Search

Fixed-wing MAV with morphing wing configuration is seen as the future design requirements for more aerodynamic efficiency. Wash in twist morphing MAV wings have shown a promising ability by producing smoother lift behavior and exhibited a significant increase in lift performances. However, the wash in twist morphing wings also suffer from massive drag penalties as compared to rigid or membrane (baseline) wings. Thus, the objective of this paper is to explore the aerodynamic performances of another morphing configuration known as wash out twist morphing MAV wing with a view towards the improvement of the drag performances of morphing MAV wing. Technically, the wash out twist morphing (TM) wing has deformation characteristics which are opposite from those of the wash in morphing wing. The force execution of the wash out morphing wing is similar to the wash in wing but with reversed vector direction. The investigation was carried out based on Fluid-Structure-Interaction (FSI) simulation method. The simulation was conducted in a 3D, quasi-static linear structural model combined with a steady-state, incompressible, and turbulent flow model. Three levels of the wash out morphing force (5N, 3N, and 1N) were used here to evaluate the morphing performances together with the baseline wing models (membrane wing and rigid wing). The lift coefficient results show that the wash out TM wings produced inferior lift performances compared to the baseline wings. This is due to weak vortices interactions, which lead to substantial adverse pressure on the TM wings. Meanwhile, TM wings have slight advantages in producing better drag performances than the rigid and membrane wings. Vortices study discovered that the TM wings have a weak tip vortex formation, which subsequently induces lower drag magnitude. However, the drag advantages found on the TM wings are still unable to overcome the lift drawbacks, which in turn, reducing the overall aerodynamics efficiency performances.

Despite mankind’s remarkable advancements in aerial technology, flying birds and insects still outperform the agility, maneuverability, and stability of man-made aircrafts. The purpose of FWMAV is to investigate the physical phenomena behind unsteady aerodynamics and nonlinear flight to achieve its aerial capabilities. Three main components are evaluated separately: (1) the non-linear flight mechanism, (2) the flow field, and (3) the force generated. The ultimate goal is to combine the accumulated data, extrapolate quantifiable results, and illustrate the high performance of FWMAVs. The team tackles problems to determine solutions that lie at the forefront of evolving flapping wing mechanics . Engineering a drone that flies utilizing four sets of flapping wings (known as a ‘quad-flapper’) stems from theories such as Kapitza’s pendulum, which demonstrates vibrational stabilization. Acquiring greater control over all aspects of the design provided opportunities for extensive research and development to supplement the solidification of the Beta Drone’s flight potential. From the motor, to the crank-and-flapper system, the most efficient combinations were examined from thrust and lift data gathered from the load cells. Alongside these drones, the team strives to find different methods of replicating naturalistic flapping motion observed in nature.

Fixed-wing MAV with morphing wing configuration is seen as the future design requirements for more aerodynamic efficiency. Wash in twist morphing MAV wings have shown a promising ability by producing smoother lift behavior and exhibited a significant increase in lift performances. However, the wash in twist morphing wings also suffer from massive drag penalties as compared to rigid or membrane (baseline) wings. Thus, the objective of this paper is to explore the aerodynamic performances of another morphing configuration known as wash out twist morphing MAV wing with a view towards the improvement of the drag performances of morphing MAV wing. Technically, the wash out twist morphing (TM) wing has deformation characteristics which are opposite from those of the wash in morphing wing. The force execution of the wash out morphing wing is similar to the wash in wing but with reversed vector direction. The investigation was carried out based on Fluid-Structure-Interaction (FSI) simulation method. The simulation was conducted in a 3D, quasi-static linear structural model combined with a steady-state, incompressible, and turbulent flow model. Three levels of the wash out morphing force (5N, 3N, and 1N) were used here to evaluate the morphing performances together with the baseline wing models (membrane wing and rigid wing). The lift coefficient results show that the wash out TM wings produced inferior lift performances compared to the baseline wings. This is due to weak vortices interactions, which lead to substantial adverse pressure on the TM wings. Meanwhile, TM wings have slight advantages in producing better drag performances than the rigid and membrane wings. Vortices study discovered that the TM wings have a weak tip vortex formation, which subsequently induces lower drag magnitude. However, the drag advantages found on the TM wings are still unable to overcome the lift drawbacks, which in turn, reducing the overall aerodynamics efficiency performances.

Semantic segmentation methods have been developed and applied to single images for object segmentation. However, for robotic applications such as high-speed agile Micro Air Vehicles (MAVs) in Autonomous Drone Racing (ADR), it is more interesting to consider temporal information as video sequences are correlated over time. In this work, we evaluate the performance of state-of-the-art methods such as Recurrent Neural Networks (RNNs), 3D Convolutional Neural Networks (CNNs), and optical flow for video semantic segmentation in terms of accuracy and inference speed on three datasets with different camera motion configurations. The results show that using an RNN with convolutional operators outperforms all methods and achieves a performance boost of 10.8% on the KITTI (MOTS) dataset with 3 degrees of freedom (DoF) motion and a small 0.6% improvement on the CyberZoo dataset with 6 DoF motion over the single-frame-based semantic segmentation method. The inference speed was measured on the CyberZoo dataset, achieving 321 fps on an NVIDIA GeForce RTX 2060 GPU and 30 fps on an NVIDIA Jetson TX2 mobile computer., Aerospace Engineering | Control & Simulation

Highly automated Unmanned Aerial Vehicles (UAVs) or "flying robots" are rapidly becoming an important asset to society. The last decade has seen the advent of an impressive number of new UAV types and applications. For many applications, the UAVs need to be safe, highly automated, and versatile. Safety is a prerequisite to allowing their use in society. While flight safety comprises many aspects, one important safety factor is the total system mass. The common thread through this research is therefore to minimize the system mass while maintaining mission capabilities to increase safety. Flight automation is required to reach many applications' full potential by addressing operational labor costs and scalability. But despite great advances in ground-based robotics, the weight and power constraints of flying robots still constitute important challenges. Last but not least, many applications also require versatile aircraft that combine the ability to hover and fly fast efficiently. Hover is required for precision take-off & landing in confined areas at a growing number of locations and for the close-up inspection of assets. Fast and efficient flight is needed to reach distant locations, perform large surveys, cope with high headwind conditions, or simply reach destinations quickly. Unfortunately, the requirements for hover and fast flight are conflicting, and this drives the search for solutions to ``combine hover with fast flight in mission-capable flying robots while cost-effectively minimizing their size and maximizing their safety.'' To investigate the minimal feasible mass of mission-capable robots, in this thesis, a novel 20 g tailed flapping-wing robot called DelFly Explorer is presented that can autonomously explore unknown unprepared rooms. It was equipped with a 4 g micro stereo-vision system which necessitated algorithms that were optimized for tiny microcontrollers with low memory. Combined with a navigation strategy that keeps the area in front of the, Control & Simulation

Wireless ranging measurements have been proposed for enabling multiple Micro Air Vehicles (MAVs) to localize with respect to each other. However, the high-dimensional relative states are weakly observable due to the scalar distance measurement. Hence, the MAVs have degraded relative localization and control performance under unobservable conditions as can be deduced by the Lie derivatives. This paper presents a nonlinear model predictive control (NMPC) by maximizing the determinant of the observability matrix to generate optimal control inputs, which also satisfy constraints including multi-robot tasks, input limitation, and state bounds. Simulation results validate the localization and control efficacy of the proposed MPC method for range-based multi-MAV systems with weak observability, which has faster convergence time and more accurate localization compared to previously proposed random motions. A real-world experiment on two Crazyflies indicates the optimal states and control behaviours generated by the proposed NMPC., Control & Simulation

Semantic segmentation methods have been developed and applied to single images for object segmentation. However, for robotic applications such as high-speed agile Micro Air Vehicles (MAVs) in Autonomous Drone Racing (ADR), it is more interesting to consider temporal information as video sequences are correlated over time. In this work, we evaluate the performance of state-of-the-art methods such as Recurrent Neural Networks (RNNs), 3D Convolutional Neural Networks (CNNs), and optical flow for video semantic segmentation in terms of accuracy and inference speed on three datasets with different camera motion configurations. The results show that using an RNN with convolutional operators outperforms all methods and achieves a performance boost of 10.8% on the KITTI (MOTS) dataset with 3 degrees of freedom (DoF) motion and a small 0.6% improvement on the CyberZoo dataset with 6 DoF motion over the single-frame-based semantic segmentation method. The inference speed was measured on the CyberZoo dataset, achieving 321 fps on an NVIDIA GeForce RTX 2060 GPU and 30 fps on an NVIDIA Jetson TX2 mobile computer., Aerospace Engineering | Control & Simulation

Wireless ranging measurements have been proposed for enabling multiple Micro Air Vehicles (MAVs) to localize with respect to each other. However, the high-dimensional relative states are weakly observable due to the scalar distance measurement. Hence, the MAVs have degraded relative localization and control performance under unobservable conditions as can be deduced by the Lie derivatives. This paper presents a nonlinear model predictive control (NMPC) by maximizing the determinant of the observability matrix to generate optimal control inputs, which also satisfy constraints including multi-robot tasks, input limitation, and state bounds. Simulation results validate the localization and control efficacy of the proposed MPC method for range-based multi-MAV systems with weak observability, which has faster convergence time and more accurate localization compared to previously proposed random motions. A real-world experiment on two Crazyflies indicates the optimal states and control behaviours generated by the proposed NMPC., Control & Simulation

Micro robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational power to perform the required control tasks. Thus, spiking neural networks (SNNs) are a promising research direction. By mimicking the biological process for transferring information between neurons using spikes or impulses, they allow for low power consumption and asynchronous event-driven processing. In this work, we propose an evolved altitude controller based on a SNN for an airship which relies solely on the sensory feedback provided by an airborne radar sensor. Starting from the design of a a lightweight, low-cost, open-source airship, we also present a low-control-effort SNN architecture, an evolutionary framework for training the network in a simulated environment, and a control scheme for ameliorating the performance of the system in real-world scenarios. The system's performance is evaluated through real-world experiments, demonstrating the advantages of our approach by comparing it with an artificial neural network (ANN) and a linear controller (PID). The results show an accurate tracking of the altitude command while ensuring efficient management of the control effort. The main contributions of this work are presented in the scientific paper, corresponding to Part I of the document. Besides the research on altitude control based on SNNs and their comparison with an ANN and a PID, this thesis includes an in-depth review of the relevant literate on the main topics covered, in Part II. Finally, a detailed explanation of the methodologies used, the conclusions and recommendations for future work are proposed in Part III., Aerospace Engineering | Control & Simulation

Drones need to be able to detect and localize each other if they are to collaborate in multi-robot teams or swarms. Typically, computer vision methods based on visual appearance are investigated to this end. In contrast, in this work, a method based on dense optical flow (OF) is developed that detects dynamic objects. This is achieved by comparing the flow vectors of dense OF with the direction to the Focus of Expansion (FoE) in the image plane. A simulation in AirSim is developed to validate this approach and to create datasets for motion-based object detection of MAVs. This simulation includes ground-truth FoE, depth, OF and IMU data. The results show that this method performs well if the OF vector's magnitude is large enough and its angle is sufficiently different from those of static world points. We expect that the presented method will serve as a useful baseline for deep learning methods that use dense optical flow as input., Source code: https://github.com/evroon/mav-detection, Aerospace Engineering

type:Regular Paper, This paper shows some guidelines to improve the estimation accuracy of a navigation system, which is based on Wide-Field-Integration (WFI) of optic flow. Optic flow is a vector field of relative velocities obtained by photoreceptors in image sensors, and WFI of optic flow enables motion estimation robust by integrating wide range of optic flow. Since the system has several attractive features (small size, light weight, and low computation), it is applicable to an autonomous control system of micro air vehicles. However, due to some restrictions of real systems, WFI of optic flow theory should be applied while considering sensor arrangement for better estimation. In this paper, first an adequate number and adequate optical-axes of image sensors are investigated. Then, a new estimation system combining a gyro sensor with WFI of optic flow is discussed by numerical simulations. Finally, the essential effect for the number of cameras are verified in experiments.

We have developed a tailless, two-winged flapping drone with a full span length of 180 mm and a total weight of 20.5 g. The developed flapping drone is characterized by three biomimetic techniques: an anisotropic vein pattern reinforcing the wing surfaces, an elastic flapping mechanism, and gravity center position control in the abdomen. On the basis of experimental and numerical results, the flapping wings are reinforced by a vein pattern made of an anisotropic carbon fiber-reinforced plastic (CFRP) laminate to passively provide appropriate aeroelastic deformation and positively utilize snap-though buckling on the wing surface at stroke reversals to provide a fast feathering rotation. The flapping wing kinematics are provided by a novel flapping mechanism with an energy recovery system using the elasticity of the mechanical system. Unlike other previously developed flapping robots, feedback control to stabilize the pitch and roll angles of the drone’s body is conducted using a technique of gravity center position control, where the tail angles of the body are changed similarly to the abdominal movements of insects in flight. The developed flapping drone has succeeded in an autonomous hovering flight for more than 30 s and a vertical take-off under a wireless condition with the gravity center position control., Sensors and Materials, 33(3), pp. 859-872; 2021

Drones need to be able to detect and localize each other if they are to collaborate in multi-robot teams or swarms. Typically, computer vision methods based on visual appearance are investigated to this end. In contrast, in this work, a method based on dense optical flow (OF) is developed that detects dynamic objects. This is achieved by comparing the flow vectors of dense OF with the direction to the Focus of Expansion (FoE) in the image plane. A simulation in AirSim is developed to validate this approach and to create datasets for motion-based object detection of MAVs. This simulation includes ground-truth FoE, depth, OF and IMU data. The results show that this method performs well if the OF vector's magnitude is large enough and its angle is sufficiently different from those of static world points. We expect that the presented method will serve as a useful baseline for deep learning methods that use dense optical flow as input., Source code: https://github.com/evroon/mav-detection, Aerospace Engineering

Micro robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational power to perform the required control tasks. Thus, spiking neural networks (SNNs) are a promising research direction. By mimicking the biological process for transferring information between neurons using spikes or impulses, they allow for low power consumption and asynchronous event-driven processing. In this work, we propose an evolved altitude controller based on a SNN for an airship which relies solely on the sensory feedback provided by an airborne radar sensor. Starting from the design of a a lightweight, low-cost, open-source airship, we also present a low-control-effort SNN architecture, an evolutionary framework for training the network in a simulated environment, and a control scheme for ameliorating the performance of the system in real-world scenarios. The system's performance is evaluated through real-world experiments, demonstrating the advantages of our approach by comparing it with an artificial neural network (ANN) and a linear controller (PID). The results show an accurate tracking of the altitude command while ensuring efficient management of the control effort. The main contributions of this work are presented in the scientific paper, corresponding to Part I of the document. Besides the research on altitude control based on SNNs and their comparison with an ANN and a PID, this thesis includes an in-depth review of the relevant literate on the main topics covered, in Part II. Finally, a detailed explanation of the methodologies used, the conclusions and recommendations for future work are proposed in Part III., Aerospace Engineering | Control & Simulation

type:Journal Article, In this study, a FSI design of an insect–like micro flapping wing is proposed. Similar to actual insects, the proposed design actively uses the FSI to create the passive wing motions. Each design solution has a 2.5–D structure for the MEMS technology. The 3–D unsteady monolithic FSI equation system is solved to find the satisfactory design solutions using a projection method in a parallel computation environment. An area of satisfactory design solutions in a design parameter space or Design Window (DW) is presented. Each design solution in the present DW can generate the thrust sufficient to support the weight of the model insect. Therefore, the insect–like MEMS–based MAVs are possible., VII International Conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED PROBLEMS 2017), June 12 – 14, 2017, Rhodes Islad, Greece

With a growing number of drones, the risk of collision with other air traffic or fixed obstacles increases. New safety measures are required to keep the operation of Unmanned Aerial Vehicles (UAVs) safe. One of these measures is the use of a Collision Avoidance System (CAS), a system that helps the drone autonomously detect and avoid obstacles. The design of a Collision Avoidance System is a complex task with many smaller subproblems, as illustrated by Albaker and Rahim [1]. How should the drone sense nearby obstacles? When is there a risk of collision? What should the drone do when a conflict is detected? All of these questions need to be answered to develop a functional Collision Avoidance System. However, all of these subproblems – except the sensing of obstacles – only concern the behavior of the vehicle. They can be solved independently of the target platform as long as it can perform the required maneuvers; it does not matter whether it is a UAV or a larger vehicle. The sensing of the environment, on the other hand, is the only subproblem that places requirements on the hardware, specifically the sensors that should be carried by the UAV. It is the hardware that sets UAVs apart from other vehicles. Unlike autonomous cars, other groundbased vehicles or larger aircraft, UAVs have only a small payload capacity. It is therefore not practical to carry large or heavy sensors such as LIDAR or radar for obstacle avoidance. Instead, obstacle avoidance on UAVs requires clever use of lightweight sensors: cameras, microphones or antennae. This research will therefore focus on the sensing of the environment. Out of the sensors mentioned above – cameras, microphones and antennae – cameras are the only ones that can detect nearly all groundbased obstacles and other air traffic; microphones and antennae are limited to detection of sources of noise or radio signals1. Therefore, this research will focus on the visual detection of obstacles. The field of computer vision is welld, Control & Simulation

In micro air vehicles (MAVs), a magnetometer is usually employed for heading estimation. However, in real flight campaigns the magnetometer can easily suffer interference from external magnetic disturbances. In this paper, a simple novel method is proposed to conduct real-time calibration of the magnetometer subject to external disturbances with the aid of gravity and global navigation satellite system measurements. First, two equalities are derived from the rigid-body rotation equations. Then, the closed-form solution to the pseudo magnetometer biases is derived according to these equalities. To enhance the robustness of the solver, an optimizer based on a gradient-descent algorithm is proposed. The bifurcation analysis and convexity proofs are given to show that the proposed optimization has a unique solution and no local optimum. The execution time analysis of the proposed method also indicates that it is a very fast algorithm. Finally, the calibration parameters are determined by algebraic equations formed by pseudo biases. The proposed method is attitude-free, simple and easy-to-implement. The experiments on a real-world fixed-wing MAV demonstrate that the method is effective.

In micro air vehicles (MAVs), a magnetometer is usually employed for heading estimation. However, in real flight campaigns the magnetometer can easily suffer interference from external magnetic disturbances. In this paper, a simple novel method is proposed to conduct real-time calibration of the magnetometer subject to external disturbances with the aid of gravity and global navigation satellite system measurements. First, two equalities are derived from the rigid-body rotation equations. Then, the closed-form solution to the pseudo magnetometer biases is derived according to these equalities. To enhance the robustness of the solver, an optimizer based on a gradient-descent algorithm is proposed. The bifurcation analysis and convexity proofs are given to show that the proposed optimization has a unique solution and no local optimum. The execution time analysis of the proposed method also indicates that it is a very fast algorithm. Finally, the calibration parameters are determined by algebraic equations formed by pseudo biases. The proposed method is attitude-free, simple and easy-to-implement. The experiments on a real-world fixed-wing MAV demonstrate that the method is effective.

Autonomous flight of Flapping Wing Micro Air Vehicles (FWMAVs) is a major challenge in the field of robotics, due to their light weight and their flapping-induced body motions. An FWMAV is presented weighing a mere 20 g while all its sensors and processing for autonomous flight are onboard. The navigation is based on a 4-g stereo vision camera with onboard processing. Three basic navigational tasks are demonstrated, namely obstacle avoidance, door traversing and corridor following. The presented combination of sensors and control routines is shown to allow flight in common unprepared environments like corridors and offices. The algorithms do not depend on prior classification or learning of the environment or control logic and work in any unprepared environment with vertical texture. While some failure cases remain, this work forms an important step towards very small autonomous indoor MAV., Control & Simulation

Flow visualizations have been performed on a free flying, flapping-wing micro air vehicle (MAV), using a large-scale particle image velocimetry (PIV) approach. The PIV method involves the use of helium-filled soap bubbles (HFSB) as tracer particles. HFSB scatter light with much higher intensity than regular seeding particles, comparable to that reflected off the flexible flapping wings. This enables flow field visualization to be achieved close to the flapping wings, in contrast to previous PIV experiments with regular seeding. Unlike previous tethered wind tunnel measurements, in which the vehicle is fixed relative to the measurement setup, the MAV is now flown through the measurement area. In this way, the experiment captures the flow field of the MAV in free flight, allowing the true nature of the flow representative of actual flight to be appreciated. Measurements were performed for two different orientations of the light sheet with respect to the flight direction. In the first configuration, the light sheet is parallel to the flight direction, and visualizes a streamwise plane that intersects the MAV wings at a specific spanwise position. In the second configuration, the illumination plane is normal to the flight direction, and visualizes the flow as the MAV passes through the light sheet, Aerodynamics, Control & Simulation

Due to their potential to expand our sensing and mission capabilities in both military and civilian applications, micro air vehicles (MAVs) have recently gained increased recognition. However, man-made MAVs have struggled to meet the aerodynamic performance and maneuvering capabilities of biological flapping wing flyers (small birds and insects) which operate at MAV-scales (Reynolds numbers on the order of 103–104). Several past studies have focused on developing and analyzing flapping-wing MAV designs due to the possibility of achieving the increased lift, performance and flight capabilities seen in biological flapping wing flyers. However, there are still a lack of baseline design principles to follow when constructing a flexible flapping wing for a given set of wing kinematics, target lift values, mission capabilities, etc. This is due to the limited understanding of the complex, unsteady flow and aeroelastic effects intrinsic to flexible flapping wings. In the current research, a computational fluid dynamics (CFD) solver was coupled with a computational structural dynamics (CSD) solver to simulate the aerodynamics and inherent aeroelastic effects of a flexible flapping wing in hover. The coupled aeroelastic solver was validated against experimental test data to assess the predictive capability of the coupled solver. The predicted and experimental results showed good correlation over several different test cases. Experimental tests included particle image velocimetry (PIV) measurements, instantaneous aerodynamic force measurements and dynamic wing deformation recordings via a motion capture system. The aeroelastic solver was able to adequately predict the process of leading edge vortex (LEV) formation and shedding observed during experimentation. Additionally, the instantaneous lift and drag force-time histories as well as passive wing deformations agreed satisfactorily with the experimental measurements. The coupled CFD/CSD solver was used to determine how varie

Flow visualizations have been performed on a free flying, flapping-wing micro air vehicle (MAV), using a large-scale particle image velocimetry (PIV) approach. The PIV method involves the use of helium-filled soap bubbles (HFSB) as tracer particles. HFSB scatter light with much higher intensity than regular seeding particles, comparable to that reflected off the flexible flapping wings. This enables flow field visualization to be achieved close to the flapping wings, in contrast to previous PIV experiments with regular seeding. Unlike previous tethered wind tunnel measurements, in which the vehicle is fixed relative to the measurement setup, the MAV is now flown through the measurement area. In this way, the experiment captures the flow field of the MAV in free flight, allowing the true nature of the flow representative of actual flight to be appreciated. Measurements were performed for two different orientations of the light sheet with respect to the flight direction. In the first configuration, the light sheet is parallel to the flight direction, and visualizes a streamwise plane that intersects the MAV wings at a specific spanwise position. In the second configuration, the illumination plane is normal to the flight direction, and visualizes the flow as the MAV passes through the light sheet, Aerodynamics, Control & Simulation

Autonomous flight of Flapping Wing Micro Air Vehicles (FWMAVs) is a major challenge in the field of robotics, due to their light weight and their flapping-induced body motions. An FWMAV is presented weighing a mere 20 g while all its sensors and processing for autonomous flight are onboard. The navigation is based on a 4-g stereo vision camera with onboard processing. Three basic navigational tasks are demonstrated, namely obstacle avoidance, door traversing and corridor following. The presented combination of sensors and control routines is shown to allow flight in common unprepared environments like corridors and offices. The algorithms do not depend on prior classification or learning of the environment or control logic and work in any unprepared environment with vertical texture. While some failure cases remain, this work forms an important step towards very small autonomous indoor MAV., Control & Simulation

Control & Simulation

One of the major challenges of evolutionary robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction of the sensory inputs and motor actions as a tool to tackle this problem. Abstraction in robots is simply the use of preprocessed sensory inputs and low-level closed-loop control systems that execute higher-level motor commands. To demonstrate the impact abstraction could have, we evolved two controllers with different levels of abstraction to solve a task of forming an asymmetric triangle with a homogeneous swarm of micro air vehicles. The results show that although both controllers can effectively complete the task in simulation, the controller with the lower level of abstraction is not effective on the real vehicle, due to the reality gap. The controller with the higher level of abstraction is, however, effective both in simulation and in reality, suggesting that abstraction can be a useful tool in making evolved behavior robust to the reality gap. Additionally, abstraction aided in reducing the computational complexity of the simulation environment, speeding up the optimization process. Preeminently, we show that the optimized behavior exploits the environment (in this case the identical behavior of the other robots) and performs input shaping to allow the vehicles to fly into and maintain the required formation, demonstrating clear sensory-motor coordination. This shows that the power of the genetic optimization to find complex correlations is not necessarily lost through abstraction as some have suggested., Control & Simulation

This work investigates the design parameters and their consequences in the control of a helicopter rotor combined with a pair of fixed wings. This hybrid vehicle has a light and aerodynamically efficient rotor with a large range of pitch angles to enable both hover and forward flight. Because of the light stiff rotor and heavy wings, the hybrid vehicle exhibits couplings between the roll and pitch axes during hover flight. The rotor-wing interaction depends on a lot of parameters. In this article, we utilize a simplified theoretic model and simulations in order to gain insight in the effect of these parameters on the vehicle dynamics. Finally, a controller is designed that compensates undesired coupling between pitch and roll., Control & Simulation

One of the major challenges of evolutionary robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction of the sensory inputs and motor actions as a tool to tackle this problem. Abstraction in robots is simply the use of preprocessed sensory inputs and low-level closed-loop control systems that execute higher-level motor commands. To demonstrate the impact abstraction could have, we evolved two controllers with different levels of abstraction to solve a task of forming an asymmetric triangle with a homogeneous swarm of micro air vehicles. The results show that although both controllers can effectively complete the task in simulation, the controller with the lower level of abstraction is not effective on the real vehicle, due to the reality gap. The controller with the higher level of abstraction is, however, effective both in simulation and in reality, suggesting that abstraction can be a useful tool in making evolved behavior robust to the reality gap. Additionally, abstraction aided in reducing the computational complexity of the simulation environment, speeding up the optimization process. Preeminently, we show that the optimized behavior exploits the environment (in this case the identical behavior of the other robots) and performs input shaping to allow the vehicles to fly into and maintain the required formation, demonstrating clear sensory-motor coordination. This shows that the power of the genetic optimization to find complex correlations is not necessarily lost through abstraction as some have suggested., Control & Simulation

This work investigates the design parameters and their consequences in the control of a helicopter rotor combined with a pair of fixed wings. This hybrid vehicle has a light and aerodynamically efficient rotor with a large range of pitch angles to enable both hover and forward flight. Because of the light stiff rotor and heavy wings, the hybrid vehicle exhibits couplings between the roll and pitch axes during hover flight. The rotor-wing interaction depends on a lot of parameters. In this article, we utilize a simplified theoretic model and simulations in order to gain insight in the effect of these parameters on the vehicle dynamics. Finally, a controller is designed that compensates undesired coupling between pitch and roll., Control & Simulation

Flapping wing vehicles have become a compelling alternative to classical fixed or rotary wing aircraft, especially as unmanned aircraft technology focuses on smaller, more agile platforms. Flying insects provide an inspiration for the control of flapping wing platforms, using limited computational resources in their specialized neural pathways to generate robust, agile performance. The flapping wing design is less studied, and the underlying physical principles are often more complex – non-linear time varying dynamics are dominated by forcing due to complex, unsteady aerodynamics. Reduced order models are critical to formulating tractable sensing and control concepts from the complex physics of flapping wing flight. Previous research has focused on a single methodology for the estimation of flight dynamics. This dissertation investigates the reduced order modeling of flapping wing flight dynamics for the purposes of tractable simulation and control, comparing multiple methodologies. Simplification of rigid body vehicle dynamics due to both linearization and time-invariance is discussed, and computational and experimental verification is presented for a simplified model of flapping wing aerodynamics. Additionally, a novel method is presented to maximize the agility and performance of a flapping wing vehicle when reducing the number of control inputs. These reduced order modeling techniques are applied to both a model of a small flying insect and to a flapping wing micro air vehicle.

Flapping wing micro air vehicles (MAVs) take inspiration from natural fliers, such as insects and hummingbirds. Existing designs manage to mimic the wing motion of natural fliers to a certain extent; nevertheless, differences will always exist due to completely different building blocks of biological and man-made systems. The same holds true for the design of the wings themselves, as biological and engineering materials differ significantly. This paper presents results of experimental optimization of wing shape of a flexible wing for a hummingbird-sized flapping wing MAV. During the experiments we varied the wing 'slackness' (defined by a camber angle), the wing shape (determined by the aspect and taper ratios) and the surface area. Apart from the generated lift, we also evaluated the overall power efficiency of the flapping wing MAV achieved with the various wing design. The results indicate that especially the camber angle and aspect ratio have a critical impact on the force production and efficiency. The best performance was obtained with a wing of trapezoidal shape with a straight leading edge and an aspect ratio of 9.3, both parameters being very similar to a typical hummingbird wing. Finally, the wing performance was demonstrated by a lift-off of a 17.2 g flapping wing robot., SCOPUS: ar.j, info:eu-repo/semantics/published

The visual cue of optical flow plays an important role in the navigation of flying insects, and is increasingly studied for use by small flying robots as well. A major problem is that successful optical flow control seems to require distance estimates, while optical flow is known to provide only the ratio of velocity to distance. In this article, a novel, stability-based strategy is proposed for monocular distance estimation, relying on optical flow maneuvers and knowledge of the control inputs (efference copies). It is shown analytically that given a fixed control gain, the stability of a constant divergence control loop only depends on the distance to the approached surface. At close distances, the control loop starts to exhibit self-induced oscillations. The robot can detect these oscillations and hence be aware of the distance to the surface. The proposed stability-based strategy for estimating distances has two main attractive characteristics. First, self-induced oscillations can be detected robustly by the robot and are hardly influenced by wind. Second, the distance can be estimated during a zero divergence maneuver, i.e., around hover. The stability-based strategy is implemented and tested both in simulation and on board a Parrot AR drone 2.0. It is shown that the strategy can be used to: (1) trigger a final approach response during a constant divergence landing with fixed gain, (2) estimate the distance in hover, and (3) estimate distances during an entire landing if the robot uses adaptive gain control to continuously stay on the 'edge of oscillation.', Control & Simulation

The interest in Micro Air Vehicles (MAVs) has stimulated continuous research activities, in view of their potential in civilian and military applications. An autonomous MAV with dedicated onboard sensors would be capable of executing mission in closed environments, such as surveillance, in door inspection in support of rescue operations and information gathering. Flapping wing MAV (also referred to as ’ornithopter’), which is the most intriguing type in the MAV family is often inspired from biological examples has triggered attention due to their outstanding manoeuvrability and flight capability at the low Reynolds number regime. Like natural flyers, flapping wing MAVs are usually equipped with a single or multiple pairs of flexible wings to generate both thrust and lift at the same time. Compared to the other locomotion, the flappingmotion benefits from a number of specific unsteady lift enhancement mechanisms. This thesis performs an integrated experimental and numerical study in order to explore the aerodynamic behaviour associated with flapping wing MAVs. The study is hereby divided into twofold: (1) Experimentally investigate the aerodynamic characteristics of flappingwingMAV, using state-of-the-artmeasurement techniques; (2) Develop a novel numerical methodology with particular focus in simulating flapping wing aerodynamics. For the experimental study, theDelFlyMicro was employed as the testbed in this thesis. The DelFly Micro uses an X-wing configuration which performs the clap-and-fling mechanisms three times during each flapping period and thus will result in complex flow behaviour. The aerodynamic characteristics were evaluated by means of extensive force production and power consumption measurements. The aero-structural effect of the DelFly Micro is fist investigated by means of a vacuum test, where the aerodynamically associated thrust component and different deformation patterns in air and in vacuum were addressed. Subsequently, the effect of wing prope, AWEP, Aerospace Engineering

The aerodynamic characteristics around a micro air vehicle wing with an inverse-Zimmerman configuration are numerically investigated by an in-house programmed solver particularly dedicated for aircrafts operating in low Reynolds number regime. The complex three-dimensional aerodynamic performance was investigated in terms of force generation and flow structures visualization. Results show that the flow around the low aspect ratio MAV wing is characterized by complex three-dimensional separation-dominated flow. The flow fields exhibit separation, reattachment, secondary separation, secondary reattachment, and strong interaction between the separated boundary layer and wingtip vortices. In addition, the effect of tip-attached vertical stabilizers on flow structure and aerodynamic forces is addressed in this paper. The stabilizers significantly influence both the flow structure and aerodynamic forces via reducing the strength of wingtip vortices and shedding and interacting of wingtip vortices. Eventually, the unsteadiness of the aerodynamics revealed that higher angle of attack will result in stronger unsteady phenomena as demonstrated by the oscillating forces., Aerodynamics

The visual cue of optical flow plays an important role in the navigation of flying insects, and is increasingly studied for use by small flying robots as well. A major problem is that successful optical flow control seems to require distance estimates, while optical flow is known to provide only the ratio of velocity to distance. In this article, a novel, stability-based strategy is proposed for monocular distance estimation, relying on optical flow maneuvers and knowledge of the control inputs (efference copies). It is shown analytically that given a fixed control gain, the stability of a constant divergence control loop only depends on the distance to the approached surface. At close distances, the control loop starts to exhibit self-induced oscillations. The robot can detect these oscillations and hence be aware of the distance to the surface. The proposed stability-based strategy for estimating distances has two main attractive characteristics. First, self-induced oscillations can be detected robustly by the robot and are hardly influenced by wind. Second, the distance can be estimated during a zero divergence maneuver, i.e., around hover. The stability-based strategy is implemented and tested both in simulation and on board a Parrot AR drone 2.0. It is shown that the strategy can be used to: (1) trigger a final approach response during a constant divergence landing with fixed gain, (2) estimate the distance in hover, and (3) estimate distances during an entire landing if the robot uses adaptive gain control to continuously stay on the 'edge of oscillation.', Control & Simulation

The interest in Micro Air Vehicles (MAVs) has stimulated continuous research activities, in view of their potential in civilian and military applications. An autonomous MAV with dedicated onboard sensors would be capable of executing mission in closed environments, such as surveillance, in door inspection in support of rescue operations and information gathering. Flapping wing MAV (also referred to as ’ornithopter’), which is the most intriguing type in the MAV family is often inspired from biological examples has triggered attention due to their outstanding manoeuvrability and flight capability at the low Reynolds number regime. Like natural flyers, flapping wing MAVs are usually equipped with a single or multiple pairs of flexible wings to generate both thrust and lift at the same time. Compared to the other locomotion, the flappingmotion benefits from a number of specific unsteady lift enhancement mechanisms. This thesis performs an integrated experimental and numerical study in order to explore the aerodynamic behaviour associated with flapping wing MAVs. The study is hereby divided into twofold: (1) Experimentally investigate the aerodynamic characteristics of flappingwingMAV, using state-of-the-artmeasurement techniques; (2) Develop a novel numerical methodology with particular focus in simulating flapping wing aerodynamics. For the experimental study, theDelFlyMicro was employed as the testbed in this thesis. The DelFly Micro uses an X-wing configuration which performs the clap-and-fling mechanisms three times during each flapping period and thus will result in complex flow behaviour. The aerodynamic characteristics were evaluated by means of extensive force production and power consumption measurements. The aero-structural effect of the DelFly Micro is fist investigated by means of a vacuum test, where the aerodynamically associated thrust component and different deformation patterns in air and in vacuum were addressed. Subsequently, the effect of wing prope, AWEP, Aerospace Engineering

The aerodynamic characteristics around a micro air vehicle wing with an inverse-Zimmerman configuration are numerically investigated by an in-house programmed solver particularly dedicated for aircrafts operating in low Reynolds number regime. The complex three-dimensional aerodynamic performance was investigated in terms of force generation and flow structures visualization. Results show that the flow around the low aspect ratio MAV wing is characterized by complex three-dimensional separation-dominated flow. The flow fields exhibit separation, reattachment, secondary separation, secondary reattachment, and strong interaction between the separated boundary layer and wingtip vortices. In addition, the effect of tip-attached vertical stabilizers on flow structure and aerodynamic forces is addressed in this paper. The stabilizers significantly influence both the flow structure and aerodynamic forces via reducing the strength of wingtip vortices and shedding and interacting of wingtip vortices. Eventually, the unsteadiness of the aerodynamics revealed that higher angle of attack will result in stronger unsteady phenomena as demonstrated by the oscillating forces., Aerodynamics

The goal of the present research is to understand aerodynamics at low Reynolds numbers and synthesize rules towards the development of hovering micro rotary-wing air vehicles (MRAVs). This entailed the rigorous study of airfoil characteristics at low Reynolds numbers through available experimental results as well as the use of an unsteady Reynolds-Averaged Navier-Stokes solver. A systematic, experimental, variation of parameters approach with physical rotors was carried out to design and develop a micro air vehicle-scale rotor which maximizes the hover Figure of Merit. The insights gained in low Reynolds number aerodynamics have been utilized in the systematic design of a high endurance micro-quadrotor. Based on available characteristics, the physical relations governing electric propulsion system and structural weights have been derived towards a sizing methodology for small-scale rotary-wing vehicles.

In this project a start has been made in the conceptual and practical development of a hydrogen peroxide catalytic micro expansion engine. The liquid fuel convertor is designed specifically to mechanically actuate a resonant body flapping wing micro air vehicle of approximately 4 gram. The actuator functionality is devided in several sub functions which were analysed using knowledge of scaling, boundary conditions and previous research. This resulted in a functional design descripition. (Chapter 2, 3) The design description was used to make a working concept, critical components and functionalities were defined. The components to provide fuel delivery and reaction were selected for further developement. (Chapter 4) Capillary action is a very interesting phenomenon to provide fuel transport and accumulation at the reaction site. It can repeatedly provide a fuel volume with a sufficiently high flow rate. The size of channels have to be in the order of 1mm, surface tension can also provide a back pressure. Uncertainties are encountered when integrating the system with batched reaction. The amount of fuel transfered/reacted is a complex function of time, shapes and material properties. (Chapter 5) Production of manganese oxide material was performed on aluminium plates to find a light and practical substrate for the catalyst. Fabrication parameters were varied to optimize the robustness and activity of the layer. (Chapter 6) A design is made and tested of a simple prototype for the actuator consisting of a fuel delivery system and a timed catalytic reaction in a combined reaction-expansion chamber. Temporary components are used to perform the functions of fuel storage, expansion, exhaust and suspending. These components will be designed in a later stage. (Chapter 7), Precision and Microsystems Engineering, Mechanical, Maritime and Materials Engineering

In this project a start has been made in the conceptual and practical development of a hydrogen peroxide catalytic micro expansion engine. The liquid fuel convertor is designed specifically to mechanically actuate a resonant body flapping wing micro air vehicle of approximately 4 gram. The actuator functionality is devided in several sub functions which were analysed using knowledge of scaling, boundary conditions and previous research. This resulted in a functional design descripition. (Chapter 2, 3) The design description was used to make a working concept, critical components and functionalities were defined. The components to provide fuel delivery and reaction were selected for further developement. (Chapter 4) Capillary action is a very interesting phenomenon to provide fuel transport and accumulation at the reaction site. It can repeatedly provide a fuel volume with a sufficiently high flow rate. The size of channels have to be in the order of 1mm, surface tension can also provide a back pressure. Uncertainties are encountered when integrating the system with batched reaction. The amount of fuel transfered/reacted is a complex function of time, shapes and material properties. (Chapter 5) Production of manganese oxide material was performed on aluminium plates to find a light and practical substrate for the catalyst. Fabrication parameters were varied to optimize the robustness and activity of the layer. (Chapter 6) A design is made and tested of a simple prototype for the actuator consisting of a fuel delivery system and a timed catalytic reaction in a combined reaction-expansion chamber. Temporary components are used to perform the functions of fuel storage, expansion, exhaust and suspending. These components will be designed in a later stage. (Chapter 7), Precision and Microsystems Engineering, Mechanical, Maritime and Materials Engineering

MAV (Micro Aerial Vehicle) systems have proven useful in multiple applications (e.g. aerial photogrammetry, aerial inspections,...). For each application different sensors and even different airframes are often needed. Having a flexible UAS (Unmanned Aircraft System) devices (specifically an autopilot, a payload manager and a ground control station) that can be seamlessly used with different platforms is of great interest as it reduces the development time and effort (and hence cost). This flexibility must apply to the hardware, software and design methodologies so the system can be rapidly adapted to meet the requirements of other applications. This paper introduces a flexible system developed by CATEC consisting of an autopilot, Ground Control Station (GCS) and a payload manager for MAVs. This system has already been tested in real experiments for different applications that are also presented in this paper.

This paper addresses the unsteady aerodynamic mechanisms in the hovering flight of the DelFly II flapping-wing Micro Aerial Vehicle (MAV). Stereoscopic Particle Image Velocimetry (Stereo-PIV) were carried out around the wings at a high framing rate. Thrust-force was measured to investigate the relation between the vortex dynamics and the aerodynamic force generation. The results reveal that the Leading-Edge-Vortex (LEV), as well as the high flexibility of the wings, have a major effect on thrust generation. Comparison of three wing pairs with different aspect ratio (AR) is also reported, yielding significant differences in both vortical structures and thrust generation.

The IMAV2014 Competition gives an important place to computer vision and aerial mapping. This paper presents the different strategies and algorithms that have been developed in order to tackle these particular problems. The first task consists in automatically reading the digit displayed by a seven segment panel on the front wall of a building. Robust algorithms for segments detection are used in order to extract the features from the scene. The second task requires to produce an aerial map of the competition place. In order to generate a coherent composite image, photogrammetry methods are used, including interest points pairing and bundle adjustment. We propose to mix 3D mapping methods and standard 2D image mosaicking in order to achieve the processing in the time available during the competition while obtaining an geometrically consistent map.

This paper presents an overview of the Paparazzi UAV system and its recent use in scientific research. Paparazzi is an open-source project that aims at providing a complete solution to fly fixedwing aircraft and rotorcrafts. Several hardware boards and sensors are also developed within the project. Since several years, it has been used by various institutes for scientific research. The recent use on scientific research for meteorological studies is presented as an example.

This paper focuses on the use of the Virtual Robotics Experimental Platform (V-REP) and the Robotics Operative System (ROS) working in parallel for design, test, and tuning of a vision based control system to command an Unmanned Aerial Vehicle (UAV). Here, is presented how to configure the V-REP, and ROS to work in parallel, and how to use the developed packages in ROS for the pose estimation based on vision and for the design and use of a fuzzy logic control system. It is also shown in this paper a novel vision based fuzzy control approach for the autonomous landing task on a static and on a moving platform. The control system is based on four fuzzy logic controllers (FLC) working in parallel on an external control loop based on the visual information. All the controllers were designed and tuned to command the vertical, longitudinal, lateral, and heading velocities of the UAV.

Micro Aerial Vehicles (MAVs) are small unmanned aerial vehicles (UAVs) that are generally equipped with camera, GPS and other sensors and are envisioned for many civil and commercial applications. Some of these applications require transmitting multimedia traffic and demand for a high wireless network throughput. In this paper, we consider an application scenario where a team of MAVs cover multiple areas of interest; e.g., during sports events, following known trajectories (mobility paths) and transmitting continuous streams of sensed traffic (images or video) to a ground station. We propose a Route Switching (RS) algorithm that utilizes both the location and the trajectory information of the MAVs to schedule and update routes to seamlessly transmit traffic to the destination. Simulation results show improved network performance in terms of throughput in comparison to Ad-hoc On demand Distance Vector (AODV) and Location Aided Routing (LAR) since the proposed algorithm exploits the added path information for route discovery.

Flapping wing micro air vehicles (MAVs) have the advantage of being able to fly at slow speeds and have high maneuverability. The design of a flapping wing MAV inspired by birds presents many technical challenges because birds as natural fliers exhibit higher efficiency than any man made flapping wing structure. Thus, the structure and motion of bird wings provide a starting point for the study of flapping wing MAV. In order to compare the deflections of the fabricated wings with the complex mechanism of the bird wings, it is required to quantify these deflections. Both the bird wings and the fabricated wings are very light weight and, therefore extremely light weight sensors or non-contact methods are required for the measurements of the deflections. Digital image correlation (DIC) provides one such option. DIC is an optical noncontact method used to acquire displacements. A simple two dimensional DIC code has been developed and validated in this work. Design and fabrication of wings is based on the hummingbirds. A 3D tapered wing design was developed and used for development of mold. This mold was used for fabrication of flexible polypropylene wings. For flapping motion measurements of wings, a simple slider-crank mechanism was designed to generate an oscillatory motion using brushless DC motors and Arduino Board. For modal analysis, an electrodynamic shaker was used to vibrate the wing with patterns stuck over its surface at its natural frequencies and captured the motion using a high speed camera. The captured images were analyzed using the developed DIC code and mode shapes for 1st and 2nd modes were obtained. For capturing the motion of a wing, a high speed camera mounted on a suitably designed stand was used. The validation of modal analysis was done using commercial finite element analysis software Ansys.

For Micro Aerial Vehicles (MAVs), robust obstacle avoidance during flight is a challenging problem because only light weight sensors as monocular cameras can be mounted as payload. With monocular cameras depth perception cannot be estimated from a single view, which means that information has to be estimated by combining images from multiple viewpoints. Here we present a method which focuses only on regions classified as foreground and follow features in the foreground to estimate threat of potential objects being an obstacle in the flight path by depth segmentation and scale estimation. We evaluate results from our approach in an outdoor environment with a small quadrotor drone and analyze the effect of the different stages in the image processing pipeline. We are able to determine evident obstacles in front of the drone with high confidence. Robust obstacleavoidance algorithms as presented in this article will allow Micro Aerial Vehicles to navigate semi-autonomously in outdoor scenarios.

This paper describes the hardware components and the embedded software of a self-designed unmanned aerial system (UAS) to fulfill the task of an autonomous indoor exploration. The project, the paper is based on, was conducted at the University of Braunschweig - Institute of Technology by students and scientific researchers studying and working at the Institute of Flight Guidance. The paper is structured as follows. The first part deals with the development, selection, collocation and assembling of the appropriate hardware to obtain a reliable and robust system which suits the task of controlling the UAS, collecting information about the environment and providing the computational resources to execute the implemented algorithms. The second part deals with the software architecture that generates the control commands for the quadrotor UAS and ensures the execution of the mission by the stepwise fulfillment of single tasks.

This research employs ecological interface design to improve the human machine interface of an existing ground control station for the supervisory control of UAV swarms. As a case study, a general ground surveillance mission with four UAVs is envisioned. An analysis of the swarming work domain is performed to generate a reduced set of means-end relations. This analysis leads to a novel design that shows predicted coverage and range of the UAVs in the swarm. An evaluation study with 10 participants showed that the new interface successfully enables operators to control a swarm of four UAVs and mitigate problems during mission execution. The results of the evaluation study showed that operators had a better system understanding and that it promoted creative problem solving activities to scenarios that could not be solved by fixed procedures.