Micro Air Vehicles Research Articles

Takeoff of a 2.1 g Fully Untethered Tailless Flapping-Wing Micro Aerial Vehicle With Integrated Battery

Insect-scale micro-aerial vehicles (MAV) are becoming increasingly important for sensing and mapping spatially constrained environments. However, achieving untethered flight powered by a small and lightweight on-board energy source remains a challenge. In this study, we successfully demonstrated the untethered takeoff of a flapping-wing MAV powered by a commercially available LiPo battery for a short duration without stability control. By incorporating a high-efficiency direct-drive piezoelectric actuator, and an optimized control circuit for high-impedance-width modulation and charge recovery, this electronics configuration realizes over 5 min of practical operation with a thrust of 1.5 times its own weight, while offering wireless communication capability and sensors for attitude estimation. Our MAV has a total mass of 2.1 g, which is an eight-fold reduction compared to the lightest battery-powered tailless flapping-wing MAV currently available, making this the first report of a battery-powered tailless flapping-wing MAV with insect-scale weight.

Adaptive PID Control via Sliding Mode for Position Tracking of Quadrotor MAV: Simulation and Real-Time Experiment Evaluation

The lightweight nature of micro air vehicles (MAVs) makes them highly sensitive to perturbations, thus emphasizing the need for effective control strategies that can sustain attitude stability throughout translational movement. This study evaluates the performance of two controllers (Proportional-Integral-Derivative (PID) and Adaptive PID based on Sliding Mode Control (SMC)) on a MAV that is subjected to external disturbances. These controllers are initially simulated using MATLAB®/Simulink™ and then implemented in real-time on the Parrot Mambo Minidrone. The observation on the waypoint follower and the orbit follower in both simulation and experiment showed that the Adaptive PID (APID) controller is more effective and robust than the PID controller against external disturbances such as wind gusts. The study provides evidence of the potential of the APID control scheme in enhancing the resilience and stability of MAVs, making them suitable for various applications including surveillance, search and rescue, and environmental monitoring.

Robust Attitude and Heading Estimation under Dynamic Motion and Magnetic Disturbance.

Robust and accurate attitude and heading estimation using Micro-Electromechanical System (MEMS) Inertial Measurement Units (IMU) is the most crucial technique that determines the accuracy of various downstream applications, especially pedestrian dead reckoning (PDR), human motion tracking, and Micro Aerial Vehicles (MAVs). However, the accuracy of the Attitude and Heading Reference System (AHRS) is often compromised by the noisy nature of low-cost MEMS-IMUs, dynamic motion-induced large external acceleration, and ubiquitous magnetic disturbance. To address these challenges, we propose a novel data-driven IMU calibration model that employs Temporal Convolutional Networks (TCNs) to model random errors and disturbance terms, providing denoised sensor data. For sensor fusion, we use an open-loop and decoupled version of the Extended Complementary Filter (ECF) to provide accurate and robust attitude estimation. Our proposed method is systematically evaluated using three public datasets, TUM VI, EuRoC MAV, and OxIOD, with different IMU devices, hardware platforms, motion modes, and environmental conditions; and it outperforms the advanced baseline data-driven methods and complementary filter on two metrics, namely absolute attitude error and absolute yaw error, by more than 23.4% and 23.9%. The generalization experiment results demonstrate the robustness of our model on different devices and using patterns.

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

AbstractIn recent years, unmanned aerial vehicles (UAVs) have been confirmed as a powerful tool for countless applications in nearly every industry, in which collision avoidance plays a vital role for micro aerial vehicles (MAVs) performing autonomous missions. The complexity of collision avoidance systems is directly related to the complexity of the navigation environment, the avoidance of highly uncertain obstacles being a more thoroughly researched critical aspect. The non‐cooperative collision avoidance strategy is necessary when communication between the agents is unavailable, under the case with many existing passive obstacles and non‐cooperative flying objects. In this article, we review the topic of non‐cooperative collision avoidance for MAVs in dynamic environments by covering two main issues: onboard sensing and obstacle description algorithms. A detailed discussion of these two topics is provided to face non‐cooperative collision avoidance technology for MAVs. Finally, the paper summarizes the open challenges and foreseen future directions in this field.

A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments.

Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of the Micro Aerial Vehicles (MAVs) for positioning is not practical. In this paper, we propose a novel cooperative relative positioning method for MAVs in GNSS-denied scenarios. Specifically, the system model framework is first constructed, and then the Extended Kalman Filter (EKF) algorithm, which is introduced for its ability to handle nonlinear systems, is employed to fuse inter-vehicle ranging and onboard IMU information, achieving joint position estimation of the MAVs. The proposed method mainly addresses the problem of error accumulation in the IMU and exhibits high accuracy and robustness. Additionally, the method is capable of achieving relative positioning without requiring an accurate reference anchor. The system observability conditions are theoretically derived, which means the system positioning accuracy can be guaranteed when the system satisfies the observability conditions. The results further demonstrate the validity of the system observability conditions and investigate the impact of varying ranging errors on the positioning accuracy and stability. The proposed method achieves a positioning accuracy of approximately 0.55 m, which is about 3.89 times higher than that of an existing positioning method.

Sectional Leading Edge Vortex Lift and Drag Coefficients of Autorotating Samaras

Autorotating samaras such as Sycamore seeds are capable of descending at exceptionally slow speeds and the secret behind this characteristic is attributed to a flow mechanism known as the leading edge vortex (LEV). A stable LEV is known to increase the maximum lift coefficient attainable at high angles of attack and recent studies of revolving and flapping wings have proposed suitable lift and drag coefficient models to characterise the aerodynamic forces of the LEV. For the samara, however, little has been explored to properly test the suitability of these low-order lift and drag coefficient models in describing the aerodynamic forces produced by the samara. Thus, in this paper, we aim to analyse the use of two proposed aerodynamic models, namely, the normal force and Polhamus models, in describing the sectional aerodynamic lift of a samara that is producing a LEV. Additionally, we aim to quantify the aerodynamic parameters that can describe the lift and drag of the samara for a range of wind speed conditions. To achieve this, the study first examined the samara flight data available in the literature, and from it, the profiles of the lift coefficient curves were investigated. Subsequently, a numerical Blade Element-Momentum model (BEM) of the autorotating samara encompassing different lift profiles was developed and validated against a comprehensive set of samara flight data, which were measured from wind tunnel experiments conducted at the University of Bristol for three different Sycamores. The results indicated that both the normal force and Polhamus lift models combined with the normal force drag can be used to describe the two-dimensional lift characteristics of a samara exhibiting an LEV. However, the normal force model appeared to be more suitable, since the Polhamus relied on many assumptions. The results also revealed that the aerodynamic force parameters can vary with windspeed and with the samara wing characteristics, as well as along the span of the samara wing. Values of the lift curve slope, zero-lift drag coefficient, and maximum lift coefficient are predicted and presented for different samaras. The study also showed that the low-order BEM model was able to generate a good agreement with the experimental measurements in the prediction of both rotational speed and thrust. Such a validated BEM model can be used for the initial design of bio-inspired rotors for micro-air vehicles.

Modular and Scalable Fabrication of Insect‐Scale Aerial Robots toward Demonstrating Swarm Flights

Insects can navigate in cluttered spaces and perform challenging functions such as pollination and collective object transport. By exploiting scaling laws and bioinspired designs, insect‐scale micro‐aerial‐vehicles (MAVs) have demonstrated impressive flight capabilities such as in‐flight collision resilience and acrobatic maneuvers. However, existing subgram MAVs are difficult to design, construct, and repair. Coupled with challenges in robot sensing and control, existing subgram MAVs have not achieved insect‐like swarm flight, which limits potential studies of swarm behaviors and future applications such as collective sensing. Herein, a new design and fabrication method is developed to substantially improve the fabrication scalability of subgram MAVs. Based on a small set of design parameters, an automated algorithm generates the laser cut files of microrobotic components. To reduce fabrication and assembly time, stereolithographic 3D printing is used for making static components such as the airframe and connectors. The modular design enables straightforward assembly and repair, which reduces the overall fabrication time by over 2 times. Owing to the ease of fabrication and good reliability, two subgram MAVs demonstrate controlled hovering flight and coordinated lifting of an object. This result lays the foundation for future robotic studies of collective insect flight.

An Aerial Robotics Investigation into the Stability, Coordination, and Movement of Strategies for Directing Swarm and Formation of Autonomous MAVs and Diverse Groups of Driverless Vehicles (UGVs)

This study will discuss the matter of movement communication and preparation of tight configurations of land & flying robots. Remotely Operated Cars (UGVs) and Unmanned Aerial Vehicles (UAVs), in specific Micro Aerial Vehicles (MAVs), would be used to fix circumstances where a creation of UGVs and UAVs, in specific Micro Aerial Vehicles (MAVs), should counteract their velocity and direction to finish a mission of traffic sequence to a targeted area. The motion planning and stabilisation strategy given here is a useful tool for deploying closely collaborating robot teams including both outdoor and indoor settings. The installation of large groups of Micro Aerial Vehicles (MAVs) in a legitimate (indoor and outdoor) environment without the use of auxiliary positioning applications (such as Vicon or GPS) is indeed a natural development in the area of autonomously flying systems. Stability, control, and trajectory planning techniques for guiding swarm or configurations of unmanned MAVs, as well as diverse groups with Unmanned Ground Vehicles (UGVs) operating alongside MAVs, will be discussed in greater detail. These approaches discussed all are designed for the use of inter squads in true complex scenarios even without necessity for worldwide translation or motion capture systems, as they are predicated on board optical comparative localisation of single MAVs. This multi - objective optimisation being an enabler for the introduction of swarming of tiny autonomous drones beyond the labs with equipment for precise robot positioning. Model Predictive Control (MPC) is being used to address a formations to goal territory issue, and the form drive idea is based on a simulated approach. The Particle swarm optimization approach is utilised for digital leader trajectories planning, as well as control and stabilisation of follows (MAVs and UGVs). The proposed technique could be tested in the future using a range of simulation and practical tests.

Detection, Localization, and Tracking of Multiple MAVs With Panoramic Stereo Camera Networks

Malicious use of micro aerial vehicles (MAVs) has become a serious threat to public safety and personal privacy in recent years. Motivated by this problem, we propose a systematic approach to monitor the intrusion of malicious MAVs based on a novel type of panoramic stereo camera networks. Each sensing node of such a network consists of 16 lenses that can form a 360-degree panoramic vision system. The 16 lenses further form 8 pairs of stereo cameras that can directly localize aerial targets. The effective range for a sensing node localizing a MAV like DJI M300 could reach 80 meters, which is much farther than existing commercial stereo cameras. In terms of algorithms, we propose i) a novel visual MAV detection algorithm based primarily on motion features of MAVs, ii) an efficient stereo localization algorithm based on sparse feature points, and iii) robust multi-target tracking and trajectory fusion algorithm to fuse the observations of different sensing nodes. The effectiveness, robustness, and accuracy of the proposed algorithms together with the overall system have been verified by extensive experimental tests. To the best of our knowledge, this is the first systematic approach to detect, localize, and track unknown MAVs in the literature. Our approach provides a scalable solution to securely cover large areas of interest against malicious MAV intrusion. Note to Practitioners—Micro aerial vehicles (MAVs) have been widely used in many domains nowadays. However, they have also brought many safety problems. To monitor the intrusion of malicious MAVs, this paper proposes a novel type of panoramic stereo camera networks that can detect, localize, and track multiple MAVs simultaneously. Such a network consists of a number of sensing nodes and a central node. Each sensing node is able to detect, localize, and track multiple MAV targets. The role of the central node is to fuse the observations from multiple sensing nodes to generate more accurate trajectories of the MAV targets and in the meantime secure a large area in a coordinated way. This paper presents the details of the prototype of the system and the key algorithms therein.

Three-Dimensional Bearing-Only Target Following via Observability-Enhanced Helical Guidance

This paper studies the problem of air-to-air target following of micro aerial vehicles (MAVs) motivated by the application of defense against malicious MAVs. When the bearing of the target MAV has been measured by the onboard visual sensor of the pursuer MAV, the problem becomes three-dimensional (3-D) bearing-only target following, which has been rarely studied in the literature and faces some unique challenges. To solve this problem, we propose the following novel results. First, to estimate the motion of the target MAV from 3-D bearing measurements, we propose a new pseudo-linear Kalman filter, which has a concise expression and superior stability compared to the classic ones such as the extended Kalman filter and modified polar coordinate filter. Second, we propose a novel approach to analyze the observability of state estimation when only bearing information is available. While the existing approaches are applicable to 2-D and single-step time-horizon cases, ours can handle more general 3-D and multiple-step time-horizon cases. Third, based on the theoretical conclusion of our observability analysis, we design a new 3-D helical guidance law that can better exploit the additional degree of freedom in 3D. The guidance law is adapted to the quadcopter's dynamics and a low-level flight controller is designed based on geometric control. Numerical simulation results verify the superior performance of the proposed algorithms compared to the state-of-the-art ones. Flight experiments on real quadrotor platforms further show the effectiveness and robustness of the proposed algorithms in practice.

Editorial for the Special Issue on Micro Air Vehicles

Recently, Micro Air Vehicles (MAVs) have been receiving a significant amount of attention from research organizations and businesses worldwide due to their unique characteristics, including high mobility, three-dimensional (3D) movement, and ease of deployment [...]

Multi-Stage NMPC for a MAV based Collision Free Navigation Under Varying Communication Delays

Time delays in communication networks are one of the main concerns in deploying robots with computation boards on the edge. This article proposes a multi-stage Nonlinear Model Predictive Control (NMPC) that is capable of handling varying network-induced time delays for establishing a control framework being able to guarantee collision-free Micro Aerial Vehicles (MAVs) navigation. This study introduces a novel approach that considers different sampling times by a tree of discretization scenarios contrary to the existing typical multi-stage NMPC where system uncertainties are modeled by a tree of scenarios. Additionally, the proposed method considers adaptive weights for the multi-stage NMPC scenarios based on the probability of time delays in the communication link. As a result of the multi-stage NMPC, the obtained optimal control action is valid for multiple sampling times. Finally, the overall effectiveness of the proposed novel control framework is demonstrated in various tests and different simulation environments.

Computationally Efficient Stability-Based Nonlinear Model Predictive Control Design for Quadrotor Aerial Vehicles

This article presents the design of a stability-guaranteed nonlinear model predictive controller for quadrotor-type microaerial vehicles to operate robustly on fast trajectories. The basic controller structure operates without having to use terminal conditions in the optimization problem. As a result, the controller is computationally less demanding and provides more stable closed-loop performance than traditional nonlinear predictive control schemes. This article presents a detailed stability analysis without terminal costs or terminal constraints and proves the asymptotic stability and necessary conditions for the recursive feasibility of the system. This article derives the growth-bound sequence that enables obtaining the shortest possible prediction horizon for stability. The proposed analysis provides the necessary conditions to implement the controller while using the shortest stabilizing prediction horizon compared to major traditional predictive control schemes reported in the literature. This particular feature enables the proposed controller to perform fast optimization and, hence, the capability to implement fast trajectories using feedback regularization. In order to demonstrate the validity of this new proposed control scheme, first, several MATLAB simulations are conducted to demonstrate the improved performance of the controller especially when the quadrotor vehicle follows fast trajectories. Real-time lab experiments are also conducted to validate the performance of the proposed scheme for point stabilization (hovering) and trajectory tracking problems. The results show that the proposed scheme can stabilize the system with a relatively short prediction horizon, a fast convergence rate, and a small tracking error.

Numerical study on the aerodynamic performance of the three-dimensional wing of a jellyfish-like flyer

The jellyfish-like flying machine is a new development direction of the future bionic flapping-wing aircraft besides the insect-mimic and bird-mimic micro air vehicles (MAVs). To better understand the underlying fluid mechanisms of the jellyfish-like flyer, we numerically simulated the aerodynamic forces of the three-dimensional flapping wings under different control parameters. The effects of flapping amplitude, vortex wake, up-flight speed, and wing–wing interaction on aerodynamic performance were investigated. The results show that, at hovering, the mean lift rises rapidly at first and then tends to be stable with the increase in flapping amplitude. The vortex wake can improve the lift at large flapping amplitudes, while it reduces the lift at very small flapping amplitudes. With the increase in up-flight speed, the lift decreases. However, the sources of lift reduction are different for different flapping amplitudes. When the two wings flap together and the distance between the wings is small enough, the wing–wing interaction can improve the lift by about 15% compared with that of a single wing, but much higher power is required, resulting in lower efficiency. The results of this study provide new insights into the flight mechanism of the jellyfish-like aircraft and have important guiding significance for the design and optimization of the jellyfish-like flying machine.

Air-to-air Micro Air Vehicle interceptor with an embedded mechanism and deep learning

Illegal flight of small and portable aerial vehicles, or the so-called Micro Air Vehicles (MAVs), has become a serious safety issue in most cities around the world. Most existing intercepting systems hardly detect them due to their size, or these systems are too expensive to install everywhere. This paper presents a novel solution to tackle this problem using another aerial vehicle that is capable of flying in close proximity to intruding MAVs, detecting, and intercepting them. This vehicle is embedded with an intercepting mechanism and a vision-based detector for autonomously tracking and intercepting the target. The tilted arm design installed with inverted motors and nets maximizes the chance of intercepting or even capturing intruding MAVs. A deep learning-based detector integrated with a Kalman filter tracker is proposed to enable real-time MAV detection and tracking using an onboard processing unit and a single camera. In this study, 9332 images of flying MAVs were used for training the pre-trained model. YOLOv4-tiny showed the best balance of accuracy and inference speed among the models chosen for the analysis, which obtained an average precision of 90.04% and 14 FPS in the evaluation. In addition, a control strategy that adapts the detected MAV size or its relative distance to the MAV interceptor in the velocity controller is developed to approach the intruding MAV steadily. The performance of the proposed system for aerial target tracking was validated in several flight tests. The results show that our vehicle can successfully detect, track, and intercept the moving target.

Performance enhancement of a bioinspired micro air vehicle by integrating a smart composite in its morphing wing

The purpose of this paper is to show the advantages of using a smart composite in a micro air vehicle (MAV) equipped with morphing wing technology. A Macro Fiber Composite (MFC) actuator is attached to the wing’s bottom surface to modify the wing camber during the mission. This material allows the MAV to be optimized according to each flight, thus making it more versatile and attractive to the market. The elongation of the lower surface when a positive voltage is applied to the actuator is translated to an increment in camber, which results in an increment in the maximum lift coefficient, thus enabling the vehicle to fly slower to adapt to any payload. Besides, a reduction in camber results in an increase in aerodynamic efficiency, which improves range and endurance. Several tests of the MAV at prototype level have been carried out at INTA, so as to demonstrate the feasibility of implementing MFC actuators to control and manoeuvre these vehicles. The use of this material in aerospace industry opens up various fields of research in aerospace engineering, such as new features in flight mechanics and aerodynamic performance and new strategies in the design of flight stability and control laws.

RAST: Risk-Aware Spatio-Temporal Safety Corridors for MAV Navigation in Dynamic Uncertain Environments

Autonomous navigation of Micro Aerial Vehicles (MAVs) in dynamic and unknown environments is a complex and challenging task. Current works rely on assumptions to solve the problem. The MAV's pose is precisely known, the dynamic obstacles can be explicitly segmented from static ones, their number is known and fixed, or they can be modeled with given shapes. In this letter, we present a method for MAV navigation in dynamic uncertain environments without making any of these assumptions. The method employs a particle-based dynamic map to represent the local environment and predicts it to the near future. Collision risk is defined based on the predicted maps and a series of risk-aware spatio-temporal (RAST) safety corridors are constructed, which are finally used to optimize a dynamically-feasible collision-free trajectory for the MAV. We compared our method with several state-of-the-art works in 12000 simulation tests in Gazebo with the physical engine enabled. The results show that our method has the highest success rate at different uncertainty levels. Finally, we validated the proposed method in real experiments.

Mode transition in fluid–structure interaction of piezoelectric membrane wings

Flow-induced vibrations can be utilized to harvest energy for micro-air vehicles (MAVs). A flexible membrane wing with an embedded piezoelectric energy harvester at an angle of attack of 12° and the Reynolds number (Re) of 8000 is studied by numerical simulations. An aero-electro-mechanical model is established to investigate the effect of the leading-edge (LE) and trailing-edge (TE) geometries on the fluid–structure interaction (FSI) modes, aerodynamic performance, and energy harvesting performance. A new correction method of structural frequency is proposed that it considers both the aerodynamic stiffness effect and the added mass effect corresponding to a specific FSI mode of interest. The results suggest that the mode transition accompanied by the performance changes is essentially caused by the FSI state transition, which is distinguished by the corrected structural frequency and the vortex shedding one. With the Fourier mode decomposition (FMD) method, the modes of membrane vibration and pressure fluctuation become clear. The LE geometry is found to affect the FSI state by influencing the leading-edge vortices, which further triggers the mode transition.

FIImap: Fast Incremental Inflate Mapping for Autonomous MAV Navigation

Three-dimensional mapping is an essential component of autonomous Micro Aerial Vehicle (MAV) navigation. The paper focuses on the 3D spatial representation method of MAV to overcome the collision problem caused by soft constraints, control error, and planning with the center of mass by inflating the occupancy grid map. A fast incremental inflated map construction method is proposed, which reduces the time-consumption caused by the increase of map range and inflated size. The method focuses on areas of the map that occupied state changes and introduces two arrays that record newly appearing and disappearing obstacles. Then, a series of breadth-first search algorithms are used to traverse the parts of the inflated map that need local modification to update the inflated map. Moreover, a sliding map model is designed based on the MAV position, which is suitable for large-range autonomous flight. The effectiveness of the proposed approach is verified with simulated and actual flight data. The proposed method takes about 3 ms to construct the inflated map with a local update range of 16 m × 16 m × 6 m.

Hardware in the loop simulation and implementation of a dragonfly-like MAV using clap and fling mechanism

The main aim of this paper is to model and simulate flight dynamics and to design a controller for a dragonfly-like micro aerial vehicle (MAV). This MAV has flapping wings using a clap and fling mechanism with a rigid abdomen. Kane’s method is applied to derive the longitudinal motion equations of the MAV as a multi-body system. Then, the aerodynamic lift and drag forces of the MAV are obtained. These equations are substituted in the derived motion equations. Furthermore, a new structure for a dragonfly-like flapping wing MAV is presented in which abdomen motion is considered in the longitudinal mode. In this work, the use of the abdomen is also formulated. A change in the motor’s speed generates differential thrust that creates a pitch moment as a control input. State space linearized equations of motion are presented by using appropriate assumptions in the aerodynamics, and dynamic nonlinear equations. To validate the linearized model, the response of the open-loop linear system is compared with the nonlinear one. In addition, the positive role of the abdomen in the open loop is discussed and analyzed. Finally, an LQR controller is designed for the pitch mode which is robust against disturbances. The validity, effectiveness, and performance of the derived equations and designed autopilot are shown by implemented hardware in the loop testbed using a fixed abdomen. Results demonstrate a good agreement between theoretical and experimental responses with accurate hovering at the desired angle.