DESIGN AND CONTROL OF A HUMMINGBIRD-SIZE FLAPPING WING MICRO AERIAL VEHICLE

Flying animals with flapping wings may best exemplify the astonishing ability of natural selection on design optimization. They evince extraordinary prowess to control their flight, while demonstrating rich repertoire of agile maneuvers. They remain surprisingly stable during hover and can make sharp turns in a split second. Characterized by high-frequency flapping wing motion, unsteady aerodynamics, and the ability to hover and perform fast maneuvers, insect-like flapping flight presents an extraordinary aerial locomotion strategy perfected at small size scales. Flapping Wing Micro Aerial Vehicles (FWMAVs) hold great promise in bridging the performance gap between engineered flying vehicles and their natural counterparts. They are perfect candidates for potential applications such as fast response robots in search and rescue, environmental friendly agents in precision agriculture, surveillance and intelligence gathering MAVs, and miniature nodes in sensor networks

Evaluation of the Thorax of Manduca Sexta for Flapping Wing Micro Air Vehicle Applications

The tobacco hornworm hawkmoth (Manduca sexta) provides an excellent model from which to gather knowledge pertaining to the development of a Flapping Wing Micro Air Vehicle (FWMAV). One of the major challenges in design of a FWMAV is the energy demanding nature of low Reynolds number flapping flight. Therefore, an understanding of the power required by the flight muscles to actuate the wings is essential for the design of a FWMAV. The M.sexta wing/thorax mechanism was evaluated as a mechanical system in order to gain insight to the mechanical power required to produce the full natural wing stroke. A unique dynamic load device was designed and constructed to mechanically actuate the upstroke and downstroke of the M.sexta in order to achieve the full flapping motion. Additionally, the forces applied through the flight muscles were directly measured in order to attain the power requirements of the flight muscles simultaneously. The experiment yielded wing stroke amplitudes of + 60 and - 35, which is what is seen in nature during hovering. The DVM and DLM muscle groups were calculated to have a power density of 112 W/kg with the vehicle energy density being 2 W/kg. The power output requirement indicates the need for a lightweight and energy-dense power source/actuator combination for the development of FWMAVs

Design and Development of a Bioinspired Gliding-optimised Tandem Wings for Micro Aerial Robots

Research on aerial robotics particularly the articulated/flapping wing robots have gained a remarkable attention during the recent years due to their agility and stealthiness in surveillance and reconnaissance missions. However, wing flapping is highly energy-intensive that can be complemented by gliding to conserve energy for long-range flights as demonstrated by desert locusts (Schistocerca gregaria). Therefore, inspired by this insect we explore novel solutions in this thesis to address some of the challenging problems of aeronautics concerning development, fabrication, and aerodynamic optimisation of a bio-inspired glid�ing wing for micro aerial vehicle (MAV) applications. Initially, we investigate the aerofoil geometries (2D wings) of a locust by performing a pseudo-microscopic scanning of its wings in gliding posture. Using numerical analysis and a novel optimisation methodology based on Nash-Genetic Algorithms, we study and enhance the aerodynamic performance of the digitally reconstructed aerofoils. The optimised as well as the original aerofoils are integrated using Computer-Aided Design (CAD) to form 3D wings that are subjected to a Computational Fluid Dynamics (CFD) modelling, and a Finite Element Analysis (FEA) to validate their aerodynamic performance and manufacturing-worthiness, respectively. Having established the required performance criteria numerically, a novel combination of fabrication techniques involving 3D printing, vacuum thermoforming, and laser trimming is proposed to realise the digital wings as artificial wing prototypes. Furthermore, we explore the novel Computer-Associated Design (CAsD) based on machine learning technology to obtain computer-generated wings for a detailed comparative study of the mechanical properties associated with each wing prototype designed by an engineer, a computer, and the nature

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Real-Time, Multiple Pan/Tilt/Zoom Computer Vision Tracking and 3D Positioning System for Unmanned Aerial System Metrology

The study of structural characteristics of Unmanned Aerial Systems (UASs) continues to be an important field of research for developing state of the art nano/micro systems. Development of a metrology system using computer vision (CV) tracking and 3D point extraction would provide an avenue for making these theoretical developments. This work provides a portable, scalable system capable of real-time tracking, zooming, and 3D position estimation of a UAS using multiple cameras. Current state-of-the-art photogrammetry systems use retro-reflective markers or single point lasers to obtain object poses and/or positions over time. Using a CV pan/tilt/zoom (PTZ) system has the potential to circumvent their limitations. The system developed in this paper exploits parallel-processing and the GPU for CV-tracking, using optical flow and known camera motion, in order to capture a moving object using two PTU cameras. The parallel-processing technique developed in this work is versatile, allowing the ability to test other CV methods with a PTZ system using known camera motion. Utilizing known camera poses, the object\u27s 3D position is estimated and focal lengths are estimated for filling the image to a desired amount. This system is tested against truth data obtained using an industrial system

Applied Aerodynamics

Aerodynamics, from a modern point of view, is a branch of physics that study physical laws and their applications, regarding the displacement of a body into a fluid, such concept could be applied to any body moving in a fluid at rest or any fluid moving around a body at rest. This Book covers a small part of the numerous cases of stationary and non stationary aerodynamics; wave generation and propagation; wind energy; flow control techniques and, also, sports aerodynamics. It's not an undergraduate text but is thought to be useful for those teachers and/or researchers which work in the several branches of applied aerodynamics and/or applied fluid dynamics, from experiments procedures to computational methods

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Development of a Forced Oscillation Test Technique for Determination of MAV Stability Characteristics

This thesis presents the development and validation of a forced oscillation test technique for the determination of Micro Air Vehicle (MAV) stability characteristics. The test setup utilizes a scotch yoke mechanism to oscillate a MAV along a single axis at a fixed amplitude and frequency. The aerodynamic reaction forces to this sinusoidal perturbation are measured and converted into meaningful stability parameters. The purpose of this research is to demonstrate that forced oscillation testing is an effective means of measuring the stability parameters of a MAV. Initial tests show that the forced oscillation test process is returning results which match the expected trends. Comparison of the results to an analytical model of blade flapping shows that the experimental results are of the proper magnitude. It can be concluded from this research that forced oscillation testing is a feasible method for determining the stability parameters of MAVs