The evolution of human beings is marked by adaptation. The ability to adapt to and manipulate our environment is one definer of intelligence, and ours is unique among life on Earth. Since moving off of the African Continent, humans have migrated to inhabit every part of the Earth. Human existence and perpetuity in the universe depends upon the success of this adaptation, and inevitably, migrating off of this planet. The technological advances being developed today will change our way of life, and enable people to travel to and live permanently on the Moon and Mars. This study involves the architectural design and construction of a completely programmable permanent Martian settlement in the year 2050. Previous studies and proposals for Martian architecture rely mostly on existing technology. The first people are not expected to reach Mars until 2030, and new and emerging technologies will radically affect the designs being considered today. Technical challenges constrain designers of space architecture today, and scientific developments will solve many of these. This study seeks to explore how new technology can positively affect the architecture of the future, affording more comfortable and livable space on Mars. With a construction date of 2050, this project differs from others by benefitting from the next four decades of profound technological advancement. Leading Futurist Raymond Kurzweil predicts that the technological singularity is within this time frame, and that the 21st Century will, “Witness on the order of 20,000 years of progress (at today’s rate of progress) (Kurzweil, Law of Accelerating Change).” This thesis theorizes that nanotechnology will enable the deployment of a completely self-constructing and programmable permanent Martian settlement designed from a series of spatial modules. The anticipated results include a modular system of architectural spaces, and an increased awareness of the architectural benefits of emerging technologies as they relate to future space architecture.

Multi-agent systems (MAS) have been of interest to many researchers during the last decades. This thesis focuses on multi-robot systems (MRS) and programmable matter as two types of MAS. Regarding MRS, the focus is on the 'mergeable nervous system' (MNS) concept which allows the robots to connect to one another and establish a communication network through self-organization and then use the network to temporarily report sensing events and cede authority to a single robot in the system. Here, in a collective perception scenario, we experimentally evaluate the performance of an MNS-enabled approach and compare it with that of several decentralized benchmark approaches. We show that an MNS-enabled approach is high-performing, fault-tolerant, and scalable, so it is an appropriate approach for MRS. As a goal of the thesis, using an MNS-enabled approach, we present for the first time a comprehensive comparison of control architectures in multi-robot systems, which includes a comparison of accuracy, efficiency, speed, energy consumption, scalability, and fault tolerance. Our comparisons provide designers of multi-robot systems with a better understanding for selecting the best-performing control depending on the system's objectives. Additionally, as a separate goal, we design a high-level leader based programmable matter, which can perform some basic primitive operations in a grid environment, and construct it using lower-level organisms. We design and implement deterministic algorithms for "curl" operation of this high-level matter, an instance of shape formation problem. We prove the correctness of the presented algorithms, analytically determine their complexity, and experimentally evaluate their performance.

abstract: Imagine that we have a piece of matter that can change its physical properties like its shape, density, conductivity, or color in a programmable fashion based on either user input or autonomous sensing. This is the vision behind what is commonly known as programmable matter. Envisioning systems of nano-sensors devices, programmable matter consists of systems of simple computational elements, called particles, that can establish and release bonds, compute, and can actively move in a self-organized way. In this dissertation the feasibility of solving fundamental problems relevant for programmable matter is investigated. As a model for such self-organizing particle systems (SOPS), the geometric amoebot model is introduced. In this model, particles only have local information and have modest computational power. They achieve locomotion by expanding and contracting, which resembles the behavior of amoeba. Under this model, efficient local-control algorithms for the leader election problem in SOPS are presented. As a central problem for programmable matter, shape formation problems are then studied. The limitations of solving the leader election problem and the shape formation problem on a more general version of the amoebot model are also discussed. The \smart paint" problem is also studied which aims at having the particles self-organize in order to uniformly coat the surface of an object of arbitrary shape and size, forming multiple coating layers if necessary. A Universal Coating algorithm is presented and shown to be asymptotically worst-case optimal both in terms of time with high probability and work. In particular, the algorithm always terminates within a linear number of rounds with high probability. A linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information is presented, which implies that the proposed algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of the proposed algorithm may be better than linear in practice. Developed algorithms utilize only local control, require only constant-size memory particles, and are asymptotically optimal in terms of the total number of particle movements needed to reach the desired shape configuration. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2017

<p>Metamaterials are artificially structured materials which attain their unconventional macroscopic properties from their cellular configuration rather than their constituent chemical composition. The judicious design of this cellular structure opens the possibility to program and control the optical, mechanical, acoustic, or thermal responses of metamaterials. This Ph.D. dissertation focuses on scalable design and manufacturing strategies for optical and mechanical metamaterials.<br> <br> </p> <p>The fabrication of optical metamaterials still relies heavily on low-throughput process such as electron beam lithography, which is a serial technique. Thus, there is a growing need for the development of high-throughput, parallel processes to make the fabrication of optical metamaterials more accessible and cost-effective. The first part of this dissertation presents a scalable manufacturing method, termed “roll-to-roll laser induced superplasticity” (R2RLIS), for the production of flexible optical metamaterials, specifically metallic near-perfect absorbers. R2RLIS enables the rapid and inexpensive fabrication of ultra-smooth metallic nanostructures over large areas using conventional CO₂ engravers or inexpensive diode lasers. Using low-cost metal/epoxy nanomolds, the minimum feature size obtained by R2RLIS was <40 nm, facilitating the rapid fabrication of flexible near-perfect absorbers at visible frequencies with the capability to wrap around non-planar surfaces.</p> <p> </p> <p>The existing approaches for designing mechanical metamaterials are mostly <i>ad hoc</i>, and rely heavily on intuition and trial-and-error. A rational and systematic approach to create functional and programmable mechanical metamaterials is therefore desirable to unlock the vast design space of mechanical properties. The second part of this dissertation introduces a systematic, algorithmic design strategy based on Voronoi tessellation to create architected soft machines (ASMs) and twisting mechanical metamaterials (TMMs) with programmable motion and properties. ASMs are a new class of soft machines that benefit from their 3D-architected structure to expand the range of mechanical properties and behaviors achievable by 3D printed soft robots. On tendon-based actuation, ASMs deform according to the topologically encoded buckling of their structure to produce a wide range of motions such as contraction, twisting, bending, and cyclic motion. TMMs are a new class of chiral mechanical metamaterials which exhibit compression-twist coupling, a property absent in isotropic materials. This property manifests macroscopically and is independent of the flexible material chosen to fabricate the TMM. The nature of this compression-twist coupling can be programmed by simply tuning two design parameters, giving access to distinct twisting regimes and tunable onset of auxetic (negative Poisson’s ratio) behavior. Taking a metamaterial approach toward the design of soft machines substantially increases their number of degrees of freedom in deformation, thus blurring the boundary between materials and machines.</p>