Control in Robotics
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
How to Start a Robotics Company REPORT
REPORT How to Start a Robotics Company TABLE OF CONTENTS FIRST, IDENTIFY A MARKET NEXT, BUILD A TEAM CRACKING THE VC CODE UP AND RUNNING FINAL WORDS OF ADVICE WHY ARE ROBOTICS COMPANIES DYING? roboticsbusinessreview.com 2 HOW TO START A ROBOTICS COMPANY Building robots are hard; building robot companies are even harder. Here are some tips and advice from those who’ve done it. By Neal Weinberg By all measures, there’s never been a better time to start a robotics About the author: company. Worldwide spending on robotics systems and drones will total $115.7 Neal Weinberg billion in 2019, an increase of 17.6% over 2018, according to IDC. By 2022, is a freelance IDC expects robotics-related spending to reach $210.3 billion, with a technology writer and editor, with compound annual growth rate of 20.2%. experience as Venture capital is flowing like it’s 1999 all over again. According to the a technology PwC/CB Insight MoneyTree Report for 2018, VC funding in the U.S. jumped business writer to $99.5 billion, the highest yearly funding level since the dotcom boom. for daily news- In the category of AI-related companies, which includes robotics, startups papers, and as a writer and editor raised $9.3 billion in 2018, a 72% increase compared to 2017. Nuro, a for technology Silicon Valley startup that is piloting a driverless delivery robot vehicle, publications such announced in February that it successfully raised an astounding $940 as Computer- million from SoftBank. world and Net- Stocks in robotics and AI are hot commodities. -
Claytronics - a Synthetic Reality D.Abhishekh, B.Ramakantha Reddy, Y.Vijaya Kumar, A.Basi Reddy
International Journal of Scientific & Engineering Research, Volume 4, Issue 3, March‐2013 ISSN 2229‐5518 Claytronics - A Synthetic Reality D.Abhishekh, B.Ramakantha Reddy, Y.Vijaya Kumar, A.Basi Reddy, Abstract— "Claytronics" is an emerging field of electronics concerning reconfigurable nanoscale robots ('claytronic atoms', or catoms) designed to form much larger scale machines or mechanisms. Also known as "programmable matter", the catoms will be sub-millimeter computers that will eventually have the ability to move around, communicate with each others, change color, and electrostatically connect to other catoms to form different shapes. Claytronics technology is currently being researched by Professor Seth Goldstein and Professor Todd C. Mowry at Carnegie Mellon University, which is where the term was coined. According to Carnegie Mellon's Synthetic Reality Project personnel, claytronics are described as "An ensemble of material that contains sufficient local computation, actuation, storage, energy, sensing, and communication" which can be programmed to form interesting dynamic shapes and configurations. Index Terms— programmable matter, nanoscale robots, catoms, atoms, electrostatically, LED, LATCH —————————— —————————— 1 INTRODUCTION magine the concept of "programmable matter" -- micro- or which are ringed by several electromagnets are able to move I nano-scale devices which can combine to form the shapes of around each other to form a variety of shapes. Containing ru- physical objects, reassembling themselves as we needed. An dimentary processors and drawing electricity from a board that ensemble of material called catoms that contains sufficient local they rest upon. So far only four catoms have been operated to- computation, actuation, storage, energy, sensing & communica- gether. The plan though is to have thousands of them moving tion which can be programmed to form interesting dynamic around each other to form whatever shape is desired and to shapes and configurations. -
Metallurgical Engineering
ENSURING THE EXPERTISE TO GROW SOUTH AFRICA Discipline-specific Training Guideline for Candidate Engineers in Metallurgical Engineering R-05-MET-PE Revision No.: 2: 25 July 2019 ENGINEERING COUNCIL OF SOUTH AFRICA Tel: 011 6079500 | Fax: 011 6229295 Email: [email protected] | Website: www.ecsa.co.za ENSURING THE Document No.: Effective Date: Revision No.: 2 R-05-MET-PE 25/07/2019 Subject: Discipline-specific Training Guideline for Candidate Engineers in Metallurgical Engineering Compiler: Approving Officer: Next Review Date: Page 2 of 46 MB Mtshali EL Nxumalo 25/07/2023 TABLE OF CONTENTS DEFINITIONS ............................................................................................................................ 3 BACKGROUND ......................................................................................................................... 5 1. PURPOSE OF THIS DOCUMENT ......................................................................................... 5 2. AUDIENCE............................................................................................................................. 6 3. PERSONS NOT REGISTERED AS CANDIDATES OR NOT BEING TRAINED UNDER COMMITMENT AND UNDERTAKING (C&U) ..................................................... 7 4. ORGANISING FRAMEWORK FOR OCCUPATIONS ............................................................. 7 4.1 Extractive Metallurgical Engineering ..................................................................................... 8 4.2 Mineral Processing Engineering .......................................................................................... -
2.1: What Is Robotics? a Robot Is a Programmable Mechanical Device
2.1: What is Robotics? A robot is a programmable mechanical device that can perform tasks and interact with its environment, without the aid of human interaction. Robotics is the science and technology behind the design, manufacturing and application of robots. The word robot was coined by the Czech playwright Karel Capek in 1921. He wrote a play called “Rossum's Universal Robots” that was about a slave class of manufactured human-like servants and their struggle for freedom. The Czech word robota loosely means "compulsive servitude.” The word robotics was first used by the famous science fiction writer, Isaac Asimov, in 1941. 2.1: What is Robotics? Basic Components of a Robot The components of a robot are the body/frame, control system, manipulators, and drivetrain. Body/frame: The body or frame can be of any shape and size. Essentially, the body/frame provides the structure of the robot. Most people are comfortable with human-sized and shaped robots that they have seen in movies, but the majority of actual robots look nothing like humans. Typically, robots are designed more for function than appearance. Control System: The control system of a robot is equivalent to the central nervous system of a human. It coordinates and controls all aspects of the robot. Sensors provide feedback based on the robot’s surroundings, which is then sent to the Central Processing Unit (CPU). The CPU filters this information through the robot’s programming and makes decisions based on logic. The same can be done with a variety of inputs or human commands. -
Technical Report 05-73 the Methodology Center, Pennsylvania State University
Engineering Control Approaches for the Design and Analysis of Adaptive, Time-Varying Interventions Daniel E. Rivera and Michael D. Pew Control Systems Engineering Laboratory Department of Chemical and Materials Engineering Arizona State University Tempe, Arizona 85287-6006 Linda M. Collins The Methodology Center and Department of Human Development and Family Studies Penn State University State College, PA Susan A. Murphy Institute for Social Research and Department of Statistics University of Michigan Ann Arbor, MI May 30, 2005 Technical Report 05-73 The Methodology Center, Pennsylvania State University 1 Abstract Control engineering is the field that examines how to transform dynamical system behavior from undesirable conditions to desirable ones. Cruise control in automobiles, the home thermostat, and the insulin pump are all examples of control engineering at work. In the last few decades, signifi- cant improvements in computing and information technology, increasing access to information, and novel methods for sensing and actuation have enabled the extensive application of control engi- neering concepts to physical systems. While engineering control principles are meaningful as well to problems in the behavioral sciences, the application of this topic to this field remains largely unexplored. This technical report examines how engineering control principles can play a role in the be- havioral sciences through the analysis and design of adaptive, time-varying interventions in the prevention field. The basic conceptual framework for this work draws from the paper by Collins et al. (2004). In the initial portion of the report, a general overview of control engineering principles is presented and illustrated with a simple physical example. From this description, a qualitative description of adaptive time-varying interventions as a form of classical feedback control is devel- oped, which is depicted in the form of a block diagram (i.e., a control-oriented signal and systems representation). -
An Abstract of the Dissertation Of
AN ABSTRACT OF THE DISSERTATION OF Austin Nicolai for the degree of Doctor of Philosophy in Robotics presented on September 11, 2019. Title: Augmented Deep Learning Techniques for Robotic State Estimation Abstract approved: Geoffrey A. Hollinger While robotic systems may have once been relegated to structured environments and automation style tasks, in recent years these boundaries have begun to erode. As robots begin to operate in largely unstructured environments, it becomes more difficult for them to effectively interpret their surroundings. As sensor technology improves, the amount of data these robots must utilize can quickly become intractable. Additional challenges include environmental noise, dynamic obstacles, and inherent sensor non- linearities. Deep learning techniques have emerged as a way to efficiently deal with these challenges. While end-to-end deep learning can be convenient, challenges such as validation and training requirements can be prohibitive to its use. In order to address these issues, we propose augmenting the power of deep learning techniques with tools such as optimization methods, physics based models, and human expertise. In this work, we present a principled framework for approaching a prob- lem that allows a user to identify the types of augmentation methods and deep learning techniques best suited to their problem. To validate our framework, we consider three different domains: LIDAR based odometry estimation, hybrid soft robotic control, and sonar based underwater mapping. First, we investigate LIDAR based odometry estimation which can be characterized with both high data precision and availability; ideal for augmenting with optimization methods. We propose using denoising autoencoders (DAEs) to address the challenges presented by modern LIDARs. -
Control Theory
Control theory S. Simrock DESY, Hamburg, Germany Abstract In engineering and mathematics, control theory deals with the behaviour of dynamical systems. The desired output of a system is called the reference. When one or more output variables of a system need to follow a certain ref- erence over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system. Rapid advances in digital system technology have radically altered the control design options. It has become routinely practicable to design very complicated digital controllers and to carry out the extensive calculations required for their design. These advances in im- plementation and design capability can be obtained at low cost because of the widespread availability of inexpensive and powerful digital processing plat- forms and high-speed analog IO devices. 1 Introduction The emphasis of this tutorial on control theory is on the design of digital controls to achieve good dy- namic response and small errors while using signals that are sampled in time and quantized in amplitude. Both transform (classical control) and state-space (modern control) methods are described and applied to illustrative examples. The transform methods emphasized are the root-locus method of Evans and fre- quency response. The state-space methods developed are the technique of pole assignment augmented by an estimator (observer) and optimal quadratic-loss control. The optimal control problems use the steady-state constant gain solution. Other topics covered are system identification and non-linear control. System identification is a general term to describe mathematical tools and algorithms that build dynamical models from measured data. -
History of Robotics: Timeline
History of Robotics: Timeline This history of robotics is intertwined with the histories of technology, science and the basic principle of progress. Technology used in computing, electricity, even pneumatics and hydraulics can all be considered a part of the history of robotics. The timeline presented is therefore far from complete. Robotics currently represents one of mankind’s greatest accomplishments and is the single greatest attempt of mankind to produce an artificial, sentient being. It is only in recent years that manufacturers are making robotics increasingly available and attainable to the general public. The focus of this timeline is to provide the reader with a general overview of robotics (with a focus more on mobile robots) and to give an appreciation for the inventors and innovators in this field who have helped robotics to become what it is today. RobotShop Distribution Inc., 2008 www.robotshop.ca www.robotshop.us Greek Times Some historians affirm that Talos, a giant creature written about in ancient greek literature, was a creature (either a man or a bull) made of bronze, given by Zeus to Europa. [6] According to one version of the myths he was created in Sardinia by Hephaestus on Zeus' command, who gave him to the Cretan king Minos. In another version Talos came to Crete with Zeus to watch over his love Europa, and Minos received him as a gift from her. There are suppositions that his name Talos in the old Cretan language meant the "Sun" and that Zeus was known in Crete by the similar name of Zeus Tallaios. -
Principles of Robot Locomotion
Principles of robot locomotion Sven Böttcher Seminar ‘Human robot interaction’ Index of contents 1. Introduction................................................................................................................................................1 2. Legged Locomotion...................................................................................................................................2 2.1 Stability................................................................................................................................................3 2.2 Leg configuration.................................................................................................................................4 2.3 One leg.................................................................................................................................................5 2.4 Two legs...............................................................................................................................................7 2.5 Four legs ..............................................................................................................................................9 2.6 Six legs...............................................................................................................................................11 3 Wheeled Locomotion................................................................................................................................13 3.1 Wheel types .......................................................................................................................................13 -
Ph. D. Thesis Stable Locomotion of Humanoid Robots Based
Ph. D. Thesis Stable locomotion of humanoid robots based on mass concentrated model Author: Mario Ricardo Arbul´uSaavedra Director: Carlos Balaguer Bernaldo de Quiros, Ph. D. Department of System and Automation Engineering Legan´es, October 2008 i Ph. D. Thesis Stable locomotion of humanoid robots based on mass concentrated model Author: Mario Ricardo Arbul´uSaavedra Director: Carlos Balaguer Bernaldo de Quiros, Ph. D. Signature of the board: Signature President Vocal Vocal Vocal Secretary Rating: Legan´es, de de Contents 1 Introduction 1 1.1 HistoryofRobots........................... 2 1.1.1 Industrialrobotsstory. 2 1.1.2 Servicerobots......................... 4 1.1.3 Science fiction and robots currently . 10 1.2 Walkingrobots ............................ 10 1.2.1 Outline ............................ 10 1.2.2 Themes of legged robots . 13 1.2.3 Alternative mechanisms of locomotion: Wheeled robots, tracked robots, active cords . 15 1.3 Why study legged machines? . 20 1.4 What control mechanisms do humans and animals use? . 25 1.5 What are problems of biped control? . 27 1.6 Features and applications of humanoid robots with biped loco- motion................................. 29 1.7 Objectives............................... 30 1.8 Thesiscontents ............................ 33 2 Humanoid robots 35 2.1 Human evolution to biped locomotion, intelligence and bipedalism 36 2.2 Types of researches on humanoid robots . 37 2.3 Main humanoid robot research projects . 38 2.3.1 The Humanoid Robot at Waseda University . 38 2.3.2 Hondarobots......................... 47 2.3.3 TheHRPproject....................... 51 2.4 Other humanoids . 54 2.4.1 The Johnnie project . 54 2.4.2 The Robonaut project . 55 2.4.3 The COG project . -
Chapter 4 China's High-Tech Development
CHAPTER 4 CHINA’S HIGH-TECH DEVELOPMENT SECTION 1: CHINA’S PURSUIT OF DOMINANCE IN COMPUTING, ROBOTICS, AND BIOTECHNOLOGY Key Findings • China has laid out an ambitious whole-of-government plan to achieve dominance in advanced technology. This state-led ap- proach utilizes government financing and regulations, high market access and investment barriers for foreign firms, over- seas acquisitions and talent recruitment, and, in some cases, industrial espionage to create globally competitive firms. • China’s close integration of civilian and military technology de- velopment raises concerns that technology, expertise, and intel- lectual property shared by U.S. firms with Chinese commercial partners could be transferred to China’s military. • Artificialintelligence: China—led by Baidu—is now on par with the United States in artificial intelligence due in part to robust Chinese government support, establishment of research insti- tutes in the United States, recruitment of U.S.-based talent, investment in U.S. artificial intelligence-related startups and firms, and commercial and academic partnerships. • Quantum information science: China has closed the technolog- ical gap with the United States in quantum information sci- ence—a sector the United States has long dominated—due to a concerted strategy by the Chinese government and inconsistent and unstable levels of R&D funding and limited government coordination by the United States. • High performance computing: Through multilevel government support, China now has the world’s two fastest supercomputers and is on track to surpass the United States in the next gener- ation of supercomputers—exascale computers—with an expect- ed rollout by 2020 compared to the accelerated U.S. -
Curriculum Reinforcement Learning for Goal-Oriented Robot Control
MEng Individual Project Imperial College London Department of Computing CuRL: Curriculum Reinforcement Learning for Goal-Oriented Robot Control Supervisor: Author: Dr. Ed Johns Harry Uglow Second Marker: Dr. Marc Deisenroth June 17, 2019 Abstract Deep Reinforcement Learning has risen to prominence over the last few years as a field making strong progress tackling continuous control problems, in particular robotic control which has numerous potential applications in industry. However Deep RL algorithms alone struggle on complex robotic control tasks where obstacles need be avoided in order to complete a task. We present Curriculum Reinforcement Learning (CuRL) as a method to help solve these complex tasks by guided training on a curriculum of simpler tasks. We train in simulation, manipulating a task environment in ways not possible in the real world to create that curriculum, and use domain randomisation in attempt to train pose estimators and end-to-end controllers for sim-to-real transfer. To the best of our knowledge this work represents the first example of reinforcement learning with a curriculum of simpler tasks on robotic control problems. Acknowledgements I would like to thank: • Dr. Ed Johns for his advice and support as supervisor. Our discussions helped inform many of the project’s key decisions. • My parents, Mike and Lyndsey Uglow, whose love and support has made the last four year’s possible. Contents 1 Introduction8 1.1 Objectives................................. 9 1.2 Contributions ............................... 10 1.3 Report Structure ............................. 11 2 Background 12 2.1 Machine learning (ML) .......................... 12 2.2 Artificial Neural Networks (ANNs) ................... 12 2.2.1 Strengths of ANNs .......................