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).

No description available.

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

The urgent need for a clean and sustainable power supply for wireless sensor nodes and low-power electronics in various monitoring systems and the Internet of Things has led to an explosion of research in substitute energy technologies. Traditional batteries are still the most widely used power source for these applications currently but have been blamed for chemical pollution, high maintenance cost, bulky volume, and limited energy capacity. Ambient energy in different forms such as vibration, movement, heat, wind, and waves otherwise wasted can be converted into usable electricity using proper transduction mechanisms to power sensors and low-power devices or charge rechargeable batteries. This dissertation focuses on the design, modeling, optimization, prototype, and testing of novel piezoelectric energy harvesters for extracting energy from human walking, bio-inspired bi-stable motion, and torsional vibration as an alternative power supply for wireless monitoring systems. To provide a sustainable power supply for health care monitoring systems, a piezoelectric footwear harvester is developed and embedded inside a shoe heel for scavenging energy from human walking. The harvester comprises of multiple 33-mode piezoelectric stacks within single-stage force amplification frames sandwiched between two heel-shaped aluminum plates taking and reallocating the dynamic force at the heel. The single-stage force amplification frame is designed and optimized to transmit, redirect, and amplify the heel-strike force to the inner piezoelectric stack. An analytical model is developed and validated to predict precisely the electromechanical coupling behavior of the harvester. A symmetric finite element model is established to facilitate the mesh of the transducer unit based on a material equivalent model that simplifies the multilayered piezoelectric stack into a bulk. The symmetric FE model is experimentally validated and used for parametric analysis of the single-stage force amplification frame for a large force amplification factor and power output. The results show that an average power output of 9.3 mW/shoe and a peak power output of 84.8 mW are experimentally achieved at the walking speed of 3.0 mph (4.8 km/h). To further improve the power output, a two-stage force amplification compliant mechanism is designed and incorporated into the footwear energy harvester, which could amplify the dynamic force at the heel twice before applied to the inner piezoelectric stacks. An average power of 34.3 mW and a peak power of 110.2 mW were obtained under the dynamic force with the amplitude of 500 N and frequency of 3 Hz. A comparison study demonstrated that the proposed two-stage piezoelectric harvester has a much larger power output than the state-of-the-art results in the literature. A novel bi-stable piezoelectric energy harvester inspired by the rapid shape transition of the Venus flytrap leaves is proposed, modeled and experimentally tested for the purpose of energy harvesting from broadband frequency vibrations. The harvester consists of a piezoelectric macro fiber composite (MFC) transducer, a tip mass, and two sub-beams with bending and twisting deformations created by in-plane pre-displacement constraints using rigid tip-mass blocks. Different from traditional ways to realize bi-stability using nonlinear magnetic forces or residual stress in laminate composites, the proposed bio-inspired bi-stable piezoelectric energy harvester takes advantage of the mutual self-constraint at the free ends of the two cantilever sub-beams with a pre-displacement. This mutual pre-displacement constraint bi-directionally curves the two sub-beams in two directions inducing higher mechanical potential energy. The nonlinear dynamics of the bio-inspired bi-stable piezoelectric energy harvester is investigated under sweeping frequency and harmonic excitations. The results show that the sub-beams of the harvester experience local vibrations, including broadband frequency components during the snap-through, which is desirable for large power output. An average power output of 0.193 mW for a load resistance of 8.2 kΩ is harvested at the excitation frequency of 10 Hz and amplitude of 4.0 g. Torsional vibration widely exists in mechanical engineering but has not yet been well exploited for energy harvesting to provide a sustainable power supply for structural health monitoring systems. A torsional vibration energy harvesting system comprised of a shaft and a shear mode piezoelectric transducer is developed in this dissertation to look into the feasibility of harvesting energy from oil drilling shaft for powering downhole sensors. A theoretical model of the torsional vibration piezoelectric energy harvester is derived and experimentally verified to be capable of characterizing the electromechanical coupling system and predicting the electrical responses. The position of the piezoelectric transducer on the surface of the shaft is parameterized by two variables that are optimized to maximize the power output. Approximate expressions of the voltage and power are derived by simplifying the theoretical model, which gives predictions in good agreement with analytical solutions. Based on the derived approximate expression, physical interpretations of the implicit relationship between the power output and the position parameters of the piezoelectric transducer are given. / Doctor of Philosophy / Wireless monitoring systems with embedded wireless sensor nodes have been widely applied in human health care, structural health monitoring, home security, environment assessment, and wild animal tracking. One distinctive advantage of wireless monitoring systems is to provide unremitting, wireless monitoring of interesting parameters, and data transmission for timely decision making. However, most of these systems are powered by traditional batteries with finite energy capacity, which need periodic replacement or recharge, resulting in high maintenance costs, interruption of service, and potential environmental pollution. On the other hand, abundant energy in different forms such as solar, wind, heat, and vibrations, diffusely exists in ambient environments surrounding wireless monitoring systems which would be otherwise wasted could be converted into usable electricity by proper energy transduction mechanisms. Energy harvesting, also referred to as energy scavenging and energy conversion, is a technology that uses different energy transduction mechanisms, including electromagnetic, photovoltaic, piezoelectric, electrostatic, triboelectric, and thermoelectric, to convert ambient energy into electricity. Compared with traditional batteries, energy harvesting could provide a continuous and sustainable power supply or directly recharge storage devices like batteries and capacitors without interrupting operation. Among these energy transduction mechanisms, piezoelectric materials have been extensively explored for small-size and low-power generation due to their merits of easy shaping, high energy density, flexible design, and low maintenance cost. Piezoelectric transducers convert mechanical energy induced by dynamic strain into electrical charges through the piezoelectric effect. This dissertation presents novel piezoelectric energy harvesters, including design, modeling, prototyping, and experimental tests for energy harvesting from human walking, broadband bi-stable nonlinear vibrations, and torsional vibrations for powering wireless monitoring systems. A piezoelectric footwear energy harvester is developed and embedded inside a shoe heel for scavenging energy from heel striking during human walking to provide a power supply for wearable sensors embedded in health monitoring systems. The footwear energy harvester consists of multiple piezoelectric stacks, force amplifiers, and two heel-shaped metal plates taking dynamic forces at the heel. The force amplifiers are designed and optimized to redirect and amplify the dynamic force transferred from the heel-shaped plates and then applied to the inner piezoelectric stacks for large power output. An analytical model and a finite model were developed to simulate the electromechanical responses of the harvester. The footwear harvester was tested on a treadmill under different walking speeds to validate the numerical models and evaluate the energy generation performance. An average power output of 9.3 mW/shoe and a peak power output of 84.8 mW are experimentally achieved at the walking speed of 3.0 mph (4.8 km/h). A two-stage force amplifier is designed later to improve the power output further. The dynamic force at the heel is amplified twice by the two-stage force amplifiers before applied to the piezoelectric stacks. An average power output of 34.3 mW and a peak power output of 110.2 mW were obtained from the harvester with the two-stage force amplifiers. A bio-inspired bi-stable piezoelectric energy harvester is designed, prototyped, and tested to harvest energy from broadband vibrations induced by animal motions and fluid flowing for the potential applications of self-powered fish telemetry tags and bird tags. The harvester consists of a piezoelectric macro fiber composite (MFC) transducer, a tip mass, and two sub-beams constrained at the free ends by in-plane pre-displacement, which bends and twists the two sub-beams and consequently creates curvatures in both length and width directions. The bi-direction curvature design makes the cantilever beam have two stable states and one unstable state, which is inspired by the Venus flytrap that could rapidly change its leaves from the open state to the close state to trap agile insects. This rapid shape transition of the Venus flytrap, similar to the vibration of the harvester from one stable state to the other, is accompanied by a large energy release that could be harvested. Detailed design steps and principles are introduced, and a prototype is fabricated to demonstrate and validate the concept. The energy harvesting performance of the harvester is evaluated at different excitation levels. Finally, a piezoelectric energy harvester is developed, analytically modeled, and validated for harvesting energy from the rotation of an oil drilling shaft to seek a continuous power supply for downhole sensors in oil drilling monitoring systems. The position of the piezoelectric transducer on the surface of the shaft is parameterized by two variables that are optimized to obtain the maximum power output. Approximate expressions of voltage and power of the torsional vibration piezoelectric energy harvester are derived from the theoretical model. The implicit relationship between the power output and the two position parameters of the transducer is revealed and physically interpreted based on the approximate power expression. Those findings offer a good reference for the practical design of the torsional vibration energy harvesting system.

This thesis presents the design and development of a novel biomimetic jellyfish robot that features ionic polymer metal composite actuators. The shape and swimming style of this underwater vehicle are based on oblate jellyfish species, which are known for their high locomotive efficiency. Ionic polymer metal composites (IPMC) are used as actuators in order to contract the bell and thus propel the jellyfish robot. This research focuses on translating the evolutionary successes of the natural species into a jellyfish robot that mimics the geometry, the swimming style, and the bell deformation cycle of the natural species. Key advantages of using IPMC actuators over other forms of smart material include their ability to exhibit high strain response due to a low voltage input and their ability to act as artificial muscles in water environment. This research specifically seeks to implement IPMC actuators in a biomimetic design and overcome two main limitations of these actuators: slow response rate and the material low blocking force. The approach presented in this document is based on a combination of two main methods, first by optimizing the performance of the IPMC actuators and second by optimizing the design to fit the properties of the actuators by studying various oblate species. Ionic polymer metal composites consist of a semi-permeable membrane bounded by two conductive, high surface area electrode. The IPMCs are manufactured is several variations using the Direct Assembly Process (DAP), where the electrode architecture is controlled to optimize the strain and stiffness of the actuators. The resulting optimized actuators demonstrate peak to peak strains of 0.8 % in air and 0.7 % in water across a frequency range of 0.1-1.0 Hz and voltage amplitude of 2 V. A study of different oblate species is conducted in order to attain a model system that best fits the properties of the IPMC actuators. The Aequorea victoria is chosen based on its bell morphology and kinematic properties that match the mechanical properties of the IPMC actuators. This medusa is characterized by it low swimming frequency, small bell deformation during the contraction phase, and high Froude efficiency. The bell morphology and kinematics of the Aequorea victoria are studied through the computation of the radius of curvature and thus the strain energy stored in the during the contraction phase. The results demonstrate that the Aequorea victoria stores lower strain energy compared to the other candidate species during the contraction phase. Three consecutive jellyfish robots have been built for this research project. The first generation served as a proof of concept and swam vertically at a speed of 2.2 mm/s and consumed 3.2 W of power. The second generation mimicked the geometry and swimming style of the Aurelia aurita. By tailoring the applied voltage waveform and the flexibility of the bell, the robot swam at an average speed of 1.5 mm/s and consumed 3.5 W of power. The third and final generation mimicked the morphology, swimming behavior, and bell kinematics of the Aequorea victoria. The resulting robot, swam at an average speed of 0.77 mm/s and consumed 0.7 W of power when four actuators are used while it achieved 1.5 mm/s and 1.1 W of power consumption when eight actuators are used. Key parameter including the type of the waveform, the geometry of the bell, and position and size of the IPMC actuators are identified. These parameters can be hit later in order to further optimize the design of an IPMC based jellyfish robot. / Master of Science

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.