Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: Genetic algorithms; planform; optimization; micro aerial vehicle. Includes bibliographical references (p.71-73).

"Micro aerial vehicles (MAV) are small remotely piloted or autonomous aircraft. Wingspans of MAVs can be as small as six inches to allow MAV’s to avoid detection during reconnaissance missions. Improving the aerodynamic efficiency of MAV’s by increasing the lift to drag ratio could lead to increased MAV range and endurance or future decreases in aircraft size. In this project, biologically inspired flight is used as a framework to improve MAV performance since MAV’s operate in a similar flight regime to birds. A novel wind tunnel apparatus was constructed that allows the planform shape of a MAV wing to be easily altered. The scale-model wing mimics a bird wing by using variable feather lengths to vary the wing planform shape. Genetic algorithms that use natural selection as an optimization process were applied to establish successive populations of candidate wing shapes. These wing shapes were tested in the wind tunnel where wings with higher fitness values were allowed to ‘breed’ and create a next generation of wings. After numerous generations were tested an acceptably strong solution was found that yielded a lift to drag ratio of 3.28. This planform was a non conventional planform that further emphasized the ability of a genetic algorithm to find a novel solution to a complex problem. Performance of the best planform was compared to previously published data for conventional MAV planform shapes. Results of this comparison show that while the highest lift to drag ratio found from the genetic algorithm is lower than published data, inabilities of the test wing to accurately represent a flat plate Zimmerman planform and limitations of the test setup can account for these discrepancies."

The scope of the thesis is to numerically investigate the aerodynamics of flapping airfoils in hover and forward flight. The flowfields around flapping airfoils are computed by solving the governing equations on moving and/or deforming grids. The effects of Reynolds number, reduced frequency and airfoil geometry on unsteady aerodynamics of flapping airfoils undergoing pure plunge and combined pitch-plunge motions in forward flight are investigated. It is observed that dynamic stall of the airfoil is the main mechanism of lift augmentation for both motions at all Reynolds numbers ranging from 10000 to 60000. However, the strength and duration of the leading edge vortex vary with airfoil geometry and reduced frequency. It is also observed that more favorable force characteristics are achieved at higher reduced frequencies and low plunging amplitudes while keeping the Strouhal number constant. The computed flowfields are compared with the wide range of experimental studies and high fidelity simulations thus it is concluded that the present approach is applicable for investigating the flapping wing aerodynamics in forward flight. The effects of vertical translation amplitude and Reynolds number on flapping airfoils in hover are also studied. As the vertical translation amplitude increases, the vortices become stronger and the formation of leading edge vortex is pushed towards the midstroke of the motion. The instantaneous aerodynamic forces for a given figure-of-eight motion do not alter significantly for Reynolds numbers ranging from 500 to 5500.

The Micro Aerial Vehicless (MAVs) are gaining attention in numerous applications asthese platforms are cheap and can do complex maneuvers. Moreover, most of the commer-cially available MAVs are equipped with a mono-camera. Currently, there is an increasinginterest to deploy autonomous mono-camera MAVs with obstacle avoidance capabilitiesin various complex application areas. Some of the application areas have moving obstaclesas well as stationary, which makes it more challenging for collision avoidance schemes.This master thesis set out to investigate the possibility to avoid moving and station-ary obstacles with a single camera as the only sensor gathering information from thesurrounding environment.One concept to perform autonomous obstacle avoidance is to predict the time near-collision based on a Convolution Neural Network (CNN) architecture that uses the videofeed from a mono-camera. In this way, the heading of the MAV is regulated to maximizethe time to a collision, resulting in the avoidance maneuver. Moreover, another interestingperspective is when due to multiple dynamic obstacles in the environment there aremultiple time predictions for different parts of the Field of View (FoV). The method ismaximizing time to a collision by choosing the part with the largest time to collision.However, this is a complicated task and this thesis provides an overview of it whilediscussing the challenges and possible future directions. One of the main reason was thatthe available data set was not reliable and was not provide enough information for theCNN to produce any acceptable predictions.Moreover, this thesis looks into another approach for avoiding collisions, using objectdetection method You Only Lock Once (YOLO) with the mono-camera video feed. YOLOis a state-of-the-art network that can detect objects and produce bounding boxes in real-time. Because of YOLOs high success rate and speed were it chosen to be used in thisthesis. When YOLO detects an obstacle it is telling where in the image the object is,the obstacle pixel coordinates. By utilizing the images FoV and trigonometry can pixelcoordinates be transformed to an angle, assuming the lens does not distort the image.This position information can then be used to avoid obstacles. The method is evaluated insimulation environment Gazebo and experimental verification with commercial availableMAV Parrot Bebop 2. While the obtained results show the efficiency of the method. To bemore specific, the proposed method is capable to avoid dynamic and stationary obstacles.Future works will be the evaluation of this method in more complex environments with multiple dynamic obstacles for autonomous navigation of a team of MAVs. A video ofthe experiments can be viewed at:https://youtu.be/g_zL6eVqgVM.

Though still in relative infancy, the field of flapping flight has potential to have a far-reaching impact on human life. Nature presents a myriad of examples of successful uses of this locomotion. Human efforts in flapping flight have seen substantial improvement in recent times. Wing kinematics are a key aspect of this study. This study summarizes previous wing trajectory generators and presents a new trajectory generation method built upon previous methods. This includes a novel means of commanding unequal half-stroke durations subject to robotic trajectory continuity requirements. Additionally, previous optimization methods are improved upon. Experimental optimization is performed using the new trajectory generation method and a more traditional means. Methods for quantifying and compensating for sensor time-dependence are also discussed. Results show that the Polar Fourier Series trajectory generator advanced rapidly through the optimization process, especially during the initial phase of experimentation. The Modified Berman and Wang trajectory generator moved through the design space more slowly due to the increased number of kinematic parameters. When optimizing lift only, the trajectory generators produced similar results and kinematic forms. The findings suggest that the objective statement should be modified to reward efficiency while maintaining a certain amount of lift. It is expected that the difference between the capabilities of the two trajectory generators will become more apparent under such conditions.

Le travail présenté dans cette thèse est consacré à la modélisation de la dynamique de locomotion des "soft robots", i.e. les systèmes multi-corps mobiles compliants. Ces compliances peuvent être localisées et considérées comme des liaisons passives du système,ou bien introduites par des flexibilités distribuées le long des corps. La dynamique de ces systèmes est modélisée en adoptant une approche Lagrangienne basée sur les outils mathématiques développés par l’école américaine de mécanique géométrique. Du point de vue algorithmique, le calcul de ces modèles dynamiques s’appuie sur un algorithme récursif et efficace de type Newton-Euler, ici étendu aux robots locomoteurs munis d’organes compliants. Poursuivant des objectifs de commande et de simulation rapide pour la robotique, l’algorithme proposé est capable de résoudre la dynamique externe directe ainsi que la dynamique inverse des couples internes. Afin de mettre en pratique l’ensemble de ces outils de modélisation, nous avons pris le vol battant des insectes comme exemple illustratif. Les équations non-linéaires qui régissent les déformations passives de l’aile sont établies en appliquant deux méthodes différentes. La première consiste à séparer le mouvement de l’aile en une composante rigide dite de "repère flottant" et une composante de déformation. Cette dernière est paramétrée dans le repère flottant par la méthode des modes supposés ici appliquée à l’aile vue comme une poutre d’Euler-Bernoulli soumise à la flexion et à la torsion. Quant à la seconde approche, les mouvements de l’aile n’y sont pas séparés mais directement paramétrés par les transformations finies rigides et absolues d’une poutre Cosserat. Cette approche est dite Galiléenne ou "géométriquement exacte" en raison du fait qu’elle ne requiert aucune approximation en dehors des inévitables discrétisations spatiale et temporelle imposées parla résolution numérique de la dynamique du vol. Dans les deux cas,les forces aérodynamiques sont prises en compte via un modèle analytique simplifié de type Dickinson. Les modèles et algorithmes résultants sont appliqués à la conception d’un simulateur du vol, ainsi qu’à la conception d’un prototype d’aile, dans le contexte du projet coopératif (ANR) EVA. / The objective of the present work is to model the locomotion dynamics of "soft robots", i.e. compliant mobile multi-body systems. These compliances can be either localized and treated as passive joints of the system, or introduced by distributed flexibilities along the bodies. The dynamics of these systems is modeled in a Lagrangian approach based on the mathematical tools developed by the American school of geometric mechanics. From the algorithmic viewpoint, the computation of these dynamic models is based on a recursive and efficient Newton-Euler algorithm which is extended here to the case of robots equipped with compliant organs. The proposed algorithm is compatible with control, fast simulation and real time robotic applications. It is able to solve the direct external dynamics as well as the inverse internal torque dynamics. The modeling tools and algorithms developed in this thesis are applied to one of the most advanced cases of compliante locomotion i.e. the flapping flight MAVs bio-inspired by insects. The nonlinear equations governing the passive deformations of the wing are derived using two different methods. In the first method, we separate the wing movement into a rigid component (which corresponds to the movements of a "floating frame"), and a deformation component. The latter one is parameterized in the floating frame using the assumed modes approach where the wing is considered as an Euler-Bernoulli beam undergoing flexion and torsion deformations. Regarding the second method, the wing movements are no longer separated but directly parameterize dusing rigid finite absolute transformations of a Cosserat beam. This method is called Galilean or "geometrically exact" because it does not require any approximation apart from the unavoidable spatial and temporal discretizations imposed by numerical resolution of the flight dynamics. In both cases, the aerodynamic forces are taken into account through a simplified analytical model. The resulting models and algorithms are used in the context of the collaborative project (ANR) EVA to develop a flight simulator, and to design wing prototype.