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Předložená disertační práce řeší komplexní mechatronický návrh autonomního termoelektrického zdroje energie pro letecké aplikace. Na základě dostupných zdrojů a literatury práce popisuje současný stav problematiky. V práci jsou prezentovány simulační modely MEMS termoelektrických článků, které jsou ověřeny experimentálním testováním a hodnotami dostupnými od výrobce. Na základě metodiky model-besed design byly navrženy a vyrobeny tři demonstrátory. Jejich vlastnosti byly testovány v reálných podmínkách na letecké pohonné jednotce.

<p dir="ltr">This research project aims to develop a robotic platform capable of drilling horizontal laterals from existing wellbores, offering data-guided steering and control features using information captured by sensors. The project provides an opportunity to expand the application of downhole drilling robots toward semi-autonomous operations in existing fields, especially those with declining production. Mature fields represent a global resource, and even modest hydrocarbon reserves additions are substantial to keep up the energy demand, having positive economic and environmental impacts. Available lateral drilling techniques do not fit the constraints offered by the challenge; moreover, they are not cost-effective.</p><p dir="ltr">The project will be organized into three phases to accomplish this developmental study. First, design criteria and key performance aspects will be identified and established to design a self-propelled robotic prototype capable of drilling lateral sections from an existing wellbore with an internal diameter of 4” to 6”. The creation of lateral sections can potentially add hydrocarbon reserves taking advantage of an already-drilled vertical section of a mature field. Second, the design of the prototype will take place, along with the design of a sub-surface communication and control system. The third phase is manufacturing and subsystems integration, finishing with a pilot test in a controlled environment. Based on the pilot testing results, design optimization will occur. Finally, a field version will be designed, and IP (Intellectual Property) disclosures and plans for commercialization will be identified and addressed.</p>

<p dir="ltr">The emergence of Autonomous Mobile Robots (AMR) signifies a pivotal shift in vehicle-based material handling systems, demonstrating their effectiveness across a broad spectrum of applications. Advancing beyond the traditional Automated Guided Vehicles (AGV), AMRs offer unprecedented flexibility in movement, liberated from electromagnetic guidance constraints. Their decentralized control architecture not only enables remarkable scalability but also fortifies system resilience through advanced conflict resolution mechanisms. Nevertheless, transitioning from AGV to AMR presents intricate challenges, chiefly due to the expanded complexity in path planning and task selection, compounded by the heightened potential for conflicts from their dynamic interaction capabilities. This dissertation confronts these challenges by fully leveraging the technological advancements of AMRs. A kinematic-enabled agent-based simulator was developed to replicate AMR system behavior, enabling detailed analysis of fleet dynamics and interactions within AMR intralogistics systems and their environments. Additionally, a comprehensive fleet management protocol was formulated to enhance the throughput of AMR-based intralogistics systems from an integrated perspective. A pivotal discovery of this research is the inadequacy of existing path planning protocols to provide reliable plans throughout their execution, leading to task allocation decisions based on inaccurate plan information and resulting in false optimality. In response, a novel machine learning enhanced probabilistic Multi-Robot Path Planning (MRPP) protocol was introduced to ensure the generation of dependable path plans, laying a solid foundation for task allocation decisions. The contributions of this dissertation, including the kinematic-enabled simulator, the fleet management protocol, and the MRPP protocol, are intended to pave the way for practical enhancements in autonomous vehicle-based material handling systems, fostering the development of solutions that are both innovative and applicable in industrial practices.<br></p>

<p dir="ltr">Robotic software design and implementation have traditionally relied on human engineers to fine-tune parameters, optimize hardware utilization, and mitigate unprecedented situations. As we face more demanding and complex applications, such as distributed robotic fleets and autonomous driving, explicit fine-tuning of autonomous systems yields diminishing returns. To make autonomous systems smarter, a design-time and run-time framework is required to extract constraints from high-level human decisions, and self-adapt on-the-fly to maintain desired specifications. Specifically, for controllers that govern cyber-physical interactions, making them self-adaptive involves two challenges. Firstly, controller design methods have historically neglected computing hardware constraints that realize real-time execution. Hence, intensive manual tuning is required to materialize a controller prototype with balanced control performance and computing resource consumption. Secondly, precisely modeling the physical system dynamics at edge cases is difficult and costly. However, with modeling discrepancies, controllers fine-tuned at design time may fail at run time, causing safety concerns. While humans are inherently adept at reacting and getting used to unknown system dynamics, how to transfer this knowledge to robots is still unresolved.</p><p dir="ltr">To address the two challenges, we propose a designer-augmenting framework for self-adaptive control systems. Our framework includes a resource/performance co-design tool and a model-free controller self-adaptation method for real-time control systems. Our resource/performance co-design tool automatically exploits the Pareto front of controllers, between real-time computing resource utilization and achievable control performance. The co-design tool simplifies the iterative partitioning and verification of controller performance and distributed resource budget, enabling human engineers to directly interface with high-level design decisions between quality and cost. Our controller self-adaptation method extracts objectives and tolerances from human demonstrations and applies them to real-time controller switching, allowing human experts to design fault mitigation behaviors directly through coaching. The objective extraction and real-time adaptation do not rely on prior knowledge of the plant, making them inherently robust against mismatch between the design reference model and the physical system.</p><p dir="ltr">Only with the prerequisite of real-time schedulability under Worst-Case Execution Time (WCET), will the digital controller deliver the designed dynamics. To determine the real-time schedulability of controllers during the design-time iteration and run-time self-adaptation, we propose a novel estimate of WCET based on the Mixed Weibull distribution of profiling statistics and a linear composition model. Our hybrid approach applies to design-time estimation of arbitrary-scaled controllers, yielding results as accurate as a state-of-the-art method while being more robust under small profiling sample sizes. Finally, we propose a resource consolidator that accounts for real-time schedulable bounds to utilize available computing resources while preventing deadline misses efficiently. Our consolidator, formulated as a vector packing problem, exploits different parallelization techniques on a CPU/FPGA hybrid architecture to obtain the most compact allocation plan for a given controller complexity and throughput. </p><p dir="ltr">By jointly considering all four aspects, our framework automates the co-optimization of controller performance and computing hardware requirements throughout the life cycle of a control system. As a result, the engineering time required to design and deploy a controller is significantly reduced, while the adaptivity of human engineers is extended to fault mitigation at run-time.</p>

The complex co-evolutionary systems approach (CCeSA) provides a well-suited framework for analysing agricultural systems, serving as a bridge between biophysical and socioeconomic sciences, allowing for the explanation of phenomena, and for the use of metaphors for thinking and action. By studying agricultural systems as self-generated, hierarchical, complex co-evolutionary farming systems (CCeFSs), one can investigate the interconnections between the elements that constitute CCeFSs, along with the relationships between CCeFSs and other systems, as a fundamental step to understanding sustainability as an emergent property of the system. CCeFSs are defined as human activity systems emerging from the purposes, gestalt, mental models, history and weltanschauung of the farm manager, and from his dynamic co-evolution with the environment while managing the resources at his hand to achieve his own multiple, conflicting, dynamic, semi-structured and constrained purposes. A sustainable CCeFS is described as one that exhibits both enough fitness to achieve its multiple, dynamic, constrained, semi-structured, and often incommensurable and conflicting purposes while performing above threshold values for failure, and enough flexibility to dynamically co-evolve with its changing biophysical and socioeconomic environment for a given future period. Fitness and flexibility are essential features of sustainable CCeFSs because they describe the systems' dynamic capacity to explore and exploit its dynamic phase space while co-evolving with it. This implies that a sustainable CCeFS is conceived as a set of dynamic, co-evolutionary processes, contrasting with the standard view of sustainability as an equilibrium or steady state. Achieving sustainable CCeFSs is a semi-structured, constrained, multi-objective, and dynamic optimisation management problem with an intractable search phase space, that can be solved within the CCeSA with the help of a multi-objective co-evolutionary optimisation tool. Carnico-ICSPEA2, a Co-Evolutionary Navigator (CoEvoNav) used as a CCeSA's tool for harnessing the complexity of the CCeFS of interest and its environment towards sustainability, is introduced. The software was designed by its end-user - the farm manager and author of this thesis - as an aid for the analysis and optimisation of the "San Francisco" ranch, a beef cattle enterprise running on temperate pastures and fodder crops in the central plateau of Mexico. By combining a non-linear simulator and a multi-objective evolutionary algorithm with a deterministic and stochastic framework, the CoEvoNav imitates the co-evolutionary pattern of the CCeFS of interest. As such, the software was used by the farm manager to "navigate" through his CCeFS's co-evolutionary phase space towards achieving sustainability at farm level. The ultimate goal was to enhance the farm manager's decision-making process and co-evolutionary skills, through an increased understanding of his system, the co-evolutionary process between his mental models, the CCeFS, and the CoEvoNav, and the continuous discovery of new, improved sets of heuristics. An overview of the methodological, theoretical and philosophical framework of the thesis is introduced. Also, a survey of the Mexican economy, its agricultural sector, and a statistical review of the Mexican beef industry are presented. Concepts such as modern agriculture, the reductionist approach to agricultural research, models, the system's environment, sustainability, conventional and sustainable agriculture, complexity, evolution, simulators, and multi-objective optimization tools are extensively reviewed. Issues concerning the impossibility of predicting the long-term, detailed future behaviour of CCeFSs, along with the use of simulators as decision support tools in the quest for sustainable CCeFSs, are discussed. The rationale behind the simulator used for this study, along with that of the multi-objective evolutionary tools used as the makeup of Carnico-ICSPEA2, are explained. A description of the "San Francisco" ranch, its key on-farm sustainability indicators in the form of objective functions, constraints, and decision variables, and the semi-structured, multi-objective, dynamic, constrained management problem posed by the farm manager's planned introduction of a herd of bulls for fattening as a way to increase the fitness of his CCeFS via a better management of the system's feed surpluses and the acquisition of a new pick-up truck are described as a case study. The tested scenario and the experimental design for the simulations are presented as well. Results from using the CoEvoNav as the farm manager's extended phenotype to solve his multi-objective optimisation problem are described, along with the implications for the management and sustainability of the CCeFS. Finally, the approach and tools developed are evaluated, and the progress made in relation to methodological, theoretical, philosophical and conceptual notions is reviewed along with some future topics for research.

<p dir="ltr">Over 150,000 people undergo lower-extremity amputations yearly in the United States. In recent years, multiple efforts have been made to improve the human-robot interaction between amputees and active lower limb prostheses. Using lightweight wearable technologies has been a viable solution to implement algorithms that can estimate gait kinematics and prosthesis users’ intent. Examples of wearable technologies include inertial measurement units, strain gauges, and electromyography sensors. Kinematic and force data is inputted into an Error-State Kalman filter to estimate the inversion-eversion, external-internal, and dorsiflexion-plantarflexion ankle angle. The filter tracked the ankle angle with an accuracy of 0.7724°, 0.8826°, and 1.3520°, respectively. The gait phase was estimated using a linear regression model based on a shank kinematics ground truth pattern with an average normalized accuracy of 97.79 %. A numerical simulation of a gait emulator in the form of a 3-Revolute-Prismatic-Revolute (3-RPR) manipulator. The gait emulator can test lower limb prostheses independent of human subjects, eliminating many hurdles associated with human subject testing. The manipulator was simulated with two control strategies: a traditional PID and a hybrid PID + Active Force Control controller (AFC). The hybrid PID+AFC provided higher accuracy in tracking the desired end-effector trajectory due to improved disturbance rejection. A low-cost surface electromyography (sEMG) platform was developed to robustly acquire sEMG signals, with an overall component cost of 35.06 US$. The sEMG platform integrates directly into a Micro:bit microcontroller through an expansion board. During testing with human subjects, sEMG Micro:bit platform had a reported average signal-to-noise ratio of 24.7 dB.</p>

<p>One of the most often utilized machinery in fluid power applications is the material-handling machines, which includes telehandlers, forklifts, cranes, and scissor lifts that are used from constructions to mining.<br> Counterbalance valves (CBVs), hydraulic components that protect the system from failures and manage the load under overrunning load conditions due to their distinctive design, are used in material-handling devices to ensure both the operators' and most off-road vehicles' safety. However, they present a significant shortcoming: the over-pressurization of the supply line, which leads to constringent energy consumption. The primary motivation for this work is this drawback. In this work, a CBV-based system with an adjustable pilot has been investigated using a truck-mounted hydraulic crane as a reference machine.</p> <p>By analyzing theoretically and experimentally the behavior of this novel hydraulic system, it is possible to achieve up to 90% of energy-saving than a baseline configuration of a load-holding machine by controlling the opening of the CBV by adjusting the pressure at the pilot stage. After exploring the capabilities of the studied system and the possible control strategies to control opening of the CBV, this work suggests two different solutions to control the system: “Smart CBV” and “Smart System” modes. By properly controlling the signal on the pilot stage of the CBV, "Smart CBV" enables energy savings of up to 80%. On the other hand, the "Smart System" mode can save up to 95% of energy by using the CBV as a meter-out element that successfully regulates the flow to the actuator and, consequently, its velocity. To attain these outstanding results, it is essential to maintain proper system control.</p> <p>Moreover, since safety is one of the priorities of this type of machine, a Condition Monitoring (CM) model is developed to ensure the actual functionalities of the novel proposed system. By identifying faulty conditions and preventing breakdowns before they occur, CM can be utilized to improve the safety of these type of machines. However, training a CM model using experimental data is time-consuming and expensive since it requires abundant data with different extent of machine failures from the field test. The solution suggested in this work is to generate faulty and healthy data for the reference machine using a high-fidelity simulation tool to train a CM model.</p> <p>Particular focus is given to the counterbalance valve (CBV), a crucial element for the hydraulic system of material handling machines, and the linear actuator (hydraulic cylinder). The different types of faults on two elements are modeled with an approach validated using experimental tests. Considering that the simulation model provides comparable outcomes to training on empirical data, the CM model is trained in a single fault condition and multi faults conditions using simulated data. Instead, the CM model is tested using the experimental tests in multiple faulty conditions on the chosen components.</p> <p>Moreover, finding the best CM model for this case study is another goal of this work. As a result, several CM models are investigated: Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). In terms of precision and recall, metrics frequently employed in the CM field to assess the performances of the designed CM model, the results generally indicate more than 90% accuracy.</p>

Structural colours arise from the interaction of visible light with nano-structured materials. The occurrence of such structures in nature has been known for over a century, but it is only in the last few decades that the study of natural photonic structures has fully matured due to the advances in imagining techniques and computational modelling. Even though a plethora of different colour-producing architectures in a variety of species has been investigated, a few significant questions are still open: how do these structures develop in living organisms? Does disorder play a functional role in biological photonics? If so, is it possible to say that the optical response of natural disordered photonics has been optimised under evolutionary pressure? And, finally, can we exploit the well-adapted photonic design principles that we observe in Nature to fabricate functional materials with optimised scattering response? In my thesis I try to answer the questions above: I microscopically investigate $\textit{in vivo}$ the growth of a cuticular multilayer, one of the most common colour-producing strategies in nature, in the green beetles $\textit{Gastrophysa viridula}$ showing how the interplay between different materials varies during the various life stages of the beetles; I further investigate two types of disordered photonic structures and their biological role, the random array of spherical air inclusions in the eggshells of the honeyguide $\textit{Prodotiscus regulus}$, a species under unique evolutionary pressure to produce blue eggs, and the anisotropic chitinous network of fibres in the white beetle $\textit{Cyphochilus}$, the whitest low-refractive index material; finally, inspired by these natural designs, I fabricate and study light transport in biocompatible highly-scattering materials.