A Parallel Autonomy Research Platform

We present the development of a full-scale “parallel autonomy” research platform including software and hardware. In the parallel autonomy paradigm, the control of the vehicle is shared; the human is still in control of the vehicle, but the autonomy system is always running in the background to prevent accidents. Our holistic approach includes: (1) a driveby-wire conversion method only based on reverse engineering, (2) mounting of relatively inexpensive sensors onto the vehicle, (3) implementation of a localization and mapping system, (4) obstacle detection and (5) a shared controller as well as (6) integration with an advanced autonomy simulation system (Drake) for rapid development and testing. The system can operate in three modes: (a) manual driving, (b) full autonomy, where the system is in complete control of the vehicle and (c) parallel autonomy, where the shared controller is implemented. We present results from extensive testing of a full-scale vehicle on closed tracks that demonstrate these capabilities

Transparent resource sharing framework for internet services on handheld devices

Handheld devices have limited processing power and a short battery lifetime. As a result, computationally intensive applications cannot run appropriately or cause the device to run out of battery too early. Additionally, Internet-based service providers targeting these mobile devices lack information to estimate the remaining battery autonomy and have no view on the availability of idle resources in the neighborhood of the handheld device. These battery-related issues create an opportunity for Internet providers to broaden their role and start managing energy aspects of battery-driven mobile devices inside the home. In this paper, we propose an energy-aware resource-sharing framework that enables Internet access providers to delegate (a part of) a client application from a handheld device to idle resources in the LAN, in a transparent way for the end-user. The key component is the resource sharing service, hosted on the LAN gateway, which can be remotely queried and managed by the Internet access provider. The service includes a battery model to predict the remaining battery lifetime. We describe the concept of resource-sharing-as-a-service that allows users of handheld devices to subscribe to the resource sharing service. In a proof-of-concept, we evaluate the delay to offload a client application to an idle computer and study the impact on battery autonomy as a function of the CPU cycles that can be offloaded

The Big, Gig Picture: We Can\u27t Assume the Same Constructs Matter

I am concerned about industrial and organizational (I-O) psychology\u27s relevance to the gig economy, defined here as the broad trends toward technology-based platform work. This sort of work happens on apps like Uber (where the app connects drivers and riders) and sites like MTurk (where human intelligence tasks, or HITs, are advertised to workers on behalf of requesters). We carry on with I-O research and practice as if technology comprises only things (e.g., phones, websites, platforms) that we use to assess applicants and complete work. However, technology has much more radically restructured work as we know it, to happen in a much more piecemeal, on-demand fashion, reviving debates about worker classification and changing the reality of work for many workers (Sundararajan, 2016). Instead of studying technology as a thing we use, it\u27s critical that we “zoom out” to see and adapt our field to this bigger picture of trends towards a gig economy. Rather than a phone being used to check work email or complete pre-hire assessments, technology and work are inseparable. For example, working on MTurk requires constant Internet access (Brawley, Pury, Switzer, & Saylors, 2017; Ma, Khansa, & Hou, 2016). Alarmingly, some researchers describe these workers as precarious (Spretizer, Cameron, & Garrett, 2017), dependent on an extremely flexible (a label that is perhaps euphemistic for unreliable) source of work. Although it\u27s unlikely that all workers consider their “gig” a full time job or otherwise necessary income, at least some workers do: An estimated 10–40% of MTurk workers consider themselves serious gig workers (Brawley & Pury, 2016). Total numbers for the broader gig economy are only growing, with recent tax-based estimates including 34% of the US workforce now and up to 43% within 3 years (Gillespie, 2017). It appears we\u27re seeing some trends in work reverse and return to piece work (e.g., a ride on Uber, a HIT on MTurk) as if we\u27ve simply digitized the assembly line (Davis, 2016). Over time, these trends could accelerate, and we could potentially see total elimination of work (Morrison, 2017)

Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies

Grid is an infrastructure that involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid applications often involve large amounts of data and/or computing resources that require secure resource sharing across organizational boundaries. This makes Grid application management and deployment a complex undertaking. Grid middlewares provide users with seamless computing ability and uniform access to resources in the heterogeneous Grid environment. Several software toolkits and systems have been developed, most of which are results of academic research projects, all over the world. This chapter will focus on four of these middlewares--UNICORE, Globus, Legion and Gridbus. It also presents our implementation of a resource broker for UNICORE as this functionality was not supported in it. A comparison of these systems on the basis of the architecture, implementation model and several other features is included.Comment: 19 pages, 10 figure

Navigace mobilních robotů v neznámém prostředí s využitím měření vzdáleností

The ability of a robot to navigate itself in the environment is a crucial step towards its autonomy. Navigation as a subtask of the development of autonomous robots is the subject of this thesis, focusing on the development of a method for simultaneous localization an mapping (SLAM) of mobile robots in six degrees of freedom (DOF). As a part of this research, a platform for 3D range data acquisition based on a continuously inclined laser rangefinder was developed. This platform is presented, evaluating the measurements and also presenting the robotic equipment on which the platform can be fitted. The localization and mapping task is equal to the registration of multiple 3D images into a common frame of reference. For this purpose, a method based on the Iterative Closest Point (ICP) algorithm was developed. First, the originally implemented SLAM method is presented, focusing on the time-wise performance and the registration quality issues introduced by the implemented algorithms. In order to accelerate and improve the quality of the time-demanding 6DOF image registration, an extended method was developed. The major extension is the introduction of a factorized registration, extracting 2D representations of vertical objects called leveled maps from the 3D point sets, ensuring these representations are 3DOF invariant. The extracted representations are registered in 3DOF using ICP algorithm, allowing pre-alignment of the 3D data for the subsequent robust 6DOF ICP based registration. The extended method is presented, showing all important modifications to the original method. The developed registration method was evaluated using real 3D data acquired in different indoor environments, examining the benefits of the factorization and other extensions as well as the performance of the original ICP based method. The factorization gives promising results compared to a single phase 6DOF registration in vertically structured environments. Also, the disadvantages of the method are discussed, proposing possible solutions. Finally, the future prospects of the research are presented.Schopnost lokalizace a navigace je podmínkou autonomního provozu mobilních robotů. Předmětem této disertační práce jsou navigační metody se zaměřením na metodu pro simultánní lokalizaci a mapování (SLAM) mobilních robotů v šesti stupních volnosti (6DOF). Nedílnou součástí tohoto výzkumu byl vývoj platformy pro sběr 3D vzdálenostních dat s využitím kontinuálně naklápěného laserového řádkového scanneru. Tato platforma byla vyvinuta jako samostatný modul, aby mohla být umístěna na různé šasi mobilních robotů. Úkol lokalizace a mapování je ekvivalentní registraci více 3D obrazů do společného souřadného systému. Pro tyto účely byla vyvinuta metoda založená na algoritmu Iterative Closest Point Algorithm (ICP). Původně implementovaná verze navigační metody využívá ICP s akcelerací pomocí kd-stromů přičemž jsou zhodnoceny její kvalitativní a výkonnostní aspekty. Na základě této analýzy byly vyvinuty rozšíření původní metody založené na ICP. Jednou z hlavních modifikací je faktorizace registračního procesu, kdy tato faktorizace je založena na redukci dat: vytvoření 2D „leveled“ map (ve smyslu jednoúrovňových map) ze 3D vzdálenostních obrazů. Pro tuto redukci je technologicky i algoritmicky zajištěna invariantnost těchto map vůči třem stupňům volnosti. Tyto redukované mapy jsou registrovány pomocí ICP ve zbylých třech stupních volnosti, přičemž získaná transformace je aplikována na 3D data za účelem před-registrace 3D obrazů. Následně je provedena robustní 6DOF registrace. Rozšířená metoda je v disertační práci v popsána spolu se všemi podstatnými modifikacemi. Vyvinutá metoda byla otestována a zhodnocena s využitím skutečných 3D vzdálenostních dat naměřených v různých vnitřních prostředích. Jsou zhodnoceny přínosy faktorizace a jiných modifikací ve srovnání s původní jednofázovou 6DOF registrací, také jsou zmíněny nevýhody implementované metody a navrženy způsoby jejich řešení. Nakonec následuje návrh budoucího výzkumu a diskuse o možnostech dalšího rozvoje.