Multiprocess Communication and Control Software for Humanoid Robots Neil T
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Efficient Inter-Core Communications on Manycore Machines
ZIMP: Efficient Inter-core Communications on Manycore Machines Pierre-Louis Aublin Sonia Ben Mokhtar Gilles Muller Vivien Quema´ Grenoble University CNRS - LIRIS INRIA CNRS - LIG Abstract—Modern computers have an increasing num- nication mechanisms enabling both one-to-one and one- ber of cores and, as exemplified by the recent Barrelfish to-many communications. Current operating systems operating system, the software they execute increasingly provide various inter-core communication mechanisms. resembles distributed, message-passing systems. To sup- port this evolution, there is a need for very efficient However it is not clear yet how these mechanisms be- inter-core communication mechanisms. Current operat- have on manycore processors, especially when messages ing systems provide various inter-core communication are intended to many recipient cores. mechanisms, but it is not clear yet how they behave In this report, we study seven communication mech- on manycore processors. In this report, we study seven anisms, which are considered state-of-the-art. More mechanisms, that are considered state-of-the-art. We show that these mechanisms have two main drawbacks that precisely, we study TCP, UDP and Unix domain sock- limit their efficiency: they perform costly memory copy ets, pipes, IPC and POSIX message queues. These operations and they do not provide efficient support mechanisms are supported by traditional operating sys- for one-to-many communications. We do thus propose tems such as Linux. We also study Barrelfish message ZIMP, a new inter-core communication mechanism that passing, the inter-process communication mechanism of implements zero-copy inter-core message communications and that efficiently handles one-to-many communications. -
D-Bus, the Message Bus System Training Material
Maemo Diablo D-Bus, The Message Bus System Training Material February 9, 2009 Contents 1 D-Bus, The Message Bus System 2 1.1 Introduction to D-Bus ......................... 2 1.2 D-Bus architecture and terminology ................ 3 1.3 Addressing and names in D-Bus .................. 4 1.4 Role of D-Bus in maemo ....................... 6 1.5 Programming directly with libdbus ................. 9 1 Chapter 1 D-Bus, The Message Bus System 1.1 Introduction to D-Bus D-Bus (the D originally stood for "Desktop") is a relatively new inter process communication (IPC) mechanism designed to be used as a unified middleware layer in free desktop environments. Some example projects where D-Bus is used are GNOME and Hildon. Compared to other middleware layers for IPC, D-Bus lacks many of the more refined (and complicated) features and for that reason, is faster and simpler. D-Bus does not directly compete with low level IPC mechanisms like sock- ets, shared memory or message queues. Each of these mechanisms have their uses, which normally do not overlap the ones in D-Bus. Instead, D-Bus aims to provide higher level functionality, like: Structured name spaces • Architecture independent data formatting • Support for the most common data elements in messages • A generic remote call interface with support for exceptions (errors) • A generic signalling interface to support "broadcast" type communication • Clear separation of per-user and system-wide scopes, which is important • when dealing with multi-user systems Not bound to any specific programming language (while providing a • design that readily maps to most higher level languages, via language specific bindings) The design of D-Bus benefits from the long experience of using other mid- dleware IPC solutions in the desktop arena and this has allowed the design to be optimised. -
Beej's Guide to Unix IPC
Beej's Guide to Unix IPC Brian “Beej Jorgensen” Hall [email protected] Version 1.1.3 December 1, 2015 Copyright © 2015 Brian “Beej Jorgensen” Hall This guide is written in XML using the vim editor on a Slackware Linux box loaded with GNU tools. The cover “art” and diagrams are produced with Inkscape. The XML is converted into HTML and XSL-FO by custom Python scripts. The XSL-FO output is then munged by Apache FOP to produce PDF documents, using Liberation fonts. The toolchain is composed of 100% Free and Open Source Software. Unless otherwise mutually agreed by the parties in writing, the author offers the work as-is and makes no representations or warranties of any kind concerning the work, express, implied, statutory or otherwise, including, without limitation, warranties of title, merchantibility, fitness for a particular purpose, noninfringement, or the absence of latent or other defects, accuracy, or the presence of absence of errors, whether or not discoverable. Except to the extent required by applicable law, in no event will the author be liable to you on any legal theory for any special, incidental, consequential, punitive or exemplary damages arising out of the use of the work, even if the author has been advised of the possibility of such damages. This document is freely distributable under the terms of the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. See the Copyright and Distribution section for details. Copyright © 2015 Brian “Beej Jorgensen” Hall Contents 1. Intro................................................................................................................................................................1 1.1. Audience 1 1.2. Platform and Compiler 1 1.3. -
An Introduction to Linux IPC
An introduction to Linux IPC Michael Kerrisk © 2013 linux.conf.au 2013 http://man7.org/ Canberra, Australia [email protected] 2013-01-30 http://lwn.net/ [email protected] man7 .org 1 Goal ● Limited time! ● Get a flavor of main IPC methods man7 .org 2 Me ● Programming on UNIX & Linux since 1987 ● Linux man-pages maintainer ● http://www.kernel.org/doc/man-pages/ ● Kernel + glibc API ● Author of: Further info: http://man7.org/tlpi/ man7 .org 3 You ● Can read a bit of C ● Have a passing familiarity with common syscalls ● fork(), open(), read(), write() man7 .org 4 There’s a lot of IPC ● Pipes ● Shared memory mappings ● FIFOs ● File vs Anonymous ● Cross-memory attach ● Pseudoterminals ● proc_vm_readv() / proc_vm_writev() ● Sockets ● Signals ● Stream vs Datagram (vs Seq. packet) ● Standard, Realtime ● UNIX vs Internet domain ● Eventfd ● POSIX message queues ● Futexes ● POSIX shared memory ● Record locks ● ● POSIX semaphores File locks ● ● Named, Unnamed Mutexes ● System V message queues ● Condition variables ● System V shared memory ● Barriers ● ● System V semaphores Read-write locks man7 .org 5 It helps to classify ● Pipes ● Shared memory mappings ● FIFOs ● File vs Anonymous ● Cross-memory attach ● Pseudoterminals ● proc_vm_readv() / proc_vm_writev() ● Sockets ● Signals ● Stream vs Datagram (vs Seq. packet) ● Standard, Realtime ● UNIX vs Internet domain ● Eventfd ● POSIX message queues ● Futexes ● POSIX shared memory ● Record locks ● ● POSIX semaphores File locks ● ● Named, Unnamed Mutexes ● System V message queues ● Condition variables ● System V shared memory ● Barriers ● ● System V semaphores Read-write locks man7 .org 6 It helps to classify ● Pipes ● Shared memory mappings ● FIFOs ● File vs Anonymous ● Cross-memoryn attach ● Pseudoterminals tio a ● proc_vm_readv() / proc_vm_writev() ● Sockets ic n ● Signals ● Stream vs Datagram (vs uSeq. -
Message Passing Model
Message Passing Model Giuseppe Anastasi [email protected] Pervasive Computing & Networking Lab. (PerLab) Dept. of Information Engineering, University of Pisa PerLab Based on original slides by Silberschatz, Galvin and Gagne Overview PerLab Message Passing Model Addressing Synchronization Example of IPC systems Message Passing Model 2 Operating Systems Objectives PerLab To introduce an alternative solution (to shared memory) for process cooperation To show pros and cons of message passing vs. shared memory To show some examples of message-based communication systems Message Passing Model 3 Operating Systems Inter-Process Communication (IPC) PerLab Message system – processes communicate with each other without resorting to shared variables. IPC facility provides two operations: send (message ) – fixed or variable message size receive (message ) If P and Q wish to communicate, they need to: establish a communication link between them exchange messages via send/receive The communication link is provided by the OS Message Passing Model 4 Operating Systems Implementation Issues PerLab Physical implementation Single-processor system Shared memory Multi-processor systems Hardware bus Distributed systems Networking System + Communication networks Message Passing Model 5 Operating Systems Implementation Issues PerLab Logical properties Can a link be associated with more than two processes? How many links can there be between every pair of communicating processes? What is the capacity of a link? Is the size of a message that the link can accommodate fixed or variable? Is a link unidirectional or bi-directional? Message Passing Model 6 Operating Systems Implementation Issues PerLab Other Aspects Addressing Synchronization Buffering Message Passing Model 7 Operating Systems Overview PerLab Message Passing Model Addressing Synchronization Example of IPC systems Message Passing Model 8 Operating Systems Direct Addressing PerLab Processes must name each other explicitly. -
Towards Autonomous Multi-Robot Mobile Deposition for Construction
YouWasps: Towards Autonomous Multi-Robot Mobile Deposition for Construction Julius Sustarevas1, Benjamin K. X. Tan1, David Gerber2, Robert Stuart-Smith1;3 and Vijay M. Pawar1;3 Abstract— Mobile multi-robot construction systems offer new ways to optimise the on-site construction process. In this paper we begin to investigate the functionality requirements for controlling a team of robots to build structures much greater than their individual workspace. To achieve these aims, we present a mobile extruder robot called YouWasp. We also begin to explore methods for collision aware printing and construction task decomposition and allocation. These are deployed via YouWasp and enable it to deposit material autonomously. In doing so, we are able to evaluate the potential for parallelization of tasks and printing autonomy in simulation as well as physical team of robots. Altogether, these results provide a foundation for future work that enable fleets of mobile construction systems to cooperate and help us shape our built environment in new ways. Fig. 1: Visualisation of YouWasp team of robots deployed in a construction scenario. I. INTRODUCTION The US$8.8 trillion global market value[1] of the con- manufacturing companies such as Apis Cor in Russia, Win- struction sector, shows the unmatched effort that construc- sun Decoration Engineering Company in China, NASA’s tion, as a human endevour, receives. In fact, construction 3D printing in Zero-G and US Armed Forces on-site 3D is often seen as the sector at the forefront of tackling printed barracks. Within this context, typical examples use global challenges like population growth, urbanisation and either gantry-type solutions or industrial robots with 3-to- sustainability[2]. -
Message Passing
COS 318: Operating Systems Message Passing Jaswinder Pal Singh Computer Science Department Princeton University (http://www.cs.princeton.edu/courses/cos318/) Sending A Message Within A Computer Across A Network P1 P2 Send() Recv() Send() Recv() Network OS Kernel OS OS COS461 2 Synchronous Message Passing (Within A System) Synchronous send: u Call send system call with M u send system call: l No buffer in kernel: block send( M ) recv( M ) l Copy M to kernel buffer Synchronous recv: u Call recv system call M u recv system call: l No M in kernel: block l Copy to user buffer How to manage kernel buffer? 3 API Issues u Message S l Buffer (addr) and size l Message type, buffer and size u Destination or source send(dest, msg) R l Direct address: node Id, process Id l Indirect address: mailbox, socket, recv(src, msg) channel, … 4 Direct Addressing Example Producer(){ Consumer(){ ... ... while (1) { for (i=0; i<N; i++) produce item; send(Producer, credit); recv(Consumer, &credit); while (1) { send(Consumer, item); recv(Producer, &item); } send(Producer, credit); } consume item; } } u Does this work? u Would it work with multiple producers and 1 consumer? u Would it work with 1 producer and multiple consumers? u What about multiple producers and multiple consumers? 5 Indirect Addressing Example Producer(){ Consumer(){ ... ... while (1) { for (i=0; i<N; i++) produce item; send(prodMbox, credit); recv(prodMbox, &credit); while (1) { send(consMbox, item); recv(consMbox, &item); } send(prodMbox, credit); } consume item; } } u Would it work with multiple producers and 1 consumer? u Would it work with 1 producer and multiple consumers? u What about multiple producers and multiple consumers? 6 Indirect Communication u Names l mailbox, socket, channel, … u Properties l Some allow one-to-one mbox (e.g. -
8. Robotic Systems Architectures and Programming
187 8. RoboticRobotic Systems Architectures and Programming Syste David Kortenkamp, Reid Simmons 8.1 Overview.............................................. 187 Robot software systems tend to be complex. This 8.1.1 Special Needs complexity is due, in large part, to the need to con- of Robot Architectures .................. 188 trol diverse sensors and actuators in real time, in 8.1.2 Modularity and Hierarchy.............. 188 the face of significant uncertainty and noise. Robot 8.1.3 Software Development Tools.......... 188 systems must work to achieve tasks while moni- toring for, and reacting to, unexpected situations. 8.2 History ................................................ 189 Doing all this concurrently and asynchronously 8.2.1 Subsumption ............................... 189 adds immensely to system complexity. 8.2.2 Layered Robot Control Architectures 190 The use of a well-conceived architecture, 8.3 Architectural Components ..................... 193 together with programming tools that support 8.3.1 Connecting Components................ 193 the architecture, can often help to manage that 8.3.2 Behavioral Control........................ 195 complexity. Currently, there is no single archi- 8.3.3 Executive .................................... 196 tecture that is best for all applications – different 8.3.4 Planning ..................................... 199 architectures have different advantages and dis- 8.4 Case Study – GRACE ............................... 200 advantages. It is important to understand those strengths and weaknesses when choosing an 8.5 The Art of Robot Architectures ............... 202 architectural approach for a given application. 8.6 Conclusions and Further Reading ........... 203 This chapter presents various approaches to architecting robotic systems. It starts by defining References .................................................. 204 terms and setting the context, including a recount- ing of the historical developments in the area of robot architectures. -
Open Message Queue Technical Overview Release 5.0
Open Message Queue Technical Overview Release 5.0 May 2013 This book provides an introduction to the technology, concepts, architecture, capabilities, and features of the Message Queue messaging service. Open Message Queue Technical Overview, Release 5.0 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, the following notice is applicable: U.S. GOVERNMENT RIGHTS Programs, software, databases, and related documentation and technical data delivered to U.S. Government customers are "commercial computer software" or "commercial technical data" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, duplication, disclosure, modification, and adaptation shall be subject to the restrictions and license terms set forth in the applicable Government contract, and, to the extent applicable by the terms of the Government contract, the additional rights set forth in FAR 52.227-19, Commercial Computer Software License (December 2007). -
National Robotics Initiative (NRI)(Nsf11553)
This document has been archived and replaced by NSF 12-607. National Robotics Initiative (NRI) The realization of co-robots acting in direct support of individuals and groups PROGRAM SOLICITATION NSF 11-553 National Science Foundation Directorate for Computer & Information Science & Engineering Division of Information & Intelligent Systems Directorate for Social, Behavioral & Economic Sciences Directorate for Engineering Directorate for Education & Human Resources National Institutes of Health National Institute of Neurological Disorders and Stroke National Institute on Aging National Institute of Biomedical Imaging and Bioengineering National Center for Research Resources Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institute of Nursing Research U.S. Dept. of Agriculture National Institute of Food and Agriculture National Aeronautics and Space Administration Directorate for Education and Human Resources, Game Changing Technology Division Letter of Intent Due Date(s) (required) (due by 5 p.m. proposer's local time): October 01, 2011 October 1, Annually Thereafter Small Proposals December 15, 2011 December 15, Annually Thereafter Group Large Proposals Full Proposal Deadline(s) (due by 5 p.m. proposer's local time): November 03, 2011 November 3, Annually Thereafter Small Proposals January 18, 2012 January 18, Annually Thereafter Group Large Proposals IMPORTANT INFORMATION AND REVISION NOTES Public Briefings: One or more collaborative webinar briefings with question and answer functionality will -
Sarath Singapati Inter Process Communication in Android Master of Science Thesis
SARATH SINGAPATI INTER PROCESS COMMUNICATION IN ANDROID MASTER OF SCIENCE THESIS Examiner: Professor Tommi Mikkonen Examiner and thesis subject approved by The Faculty of Computing and Electrical Engineering on 7th March 2012 II ABSTRACT TAMPERE UNIVERSITY OF TECHNOLOGY Master’s Degree Programme in Information Technology SARATH SINGAPATI INTER PROCESS COMMUNICATION IN ANDROID Master of Science Thesis, 45 pages, 4 Appendix pages June 2012 Major: Software Systems Examiner: Professor Tommi Mikkonen Keywords: Android, Google, mobile applications, Process, IPC Google's Android mobile phone software platform is currently the big opportunity for application software developers. Android has the potential for removing the barriers to success in the development and sale of a new generation of mobile phone application software. Just as the standardized PC and Macintosh platforms created markets for desktop and server software, Android, by providing a standard mobile phone application environment, creates a market for mobile applications and the opportunity for applica- tions developers to profit from those applications. One of the main intentions of Android platform is to eliminate the duplication of functionality in different applications to allow functionality to be discovered and in- voked on the fly, and to let users replace applications with others that offer similar func- tionality. The main problem here is how to develop applications that must have as few dependencies as possible, and must be able to provide services to other applications. This thesis studies the Android mobile operating system, its capabilities in develop- ing applications that communicate with each other and provide services to other applica- tions. As part of the study, a sample application called “Event Planner”, has been devel- oped to experiment how Inter Process Communication works in Android platform, ex- plains how to implement, and use Inter Process Communication (IPC). -
Applying Machine Learning to Robotics WHITEPAPER
WHITEPAPER Credit: Pixabay Applying Machine Learning to Robotics TABLE OF CONTENTS MACHINE LEARNING - TOP MARKS FOR POTENTIAL MACHINE LEARNING SOLVES BUSINESS PROBLEMS CURRENT TECHNOLOGIES AND SUPPLIERS MACHINE LEARNING IN ROBOTICS: EXAMPLE 1 MACHINE LEARNING IN ROBOTICS: EXAMPLE 2 MACHINE LEARNING IN ROBOTICS: EXAMPLE 3 NEXT-GENERATION INDUSTRY WILL RELY ON MACHINE LEARNING roboticsbusinessreview.com 2 APPLYING MACHINE LEARNING TO ROBOTICS Advances in artificial intelligence are making robots smarter at pick-and- place operations, drones more autonomous, and the Industrial Internet of Things more connected. Where else could machine learning help? By Andrew Williams A growing number of businesses worldwide are waking up to the potentially transformative capabilities of machine learning - particularly when applied to robotics systems in the workplace. In recent years, the capacity of machine learning to improve efficiency in fields as diverse as manufacturing assembly, pick-and-place operations, quality control and drone systems has also gathered a great deal of momentum. Knowing that machine learning is improving also heightens awareness of great strides being made in artificial intelligence (AI), to the extent that the two technologies are often viewed interchangeably. Major recent advances in topics such as logic and data analytics, algorithm development, and predictive analytics are also driving AI’s growth. In this report, we’ll review the latest cutting-edge machine learning research and development around the globe, and explore some emerging applications in the field of robotics. MACHINE LEARNING - TOP MARKS FOR POTENTIAL A September 2017 report by Research and Markets predicts the global machine learning market will grow from $1.4 billion in 2017 to $8.81 billion by 2022, with a compound annual growth rate of 44.1%.