objective of adaptive and optimal control in the wingtip-based prototype of the wing grid, is described. Finally, the results of the flight control on the prototype MAVs are analyzed.

Aerodynamic Efficiency Enhancement of Delta Wings for Micro Air Vehicles Using Shape-Optimization

The performance of fixed-wing micro air vehicles (MAVs) is impaired due to their small size, operating speeds, and altitude leading to limited lift, aerodynamic efficiency (lift-to-drag ratio), and low gust resilience. Nonslender delta wings have high stall angle and maneuverability, which are desirable features for MAVs. However, the aerodynamic efficiency of delta-winged MAVs is low, thus impacting the overall performance. We optimize the aerodynamic efficiency of a flat-plate delta wing using the adjoint-based aerodynamic shape optimization method coupled with incompressible Reynolds-averaged Navier–Stokes equations. The optimization improves the aerodynamic efficiency from 6.2 to 10.2 at 5° angle of attack while satisfying lift and moment constraints. The optimized wing is significantly cambered, and the axial cross section reveals that a circular arc can approximate the optimized wing airfoil. A parametric study of the circular camber delta wings with a 0–7.5% camber-to-chord ratio reveals that an increase in camber leads to linear growth in the lift, whereas the drag shows a quadratic growth. Consequently, an optimum camber exists at 2.5% for maximizing the aerodynamic efficiency (9.3); the wing has aerodynamic characteristics closely resembling the optimized wing, thus revealing that the essence of the efficiency optimization process is to identify the optimal camber distribution.

Autonomous Flight Test of a Novel Nonconventional Biplane Micro Air Vehicle

This paper presents detailed mathematical modeling, controller design, and flight test results for the autonomous mission of a nonconventional fixed-wing biplane micro air vehicle (MAV) called Skylark having span and chord length within 150 mm. Although numerous fixed-wing MAV designs are reported in the open literature, an MAV’s reliable autonomous flight is still a challenge. The primary difficulties in performing the autonomous mission of fixed-wing MAV are system integration within the weight and power budget, center of gravity (CG) management, and flight controller design for fast dynamics. In this paper, the key challenges are addressed by using the higher payload capacity of Skylark, suitable selection and design of avionics components, and design of controller after detailed development of the mathematical model, including the additional effect of propeller wash and motor countertorque. An autonomous flight test is successfully demonstrated after the validation of algorithms and software architecture through nonlinear six-degrees-of-freedom simulation. The detailed design and development approach will increase the MAV’s performance and reliability in civil and military applications.