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.

With the recent trend for systems to be more and more autonomous, there is a growing need for cooperative trajectory planning. Applications that can be considered as cooperative systems such as surveying, formation flight, and traffic control need a method that can rapidly produce trajectories while considering all of the constraints on the system. Currently most of the existing methods to handle cooperative control are based around either simple dynamics and/or on the assumption that all vehicles have homogeneous properties. In reality, typical autonomous systems will have heterogeneous, nonlinear dynamics while also being subject to extreme constraints on certain state and control variables. In this thesis, a new approach to the cooperative control problem is presented based on the bio-inspired motion strategy known as local pursuit. In this framework, decision making about the group trajectory and formation are handled at a cooperative level while individual trajectory planning is considered in a local sense. An example is presented for a case of an autonomous farming system (e.g. scouting) utilizing nonlinear vehicles to cooperatively accomplish various farming task with minimal energy consumption or minimum time. The decision making and trajectory generation is handled very quickly while being able to consider changing environments laden with obstacles

Trajectory planning is important in many applications involving unmanned aerial vehicles, underwater vehicles, spacecraft, and industrial manipulators. It is still a challenging task to rapidly find an optimal trajectory while taking into account dynamic and environmental constraints. In this dissertation, a unified, varying manifold based optimal trajectory planning method inspired by several predator-prey relationships is investigated to tackle this challenging problem. Biological species, such as hoverflies, ants, and bats, have developed many efficient hunting strategies. It is hypothesized that these types of predators only move along paths in a carefully selected manifold based on the prey’s motion in some of their hunting activities. Inspired by these studies, the predator-prey relationships are organized into a unified form and incorporated into the trajectory optimization formulation, which can reduce the computational cost in solving nonlinear constrained optimal trajectory planning problems. Specifically, three motion strategies are studied in this dissertation: motion camouflage, constant absolute target direction, and local pursuit. Necessary conditions based on the speed and obstacle avoidance constraints are derived. Strategies to tune initial guesses are proposed based on these necessary conditions to enhance the convergence rate and reduce the computational cost of the motion camouflage inspired strategy. The following simulations have been conducted to show the advantages of the proposed methods: a supersonic aircraft minimum-time-to-climb problem, a ground robot obstacle avoidance problem, and a micro air vehicle minimum time trajectory problem. The results show that the proposed methods can find the optimal solution with higher success rate and faster iv convergent speed as compared with some other popular methods. Among these three motion strategies, the method based on the local pursuit strategy has a relatively higher success rate when compared to the other two. In addition, the optimal trajectory planning method is embedded into a receding horizon framework with unknown parameters updated in each planning horizon using an Extended Kalman Filter

Geckos and other insects have fascinated scientists and casual observers with their ability to effortlessly climb up walls and across ceilings. This capability has inspired high capacity, easy release synthetic adhesives, which have focused on mimicking the fibrillar features found on the foot pads of these climbing organisms. However, without a fundamental framework that connects biological and synthetic adhesives from nanoscopic to macroscopic features, synthetic mimics have failed to perform favorably at large contact areas. In this thesis, we present a scaling approach which leads to an understanding of reversible adhesion in both synthetic and biological systems over multiple length scales. We identify, under various loading scenarios, how geometry and material properties control adhesion, and we apply this understanding to the development of high capacity, easy release synthetic adhesive materials at macroscopic size scales. Starting from basic fracture mechanics, our generalized scaling theory reveals that the ratio of contact area to compliance in the loading direction, A/C, is the governing scaling parameter for the force capacity of reversible adhesive interfaces. This scaling theory is verified experimentally in both synthetic and biological adhesive systems, over many orders of magnitude in size and adhesive force capacity (Chapter 2). This understanding is applied to the development of gecko-like adhesive pads, consisting of stiff, draping fabrics incorporated with thin elastomeric layers, which at macroscopic sizes (contact areas of 100 cm2) exhibit force capacities on the order of 3000 N. Significantly, this adhesive pad is non-patterned and completely smooth, demonstrating that fibrillar features are not necessary to achieve high capacity, easy release adhesion at macroscopic sizes and emphasizing the importance of subsurface anatomy in biological adhesive systems (Chapter 2, Chapter 3). We further extend the utility of the scaling theory under shear (Chapter 4) and normal (Chapter 5) loading conditions and develop simple expressions for patterned and non-patterned interfaces which describe experimental force capacity data as a function of geometric parameters such as contact area, aspect ratio, and contact radius. These studies provide guidance for the precise control of adhesion with enables the development of a simple transfer printing technique controlled by geometric confinement (Chapter 6). Force capacity data from each chapter, along with various literature data are collapsed onto a master plot described by the A/C scaling parameter, with agreement over 15 orders of magnitude in adhesive force capacity for synthetic and biological adhesives, demonstrating the generality and robustness of the scaling theory (Chapter 7).

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The growing need for a trustworthy Future Internet demands evolutionary approaches unfettered by legacy constrains and concepts. The networking community is calling for new network architectural proposals that address the deficiencies identified in present network realizations, acknowledge the need for a trustworthy IT infrastructure, and satisfy the society's emerging and future requirements. Proposed architectures need to be founded on well-articulated design principles, account for network operational and management complexities, embrace technology and application heterogeneity, regulate network-inherent emergent behavior, and overcome shortcomings attributed to present network realizations. This dissertation presents our proposed clean-slate Concern-Oriented Reference Model (CORM) for architecting future computer networks. CORM stands as a guiding framework from which network architectures can be derived according to specific functional, contextual, and operational requirements or constraints. CORM represents a pioneering attempt within the network realm, and to our knowledge, CORM is the first reference model that is bio-inspired and derived in accordance with the Function-Behavior-Structure (FBS) engineering framework. CORM conceives a computer network as a software-dependent complex system whose design needs to be attempted in a concern-oriented bottom-up approach along two main dimensions: a vertical dimension addressing structure and configuration of network building blocks; and a horizontal dimension addressing communication and interactions among the previously formulated building blocks. For each network dimension, CORM factors the design space into function, structure, and behavior, applying to each the principle of separation of concerns for further systematic decomposition. In CORM, the network-building block is referred to as the Network Cell (NC), which represents CORM's first basic abstraction. An NC's structure and inherent behavior are bio-inspired, imitating a bacterium cell in a bacteria colony, thus it is capable of adaptation, self-organization and evolution. An NC's functional operation is defined by CORM second basic abstraction; the ACRF framework. The ACRF framework is a conceptual framework for network-concerns derived according to our interpretation of computer network requirement specifications. CORM networks are recursively synthesized in a bottom-up fashion out of CORM NCs. CORM addresses the multi-dimensionality of computer networks by modeling the network structure and behavior using a network structural template (NST), and an information flow model (IFM), respectively. Being developed according to a complex system paradigm, CORM refutes the long endorsed concept of layering, intrinsically accounts for emergent behavior, and ensures system integrity and stability. As a reference model, CORM is more typical of conventional engineering. Therefore it was validated using the FBS engineering framework. However, the behavior to be realized in CORM-based networks was substantiated and evaluated by deriving CellNet, our proposed CORM-based network architecture. CellNet-compliant protocols' behavioral adaptation and modification were illustrated and evaluated through simulation. CORM will have a profound impact on the operation and behavior of computer networks composing the Internet. By introducing awareness adaptability and evolvability as network intrinsic features, CORM-based Internet will proactively respond to changes in operational contexts, underlying technologies, and end user requirements. A major direction in CORM future work would be to detail the IFM component. / Ph. D.

Active sensors, such as radar, lidar and sonar, emit a signal into the environment and gather information from its reflection. In contrast, passive sensors such as cameras and microphones rely on the signals emitted from the environment. In the current application of active sensors in multi-agent autonomous systems, agents only rely on their own active sensing and filter out any information available passively. However, fusing passive and active sensing information may improve the accuracy of the agents. Also, there is evidence that bats who use biosonar eavesdrop on a conspecific's echolocation sound, which shows a successful example of implementing active and passive sonar sensor fusion in nature. We studied the effect of such information fusion in the framework of two problems: the collective behavior in a multi-agent system using the Vicsek model and the canonical robotics problem of Simultaneous Localization And Mapping (SLAM). Collective behavior refers to emergence of a complex behavior in a group of individuals through local interaction. The Vicsek model is a well-established flocking model based on alignment of individuals with their neighbors in the presence of noise. We studied the aligned motion in a group in which the agents employ both active and passive sensing. Our study shows that the group behavior is less sensitive to measurement accuracy compared to modeling precision. Therefore, using measurement values of the noisier passive sonar can be beneficial. In addition, the group alignment is improved when the passive measurements are not dramatically noisier than active measurements. In the SLAM problem, a robot scans an unknown environment building a map and simultaneously localizing itself within that map. We studied a landmark-based SLAM problem in which the robot uses active and passive sensing strategies. The information provided passively can improve the accuracy of the active sensing measurements and compensate for its blind spot. We developed an estimation algorithm using Extended Kalman Filter and employed Monte Carlo simulation to find a parameter region in which fusing passive and active sonar information improves the performance of the robot. Our analysis shows this region is aligned within the common range of active sonar parameters. / Doctor of Philosophy / Group behavior is a fascinating phenomenon in animal groups such as bird flocks, fish schools, bee colonies and fireflies. For instance, many species of fireflies synchronize their flashing when they bio-luminesce. This synchronization pattern is a group behavior created as a result of local interaction formed by sensing individuals in the group. The research question for this dissertation is inspired by comes from group behavior in bats. Bats use echolocation to perceive the environment. They make a sound and listen to the echo of the sound coming back from objects and by analyzing the echo, they can get information about their surroundings. It has been observed that bats may also use the echo of other bats' sound to perceive their environment. In other words they use two different sensors, one is called active sonar since they actively make the sound and listen to its echoes, and the other one is called passive sonar since they just passively listen to the sound generated by other bats. If this information is useful, can we exploit that in design of engineered systems? We investigated these questions using numerical simulation to solve two test bed problems. The first problem is based on a mathematical flocking model in which the individuals in the group align through local interaction. We found out that eavesdropping improves the alignment of the group within a range of parameters in the model which are relevant to the sensing capabilities of the sonar sensors. The other problem is a canonical robotics problem known as the simultaneous localization and mapping (SLAM). In this problem, a robot searches an unknown environment and creates a map of the environment (mapping) and reports the path it takes within the map (localization). We found out that when the robot uses both passive and active sonar, depending on the accuracy of the two sensing approaches, it can improve the accuracy of both the generated map and the robot's path.

The goal of this research is developing a computational framework to study mechanical fatigue and fracture at different length scales for a broad range of materials. The developed multiscale framework is utilized to study the details of fracture and fatigue for the rolling contact in rails, additively manufactured alloys, and bio-inspired hierarchical materials. Rolling contact fatigue (RCF) is a major source of failure and a dominant cause of maintenance and replacements in many railways around the world. The highly-localized stress in a relatively small contact area at the wheel-rail interface promotes micro-crack initiation and propagation near the surface of the rail. 2D and 3D microstructural-based computational frameworks are developed for studying the rolling contact fatigue in rail materials. The method can predict RCF life and simulate crack initiation sites under various conditions. The results obtained from studying RCF behavior in different conditions will help better maintenance of the railways and increase the safety of trains. The developed framework is employed to study the fracture and fatigue behavior in 3D printed metallic alloys fabricated by selective laser melting (SLM) method. SLM method as a part of metal additive manufacturing (AM) technologies is revolutionizing the manufacturing sector and is being utilized across a diverse array of industries, including biomedical, automotive, aerospace, energy, consumer goods, and many others. Since experiments on 3D printed alloys are considerably time-consuming and expensive, computational analysis is a proper alternative to reduce cost and time. In this research, a computational framework is developed to study fracture and fatigue in different scales in 3D printed alloys fabricated by the SLM method. Our method for studying the fatigue at the microstructural level of 3D printed alloys is pioneering with no similar work being available in the literature. Our studies can be used as a first step toward establishing comprehensive numerical frameworks to investigate fracture and fatigue behavior of 3D metallic devices with complex geometries, fabricated by 3D printing. Composite materials are fabricated by combining the attractive mechanical properties of materials into one system. A combination of materials with different mechanical properties, size, geometry, and order of different phases can lead to fabricating a new material with a wide range of properties. A fundamental problem in engineering is how to find the design that exhibits the best combination of these properties. Biological composites like bone, nacre, and teeth attracted much attention among the researchers. These materials are constructed from simple building blocks and show an uncommon combination of high strength and toughness. By inspiring from simple building blocks in bio-inspired materials, we have simulated fracture behavior of a pre-designed composite material consisting of soft and stiff building blocks. The results show a better performance of bio-inspired composites compared to their building blocks. Furthermore, an optimization methodology is implemented into the designing the bio-inspired composites for the first time, which enables us to perform the bio-inspired material design with the target of finding the most efficient geometries that can resist defects in their structure. This study can be used as an effective reference for creating damage-tolerant structures with improved mechanical behavior. / Doctor of Philosophy / The goal of this research is developing a multiscale framework to study the details of fracture and fatigue for the rolling contact in rails, additively manufactured alloys, and bio-inspired hierarchical materials. Rolling contact fatigue (RCF) is a major source of failure and a dominant cause of maintenance and replacements in many railways around the world. Different computational models are developed for studying rolling contact fatigue in rail materials. The method can predict RCF life and simulate crack initiation sites under various conditions and the results will help better maintenance of the railways and increase the safety of trains. The developed model is employed to study the fracture and fatigue behavior in 3D printed metals created by the selective laser melting (SLM) method. SLM method as a part of metal additive manufacturing (AM) technologies is revolutionizing industries including biomedical, automotive, aerospace, energy, and many others. Since experiments on 3D printed metals are considerably time-consuming and expensive, computational analysis is a proper alternative to reduce cost and time. Our method for studying the fatigue at the microstructural level of 3D printed alloys can help to create more fatigue and fracture resistant materials. In the last section, we have studied fracture behavior in bio-inspired materials. A fundamental problem in engineering is how to find the design that exhibits the best combination of mechanical properties. Biological materials like bone, nacre, and teeth are constructed from simple building blocks and show a surprising combination of high strength and toughness. By inspiring from these materials, we have simulated fracture behavior of a pre-designed composite material consisting of soft and stiff building blocks. The results show a better performance of bio-inspired structure compared to its building blocks. Furthermore, an optimization method is implemented into the designing the bio-inspired structures for the first time, which enables us to perform the bio-inspired material design with the target of finding the most efficient geometries that can resist defects in their structure.

Yes / An efficient multi-object evolutionary algorithms are proposed for optimizing frequency characteristics of antennas based on an interfacing created by Matlab environment. This interface makes a link with CST Microwave studio where the electromagnetic investigation of antenna is realized. Very small, compact printed monopole antenna is optimized for ultra- wideband (UWB) applications. Two objective functions are introduced; the first function intends to increase the impedance bandwidth, and second function to tune the antenna to resonate at a particular frequency. The two functions operate in the range of 3.2 to 10.6 GHz and depend on the level of return loss. The computed results provide a set of proper design for UWB system in which the bandwidth achieved is 7.5GHz at the resonance frequency 4.48GHz, including relatively stable gain and radiation patterns across the operating band.

Over the last decade, rapidly growing world energy consumption is leading to supply difficulties, exhaustion of fossil energy resources, and global environmental deterioration. More than one-third of energy expenditure is attributable to buildings. Urbanization is intensifying these trends with tighter spatial interrelationships among buildings. This is escalating building energy consumption due to the mutual impact of buildings on each other and, as a result, exacerbating Urban Heat Island (UHI) effects. I sought solutions to this significant engineering issue from nature, and discovered a similar heat island effect in flowers, namely the micro-greenhouse effect. However, a special cooling effect has been observed in a peculiar temperate flower, Galanthus nivalis, which generates cooler intrafloral temperatures. In this research, I studied the special retro-reflectance of the flower petals, which has been suggested as a possible contributor to this cooling effect, and implemented a bio-inspired retro-reflective pattern for building envelopes. I conducted cross-regional energy simulation of building networks in a dynamic simulation environment in order to examine its thermal-energy impact. I found that building surface temperatures dropped considerably when neighboring buildings were retrofitted with my bio-inspired retro-reflective facade. I concluded that my bio-inspired retro-reflective pattern for building envelopes; (1) lessens the reflected heat of solar radiation in spatially-proximal buildings leading to reduced UHI, and (2) reduces the energy required for cooling and, therefore, energy consumption. The research has further implications and contributions on building design, urban planning, development of retro-reflective technology, and environmental conservation. / Master of Science