The growing need for ocean surveillance and exploration has pushed the development of novel autonomous underwater vehicle (AUV) technology. A current trend is to make use of bio-inspired propulsor to increase the overall system efficiency and performance, an improvement that has deep implications in the dynamics of the system. The goal of this dissertation is to propose a generic robust control framework specific for bio-inspired autonomous underwater vehicles (BIAUV). These vehicles utilize periodic oscillation of a flexible structural component to generate thrust, a propulsion mechanism that can be tuned to operate under resonance and consequently improve the overall system efficiency. The control parameter should then be selected to keep the system operating in such a condition. Another important aspect is to have a controller design technique that can address the time-varying behaviors, structured uncertainties and system nonlinearities. To address these needs a robust, model-based, nonlinear controller design technique is presented, called digital sliding mode controller (DSMC), which also takes into account the discrete implementation of these laws using microcontrollers. The control law is implemented in the control of a jellyfish-inspired autonomous underwater vehicle. / Ph. D.

Au cours de ces dernières années, les réseaux de capteurs sans fils ont connu un intérêt croissant à la fois au sein de la communauté scientifique et industrielle en raison du large potentiel en terme d’applications offertes. Toutefois, les capteurs sont conçus avec d’extrêmes contraintes en ressources, en particulier la limitation de l’énergie. Il est donc nécessaire de concevoir des protocoles efficaces, évolutifs et moins consommateur d’énergie afin de prolonger la durée de vie de ces réseaux. Le clustering est une approche très populaire, utilisée pour l’optimisation de la consommation d’énergie des capteurs. Cette technique permet d’influencer fortement la performance globale du réseau. En outre, dans de tels réseaux, le routage génère un nombre assez élevé d’opérations non négligeables qui affectent considérablement la durée de vie du réseau ainsi que le débit offert. Dans cette thèse, nous nous sommes intéressés d’une part aux problèmes de clustering et de routage en utilisant des méthodes d’optimisation inspirées de certaines sociétés biologiques fournissant des modèles puissants qui conduisent à l’établissement d’une intelligence globale en se basant sur des comportements individuels très simples. Nous avons proposé une approche de clustering distribuée basée sur le processus de sélection des sites de nidification chez les colonies d’abeilles. Nous avons formulé le problème de clustering distribuée comme un processus social de prise de décision dans lequel les capteurs agissent d’une manière collective pour choisir des représentants au sein de leurs clusters respectifs. Le protocole proposé assure une distribution de l’équilibrage de charge entre les membres de chaque cluster afin de prolonger la durée de vie du réseau en faisant un compromis entre la consommation d’énergie et la qualité du canal de communication. D’autre part, nous avons proposé un protocole de routage basé sur des clusters en utilisant un algorithme inspiré du phénomène de butinage des abeilles. Nous avons formulé le problème de clustring comme un problème de programmation linéaire alors que le problème du routage est résolu par une fonction de coûts. L’algorithme de clustering permet la construction efficace des clusters en faisant un compromis entre la consommation d’énergie et la qualité du canal communication au sein des clusters tandis que le routage est réalisé de manière distribuée. Les protocoles proposés ont été intensivement expérimentés sur plusieurs topologies dans différents scénarios de réseaux et comparés avec des protocoles bien connus de clustering et routage. Les résultats obtenus démontrent l’efficacité des protocoles proposés. / During the past few years, wireless sensor networks witnessed an increased interest in both the industrial and the scientific community due to the potential wide area of applications. However, sensors’ components are designed with extreme resource constraints, especially the power supply limitation. It is therefore necessary to design low power, scalable and energy efficient protocols in order to extend the lifetime of such networks. Cluster-based sensor networks are the most popular approach for optimizing the energy consumption of sensor nodes, in order to strongly influence the overall performance of the network. In addition, routing involves non negligible operations that considerably affect the network lifetime and the throughput. In this thesis, we addressed the clustering and routing problems by hiring intelligent optimization methods through biologically inspired computing, which provides the most powerful models that enabled a global intelligence through local and simple behaviors. We proposed a distributed clustering approach based on the nest-sites selection process of a honeybee swarm. We formulated the distributed clustering problem as a social decision-making process in which sensors act in a collective manner to choose their cluster heads. To achieve this choice, we proposed a multi- objective cost-based fitness function. In the design of our proposed algorithm, we focused on the distribution of load balancing among each cluster member in order to extend network lifetime by making a tradeoff between the energy consumption and the quality of the communication link among sensors. Then, we proposed a centralized cluster-based routing protocol for wireless sensor networks by using the fast and efficient searching features of the artificial bee colony algorithm. We formulated the clustering as a linear programming problem and the routing problem is solved by proposing a cost-based function. We designed a multi-objective fitness function that uses the weighted sum approach, in the assignment of sensors to a cluster. The clustering algorithm allows the efficient building of clusters by making a tradeoff between the energy consumption and the quality of the communication link within clusters while the routing is realized in a distributed manner. The proposed protocols have been intensively experimented with a number of topologies in various network scenarios and the results are compared with the well-known cluster-based routing protocols. The results demonstrated the effectiveness of the proposed protocols.

<div>Insects and hummingbirds exhibit extraordinary flight capabilities and can simultaneously master seemingly conflicting goals: stable hovering and aggressive maneuvering, unmatched by small-scale man-made vehicles. Given a sudden looming visual stimulus at hover, a hummingbird initiates a fast backward translation coupled with a 180-degree yaw turn, which is followed by instant posture stabilization in just under 10 wingbeats. Considering the wingbeat frequency of 40Hz, this aggressive maneuver is accomplished in just 0.2 seconds. Flapping Wing Micro Air Vehicles (FWMAVs) hold great promise for closing this performance gap given its agility. However, the design and control of such systems remain challenging due to various constraints.</div><div><br></div><div>First, the design, optimization and system integration of a high performance at-scale biologically inspired tail-less hummingbird robot is presented. Designing such an FWMAV is a challenging task under the constraints of size, weight, power, and actuation limitations. It is even more challenging to design such a vehicle with independently controlled wings equipped with a total of only two actuators and be able to achieve animal-like flight performance. The detailed systematic solution for the design is presented, including system modeling and analysis of the wing-actuation system, body dynamics, and control and sensing requirements. Optimization is conducted to search for the optimal system parameters, and a hummingbird robot is built and validated experimentally.</div><div><br></div><div>An open-source high fidelity dynamic simulation for FWMAVs is developed to serve as a testbed for the onboard sensing and flight control algorithm, as well as design, and optimization of FWMAVs. For simulation validation, the hummingbird robot was recreated in the simulation. System identification was performed to obtain the dynamics parameters. The force generation, open-loop and closed-loop dynamic response between simulated and experimental flights were compared and validated. The unsteady aerodynamics and the highly nonlinear flight dynamics present challenging control problems for conventional and learning control algorithms such as Reinforcement Learning.</div><div><br></div><div>For robust transient and steady-state flight performance, a robust adaptive controller is developed to achieve stable hovering and fast maneuvering. The model-based nonlinear controller can stabilize the system and adapt to system parameter changes such as wear and tear, thermo effect on the actuator or strong disturbance such as ground effect. The controller is tuned in simulation and experimentally verified by hovering, point-to-point fast traversing, and following by rapid figure-of-eight trajectory. The experimental result demonstrates the state-of-the-art performance of the FWMAV in stationary hovering and fast trajectory tracking tasks, with minimum transient and steady-state error.</div><div><br></div><div>To achieve animal level maneuvering performance, especially the hummingbirds' near-maximal performance during rapid escape maneuvers, we developed a hybrid flight control strategy for aggressive maneuvers. The proposed hybrid control policy combines model-based nonlinear control with model-free reinforcement learning. The model-based nonlinear control stabilizes the system's closed-loop dynamics under disturbance and parameter variation. With the stabilized system, a model-free reinforcement learning policy trained in simulation can be optimized to achieve the desirable fast movement by temporarily "destabilizing" the system during flight. Two test cases were demonstrated to show the effectiveness of the hybrid control method: 1)a rapid escape maneuver observed in real hummingbird, 2) a drift-free fast 360-degree body flip. Direct simulation-to-real transfers are achieved, demonstrating the hummingbird-like fast evasive maneuvers on the at-scale hummingbird robot.</div>

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This thesis presents the design and construction of a micro-scale, air powered, impact testing device for use in Virginia Tech's Biological and Bio-inspired Materials Laboratory. A brief overview of current projectile impact testers is presented along with motivation for the fabrication of a new testing system capable of firing a projectile with a maximum diameter of 0.5 mm at velocities ranging from 20 to 50 m/s. Initial design calculations and analysis were performed to optimize barrel length, projectile size, and air pressure for desired velocity ranges. Computer aided design was then utilized to create a digital model of the entire system before production began on the device. Within the scope of this project was the development of a large-scale projectile impact tester as a proof of concept of the system's design that would later be utilized by other researchers as well as the micro-scale tester which carried over the lessons learned and design improvements from the larger device. The culmination of the project was the testing of biological samples (sea urchin spine cross sections) to prove the viability of the device and highlight its research niche. Future use cases and design improvements of the small-scale impact tester were also investigated as part of this thesis work. / Master of Science / This thesis encompasses the design and fabrication of both a large-scale projectile impact tester as a proof of concept design as well as a micro-scale version that carries over many of the design elements of the large version but is designed to fire projectiles for small scale biological material tests. Also included as part of this thesis is a breakdown of the various impact testers currently available within research to show why this project was necessary. The project culminated in simple impact studies of sea urchin spines to showcase the capabilities of the impact tester in its current form as well as to outline some of the expanded properties that could be determined with simple experimental setup changes. From this impact, study it was determined that sea urchin spines are a leading candidate in the formulation of bio-inspired impact resistant ceramic foams as they have excellent energy absorption properties during dynamic loading. The calcite foam structure of the sea urchin spines proved to have better impact absorption capabilities in comparison to many current engineering materials used for impact resistance. The final part of this thesis is a brief overview of the planned future use cases of the device.

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Bio-inspired swarming behaviors provide an effective decentralized way of coordinating robot teams. However, as robot swarms increase in size, bandwidth and time constraints limit the number of agents a human can communicate with and control. To facilitate scalable human interaction with large robot swarms it is desirable to monitor and influence the collective behavior of the entire swarm through limited interactions with a small subset of agents. However, it is also desirable to avoid situations where a small number of agent failures can adversely affect the collective behavior of the swarm. We present a bio-inspired model of swarming that exhibits distinct collective behaviors and affords limited human interaction to estimate and influence these collective behaviors. Using a simple naive Bayes classifier, we show that the global behavior of a swarm can be detected with high accuracy by sampling local information from a small number of agents. We also show that adding a bio-inspired form of quorum sensing to a swarm increases the scalability of human-swarm interactions and also provides an adjustable threshold on the swarm's vulnerability to agent failures.

In this thesis we propose methods and metrics for human interaction with bio-inspired robot teams. We refine the concept of a stakeholder and demonstrate how a human can use stakeholders to lead a swarm as well as switch the swarm between different collective behaviors. We extend the human interaction metrics of interaction time and interaction effort presented in [1] to swarm systems and introduce the concept of interaction effort. These metrics allow us to understand how well the system performs under human influence. We employ systems theory to estimate these metrics, which is useful because this can be done without performing user studies.

Nonlinear constrained optimal trajectory control is an important and fundamental area of research that continues to advance in numerous fields. Many attempts have been made to present new methods that can solve for optimal trajectories more efficiently or to improve the overall performance of existing techniques. This research presents a recently developed bio-inspired method called the Virtual Motion Camouflage (VMC) method that offers a means of quickly finding, within a defined but varying search space, the optimal trajectory that is equal or close to the optimal solution. The research starts with the polynomial-based VMC method, which works within a search space that is defined by a selected and fixed polynomial type virtual prey motion. Next will be presented a means of improving the solution’s optimality by using a sequential based form of VMC, where the search space is adjusted by adjusting the polynomial prey trajectory after a solution is obtained. After the search space is adjusted, an optimization is performed in the new search space to find a solution closer to the global space optimal solution, and further adjustments are made as desired. Finally, a B-spline augmented VMC method is presented, in which a B-spline curve represents the prey motion and will allow the search space to be optimized together with the solution trajectory. It is shown that (1) the polynomial based VMC method will significantly reduce the overall problem dimension, which in practice will significantly reduce the computational cost associated with solving nonlinear constrained optimal trajectory problems; (2) the sequential VMC method will improve the solution optimality by sequentially refining certain parameters, such as the prey motion; and (3) the B-spline augmented VMC method will improve the solution iv optimality without sacrificing the CPU time much as compared with the polynomial based approach. Several simulation scenarios, including the Breakwell problem, the phantom track problem, the minimum-time mobile robot obstacle avoidance problem, and the Snell’s river problem are simulated to demonstrate the capabilities of the various forms of the VMC algorithm. The capabilities of the B-spline augmented VMC method are also shown in a hardware demonstration using a mobile robot obstacle avoidance testbed.