Manipulation and mechanics of thin elastic objects

In this thesis, multiple problems concerning the equilibrium and stability properties of thin deformable objects are considered, with particular focus given to the analysis of thin elastic rods. The problems considered can be divided into two related categories: manipulation and mechanics. First, a few results concerning symmetries in geometric optimal control theory are derived, which are later used in the analysis of thin elastic objects. Then the problem of quasi-statically manipulating an elastic rod from an initial configuration into a goal configuration is considered. Based upon an analysis of symmetries, geometric and topological characterizations of the set of all stable equilibrium configurations of an elastic rod are derived. Specifically, under a few regularity assumptions, it is shown that the set of all stable equilibrium configurations without conjugate points of an extensible, shearable, anisotropic, and uniform Cosserat elastic rod subject to conservative body forces is a smooth six-dimensional manifold parameterized by a single global coordinate chart. Furthermore, in the case of an inextensible, unshearable, anisotropic, uniform, and intrinsically straight Kirchhoff elastic rod without body forces, this six-dimensional manifold is shown to be path-connected. In addition to their applications to manipulation, the geometric and topological results described above can be used to answer questions concerning the mechanics of elastic rods and other deformable objects. For an inextensible, unshearable, isotropic, and uniform Kirchhoff elastic rod, it is shown that the closure of the set of all stable equilibria with helical centerlines is star-convex, and this property is used to compute and visualize the boundary between stable and unstable helical rods. Finally, two applications of geometric optimal control theory to the analysis of constitutive equations for thin elastic objects are considered. In the first application, the Pontryagin maximum principle is used to analyze curvature discontinuities observed in inextensible surfaces. In the second application, the Pontryagin maximum principle is used to derive constitutive equations for an elastic rod subject to a local injectivity constraint, and the use of this model for analyzing highly flexible helical springs with contact between neighboring coils is considered

Geometrical and probabilistic methods for determining association models and structures of protein complexes

Protein complexes play vital roles in cellular processes within living organisms. They are formed by interactions between either different proteins (hetero-oligomers) or identical proteins (homo-oligomers). In order to understand the functions of the complexes, it is important to know the manner in which they are assembled from the component subunits and their three dimensional structure. This thesis addresses both of these questions by developing geometrical and probabilistic methods for analyzing data from two complementary experiment types: Small Angle Scattering (SAS) and Nuclear Magnetic Resonance (NMR) spectroscopy. Data from an SAS experiment is a set of scattering intensities that can give the interatomic probability distributions. NMR experimental data used in this thesis is set of atom pairs and the maximum distance between them. From SAS data, this thesis determines the association model of the complex and intensities through an approach that is robust to noise and contaminants in solution. Using NMR data, this thesis computes the complex structure by using probabilistic inference and geometry of convex shapes. The structure determination methods are complete, that is they identify all consistent conformations and are data driven wherein the structures are evaluated separately for consistency to data and biophysical energy

Robust hybrid control for autonomous vehicle motion planning

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2001.Includes bibliographical references (p. 141-150).This dissertation focuses on the problem of motion planning for agile autonomous vehicles. In realistic situations, the motion planning problem must be solved in real-time, in a dynamic and uncertain environment. The fulfillment of the mission objectives might also require the exploitation of the full maneuvering capabilities of the vehicle. The main contribution of the dissertation is the development of a new computational and modelling framework (the Maneuver Automaton), and related algorithms, for steering underactuated, nonholonomic mechanical systems. The proposed approach is based on a quantization of the system's dynamics, by which the feasible nominal system trajectories are restricted to the family of curves that can be obtained by the interconnection of suitably defined primitives. This can be seen as a formalization of the concept of "maneuver", allowing for the construction of a framework amenable to mathematical programming. This motion planning framework is applicable to all time-invariant dynamical systems which admit dynamic symmetries and relative equilibria. No other assumptions are made on the dynamics, thus resulting in exact motion planning techniques of general applicability. Building on a relatively expensive off-line computation phase, we provide algorithms viable for real-time applications. A fundamental advantage of this approach is the ability to provide a mathematical foundation for generating a provably stable and consistent hierarchical system, and for developing the tools to analyze the robustness of the system in the presence of uncertainty and/or disturbances.(cont.) In the second part of the dissertation, a randomized algorithm is proposed for real-time motion planning in a dynamic environment. By employing the optimal control solution in a free space developed for the maneuver automaton (or for any other general system), we present a motion planning algorithm with probabilistic convergence and performance guarantees, and hard safety guarantees, even in the face of finite computation times. The proposed methodologies are applicable to a very large class of autonomous vehicles: throughout the dissertation, examples, simulation and experimental results are presented and discussed, involving a variety of mechanical systems, ranging from simple academic examples and laboratory setups, to detailed models of small autonomous helicopters.by Emilio Frazzoli.Ph.D

Artificial Intelligence based multi-agent control system

Le metodologie di Intelligenza Artificiale (AI) si occupano della possibilità di rendere le macchine in grado di compiere azioni intelligenti con lo scopo di aiutare l’essere umano; quindi è possibile affermare che l’Intelligenza Artificiale consente di portare all’interno delle macchine, caratteristiche tipiche considerate come caratteristiche umane. Nello spazio dell’Intelligenza Artificiale ci sono molti compiti che potrebbero essere richiesti alla macchina come la percezione dell’ambiente, la percezione visiva, decisioni complesse. La recente evoluzione in questo campo ha prodotto notevoli scoperte, princi- palmente in sistemi ingegneristici come sistemi multi-agente, sistemi in rete, impianti, sistemi veicolari, sistemi sanitari; infatti una parte dei suddetti sistemi di ingegneria è presente in questa tesi di dottorato. Lo scopo principale di questo lavoro è presentare le mie recenti attività di ricerca nel campo di sistemi complessi che portano le metodologie di intelligenza artifi- ciale ad essere applicati in diversi ambienti, come nelle reti di telecomunicazione, nei sistemi di trasporto e nei sistemi sanitari per la Medicina Personalizzata. Gli approcci progettati e sviluppati nel campo delle reti di telecomunicazione sono presentati nel Capitolo 2, dove un algoritmo di Multi Agent Reinforcement Learning è stato progettato per implementare un approccio model-free al fine di controllare e aumentare il livello di soddisfazione degli utenti; le attività di ricerca nel campo dei sistemi di trasporto sono presentate alla fine del capitolo 2 e nel capitolo 3, in cui i due approcci riguardanti un algoritmo di Reinforcement Learning e un algoritmo di Deep Learning sono stati progettati e sviluppati per far fronte a soluzioni di viaggio personalizzate e all’identificazione automatica dei mezzi trasporto; le ricerche svolte nel campo della Medicina Personalizzata sono state presentate nel Capitolo 4 dove è stato presentato un approccio basato sul controllo Deep Learning e Model Predictive Control per affrontare il problema del controllo dei fattori biologici nei pazienti diabetici.Artificial Intelligence (AI) is a science that deals with the problem of having machines perform intelligent, complex, actions with the aim of helping the human being. It is then possible to assert that Artificial Intelligence permits to bring into machines, typical characteristics and abilities that were once limited to human intervention. In the field of AI there are several tasks that ideally could be delegated to machines, such as environment aware perception, visual perception and complex decisions in the various field. The recent research trends in this field have produced remarkable upgrades mainly on complex engineering systems such as multi-agent systems, networked systems, manufacturing, vehicular and transportation systems, health care; in fact, a portion of the mentioned engineering system is discussed in this PhD thesis, as most of them are typical field of application for traditional control systems. The main purpose if this work is to present my recent research activities in the field of complex systems, bringing artificial intelligent methodologies in different environments such as in telecommunication networks, transportation systems and health care for Personalized Medicine. The designed and developed approaches in the field of telecommunication net- works is presented in Chapter 2, where a multi-agent reinforcement learning algorithm was designed to implement a model-free control approach in order to regulate and improve the level of satisfaction of the users, while the research activities in the field of transportation systems are presented at the end of Chapter 2 and in Chapter 3, where two approaches regarding a Reinforcement Learning algorithm and a Deep Learning algorithm were designed and developed to cope with tailored travels and automatic identification of transportation moralities. Finally, the research activities performed in the field of Personalized Medicine have been presented in Chapter 4 where a Deep Learning and Model Predictive control based approach are presented to address the problem of controlling biological factors in diabetic patients

Primary Structure and Solution Conditions Determine Conformational Ensemble Properties of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are a class of proteins that do not exhibit well-defined three-dimensional structures. The absence of structure is intrinsic to their amino acid sequences, which are characterized by low hydrophobicity and high net charge per residue compared to folded proteins. Contradicting the classic structure-function paradigm, IDPs are capable of interacting with high specificity and affinity, often acquiring order in complex with protein and nucleic acid binding partners. This phenomenon is evident during cellular activities involving IDPs, which include transcriptional and translational regulation, cell cycle control, signal transduction, molecular assembly, and molecular recognition. Although approximately 30% of eukaryotic proteomes are intrinsically disordered, the nature of IDP conformational ensembles remains unclear. In this dissertation, we describe relationships connecting characteristics of IDP conformational ensembles to their primary structures and solution conditions. Using molecular simulations and fluorescence experiments on a set of base-rich IDPs, we find that net charge per residue segregates conformational ensembles along a globule-to-coil transition. Speculatively generalizing this result, we propose a phase diagram that predicts an IDP\u27s average size and shape based on sequence composition and use it to generate hypotheses for a broad set of intrinsically disordered regions (IDRs). Simulations reveal that acid-rich IDRs, unlike their oppositely charged base-rich counterparts, exhibit disordered globular ensembles despite intra-chain repulsive electrostatic interactions. This apparent asymmetry is sensitive to simulation parameters for representing alkali and halide salt ions, suggesting that solution conditions modulate IDP conformational ensembles. We refine the ion parameters using a calibration procedure that relies exclusively on crystal lattice properties. Simulations with these parameters recover swollen coil behavior for acid-rich IDRs, but also uncover a dependence on sequence patterning for polyampholytic IDPs. These contributions initiate an endeavor to elucidate general principles that enable prediction of an IDP\u27s conformational ensemble based on primary structure and solution conditions, a goal analogous to structure prediction for folded proteins. Such principles would provide a molecular basis for understanding the roles of IDPs in physiology and pathophysiology, guide development of agents that modulate their behavior, and enable their rational design from chosen specifications

Semiannual progress report no. 1, 16 November 1964 - 30 June 1965

Summary reports of research in bioelectronics, electron streams and interactions, plasmas, quantum and optical electronics, radiation and propagation, and solid-state electronic

PSA 2018

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018