Furthering our understanding of the physics of flapping flight has the potential to benefit the field of micro air vehicles. Advancements in micro air vehicles can benefit applications such as surveillance, reconnaissance, and search and rescue. In this research, flapping kinematics of a ladybug was explored using a direct linear transformation. A flapping mechanism design is presented that was capable of executing ladybug or other species-specific kinematics. The mechanism was based on a differential gear design, had two wings, and could flap in harsh environments. This mechanism served as a test bed for force analysis and optimization studies. The first study was based on a Box-Behnken screening design to explore wing kinematic parameter design space and manually search in the direction of flapping kinematics that optimized the objective of maximum combined lift and thrust. The second study used a Box-Behnken screening design to build a response surface. Using gradient-based techniques, this surface was optimized for maximum combined lift and thrust. Box-Behnken design coupled with response surface methodology was an efficient method for exploring the mechanism force response. Both methods for optimization were capable of successfully improving lift and thrust force outputs. The incorporation of the results of these studies will aid in the design of more efficient micro air vehicles and with the ultimate goal of leading to a better understanding of flapping wing aerodynamics and the development of aerodynamic models.

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The conventional Mini and Large scale Unmanned Aerial Vehicle systems span anywhere from approximately 12 inches to 12 feet; endowing them with larger propulsion systems, batteries/fuel-tanks, which in turn provide ample power reserves for long-endurance flights, powerful actuators, on-board avionics, wireless telemetry etc. The limitations thus imposed become apparent when shifting to Micro Aerial Vehicles (MAVs) and trying to equip them with equal or near-equal flight endurance, processing, sensing and communication capabilities, as their larger scale cousins. The conventional MAV as outlined by The Defense Advanced Research Projects Agency (DARPA) is a vehicle that can have a maximum dimension of 6 inches and weighs no more than 100 grams. Under these tight constraints, the footprint, weight and power reserves available to on-board avionics and actuators is drastically reduced; the flight time and payload capability of MAVs take a massive plummet in keeping with these stringent size constraints. However, the demand for micro flying robots is increasing rapidly. The applications that have emerged over the years for MAVs include search&rescue operations for trapped victims in natural disaster succumbed urban areas; search&reconnaissance in biological, radiation, natural disaster/hazard succumbed/prone areas; patrolling&securing home/office/building premises/urban areas. VTOL capable rotary and fixed wing flying vehicles do not scale down to micro sized levels, owing to the severe loss in aerodynamic efficiency associated with low Reynolds number physics on conventional airfoils; whereas, present state of the art in flapping wing designs lack in one or more of the minimum qualities required from an MAV: Appreciable flight time, appreciable payload capacity for on-board sensors/telemetry and 6DoF hovering/VTOL performance. This PhD. work is directed towards overcoming these limitations. Firstly, this PhD thesis presents the advent of a novel Quad-Wing MAV configuration (called the QV). The Four-Wing configuration is capable of performing all 6DoF flight maneuvers including VTOL. The thesis presents the design, conception, simulation study and finally hardware design/development of the MAV. Secondly, this PhD thesis proves and demonstrates significant improvement in on-board Energy-Harvesting resulting in increased flight times and payload capacities of the order of even 200%-400% and more. Thirdly, this PhD thesis defines a new actuation principle called, Fixed Frequency, Variable Amplitude (FiFVA). It is demonstrated that by the use of passive elastic members on wing joints, a further significant increase in energy efficiency and consequently reduction in input power requirements is observed. An actuation efficiency increase of over 100% in many cases is possible. The natural evolution of actuation development led to invention of two novel actuation systems to illustrate the FiFVA actuation principle and consequently show energy savings and flapping efficiency improvement. Lastly, but not in the least, the PhD thesis presents supplementary work in the design, development of two novel Micro Architecture and Control (MARC) avionics platforms (autopilots) for the application of demonstrating flight control and communication capability on-board the Four-Wing Flapping prototype. The design of a novel passive feathering mechanism aimed to improve lift/thrust performance of flapping motion is also presented.

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- / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Establishing and maintaining successful communication stands as a critical prerequisite for achieving the goals of inducing and studying advanced computation in small-scale living neuronal networks. The following work establishes a novel and effective method for communicating arbitrary "sensory" input information to cultures of living neurons, living neuronal networks (LNNs), consisting of approximately 20 000 rat cortical neurons plated on microelectrode arrays (MEAs) containing 60 electrodes. The sensory coding algorithm determines a set of effective codes (symbols), comprised of different spatio-temporal patterns of electrical stimulation, to which the LNN consistently produces unique responses to each individual symbol. The algorithm evaluates random sequences of candidate electrical stimulation patterns for evoked-response separability and reliability via a support vector machine (SVM)-based method, and employing the separability results as a fitness metric, a genetic algorithm subsequently constructs subsets of highly separable symbols (input patterns). Sustainable input/output (I/O) bit rates of 16-20 bits per second with a 10% symbol error rate resulted for time periods of approximately ten minutes to over ten hours. To further evaluate the resulting code sets' performance, I used the system to encode approximately ten hours of sinusoidal input into stimulation patterns that the algorithm selected and was able to recover the original signal with a normalized root-mean-square error of 20-30% using only the recorded LNN responses and trained SVM classifiers. Response variations over the course of several hours observed in the results of the sine wave I/O experiment suggest that the LNNs may retain some short-term memory of the previous input sample and undergo neuroplastic changes in the context of repeated stimulation with sensory coding patterns identified by the algorithm.