Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Subshifts with Simple Cellular Automata

A subshift is a set of infinite one- or two-way sequences over a fixed finite set, defined by a set of forbidden patterns. In this thesis, we study subshifts in the topological setting, where the natural morphisms between them are ones defined by a (spatially uniform) local rule. Endomorphisms of subshifts are called cellular automata, and we call the set of cellular automata on a subshift its endomorphism monoid. It is known that the set of all sequences (the full shift) allows cellular automata with complex dynamical and computational properties. We are interested in subshifts that do not support such cellular automata. In particular, we study countable subshifts, minimal subshifts and subshifts with additional universal algebraic structure that cellular automata need to respect, and investigate certain criteria of ‘simplicity’ of the endomorphism monoid, for each of them. In the case of countable subshifts, we concentrate on countable sofic shifts, that is, countable subshifts defined by a finite state automaton. We develop some general tools for studying cellular automata on such subshifts, and show that nilpotency and periodicity of cellular automata are decidable properties, and positive expansivity is impossible. Nevertheless, we also prove various undecidability results, by simulating counter machines with cellular automata. We prove that minimal subshifts generated by primitive Pisot substitutions only support virtually cyclic automorphism groups, and give an example of a Toeplitz subshift whose automorphism group is not finitely generated. In the algebraic setting, we study the centralizers of CA, and group and lattice homomorphic CA. In particular, we obtain results about centralizers of symbol permutations and bipermutive CA, and their connections with group structures.Siirretty Doriast

Automata-Theoretic Approaches to Planning in Robotics: Combinatorial Filter Minimization, Planning to Chronicle, Temporal Logic Planning With Soft Specifications, and Sensor Selection for Detecting Deviations From a Planned Itinerary

In this dissertation, we present a collection of new planning algorithms that enable robots to achieve complex goals, beyond simple point-to-point path planning, using automata-theoretic methods, and we consider the filter minimization (FM) problem and a variant of it, filter partitioning minimization (FPM) problem, which aims to minimize combinatorial filters, used for filtering and automata-theoretic planning in systems with discrete sensor data. We introduce a new variant of bisimulation, compatibility, and using this notion we identify several classes of filters for which FM or FPM is solvable in polynomial time, and propose several integer linear programming (ILP) formulations of FM and FPM. Then, we consider a problem, planning to chronicle, in which a robot is tasked with observing an uncertain time-extended process to produce a ‘chronicle’ of occurrent events that meets a given specification. This problem is useful in applications where we deploy robots to autonomously make structured videos or documentaries from events occurring in an unpredictable environment. Next, we study two variants of temporal logic planning in which the objective is to synthesize a trajectory that satisfies an optimal selection of soft constraints while nevertheless satisfying a hard constraint expressed in linear temporal logic (LTL). We also extend planning to chronicle with the idea of this problem. Then, we consider the problem of planning where to observe the behavior of an agent to ensure that the agent’s execution within the environment fits a pre-disclosed itinerary. This problem arises in a range of contexts including in validating safety claims about robot behavior, applications in security and surveillance, and for both the conception and the (physical) design and logistics of scientific experiments

Deployment of Heterogeneous Swarm Robotic Agents Using a Task-Oriented Utility-Based Algorithm

In a swarm robotic system, the desired collective behavior emerges from local decisions made by robots, themselves, according to their environment. Swarm robotics is an emerging area that has attracted many researchers over the last few years. It has been proven that a single robot with multiple capabilities cannot complete an intended job within the same time frame as that of multiple robotic agents. A swarm of robots, each one with its own capabilities, are more flexible, robust, and cost-effective than an individual robot. As a result of a comprehensive investigation of the current state of swarm robotic research, this dissertation demonstrates how current swarm deployment systems lack the ability to coordinate heterogeneous robotic agents. Moreover, this dissertation's objective shall define the starting point of potential algorithms that lead to the development of a new software environment interface. This interface will assign a set of collaborative tasks to the swarm system without being concerned about the underlying hardware of the heterogeneous robotic agents. The ultimate goal of this research is to develop a task-oriented software application that facilitates the rapid deployment of multiple robotic agents. The task solutions are created at run-time, and executed by the agents in a centralized or decentralized fashion. Tasks are fractioned into smaller sub-tasks which are, then, assigned to the optimal number of robots using a novel Robot Utility Based Task Assignment (RUTA) algorithm. The system deploys these robots using it's application program interfaces (API's) and uploads programs that are integrated with a small routine code. The embedded routine allows robots to configure solutions when the decentralized approach is adopted. In addition, the proposed application also offers customization of robotic platforms by simply defining the available sensing and actuation devices. Another objective of the system is to improve code and component reusability to reduce efforts in deploying tasks to swarm robotic agents. Usage of the proposed framework prevents the need to redesign or rewrite programs should any changes take place in the robot's platform

A Dynamical System Approach for Resource-Constrained Mobile Robotics

The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots. First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment. Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated. Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios. Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long- term behavior of a marine environment from a flow point-of-view. In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics system

Semi-Structured Decision Processes: A Conceptual Framework for Understanding Human-Automation Decision Systems

The purpose of this work is to improve understanding of existing and proposed decision systems, ideally to improve the design of future systems. A "decision system" is defined as a collection of information-processing components -- often involving humans and automation (e.g., computers) -- that interact towards a common set of objectives. Since a key issue in the design of decision systems is the division of work between humans and machines (a task known as "function allocation"), this report is primarily intended to help designers incorporate automation more appropriately within these systems. This report does not provide a design methodology, but introduces a way to qualitatively analyze potential designs early in the system design process. A novel analytical framework is presented, based on the concept of "semi-Structured" decision processes. It is believed that many decisions involve both well-defined "Structured" parts (e.g., formal procedures, traditional algorithms) and ill-defined "Unstructured" parts (e.g., intuition, judgement, neural networks) that interact in a known manner. While Structured processes are often desired because they fully prescribe how a future decision (during "operation") will be made, they are limited by what is explicitly understood prior to operation. A system designer who incorporates Unstructured processes into a decision system understands which parts are not understood sufficiently, and relinquishes control by deferring decision-making from design to operation. Among other things, this design choice tends to add flexibility and robustness. The value of the semi-Structured framework is that it forces people to consider system design concepts as operational decision processes in which both well-defined and ill-defined components are made explicit. This may provide more insight into decision systems, and improve understanding of the implications of design choices. The first part of this report defines the semi-Structured process and introduces a diagrammatic notation for decision process models. In the second part, the semi-Structured framework is used to understand and explain highly evolved decision system designs (these are assumed to be representative of "good" designs) whose components include feedback controllers, alerts, decision aids, and displays. Lastly, the semi-Structured framework is applied to a decision system design for a mobile robot.Charles Stark Draper Laboratory, Inc., under IR&D effort 101

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg