四足機(jī)器人結(jié)構(gòu)設(shè)計

喜歡這套資料就充值下載吧。資源目錄里展示的都可在線預(yù)覽哦。下載后都有，請放心下載，文件全都包含在內(nèi)，圖紙為CAD格式可編輯，有疑問咨詢QQ:414951605 或 1304139763p 畢業(yè)設(shè)計(論文)任務(wù)書題 目： 四足機(jī)器人結(jié)構(gòu)設(shè)計 一、畢業(yè)設(shè)計（論文）的內(nèi)容四足機(jī)器人作為仿生機(jī)器人的一種形式，在國內(nèi)外得到了廣泛的研究。四足機(jī)器人步行腿具有多個自由度, 落足點(diǎn)是離散的, 故能在足尖點(diǎn)可達(dá)域范圍內(nèi)靈活調(diào)整行走姿態(tài), 并合理選擇支撐點(diǎn), 具有更高的避障和越障能力。本任務(wù)要求從模仿四足哺乳動物行走的角度思考，設(shè)計出四條腿具有相應(yīng)自由度的四足機(jī)器人。二、畢業(yè)設(shè)計（論文）的要求與數(shù)據(jù)1. 掌握機(jī)械設(shè)計原則及機(jī)器人機(jī)構(gòu)設(shè)計方法；2. 了解四足機(jī)器人運(yùn)動機(jī)構(gòu)的運(yùn)動學(xué)特征；3. 本任務(wù)要求從模仿四足哺乳動物行走的角度思考，設(shè)計出四條腿具有相應(yīng)自由度的四足機(jī)器人機(jī)械機(jī)構(gòu)。要求：a. 通過觀察分析確定四足機(jī)器人各部分的自由度；b. 機(jī)器人的規(guī)模尺寸可自行確定，要求四肢與軀干協(xié)調(diào)；c. 需要考慮驅(qū)動系統(tǒng)安裝問題；4. 完成四足機(jī)器人行走的3D運(yùn)動模擬。三、畢業(yè)設(shè)計（論文）應(yīng)完成的工作1、完成二萬字左右的畢業(yè)設(shè)計說明書（論文）；在畢業(yè)設(shè)計說明書（論文）中必須包括詳細(xì)的300-500個單詞的英文摘要；2、獨(dú)立完成與課題相關(guān)，不少于四萬字符的指定英文資料翻譯（附英文原文）；3、完成相關(guān)設(shè)計計算及機(jī)械設(shè)計圖（要求繪圖工作量折合A0圖紙3張以上）。4、完成3D模擬運(yùn)動仿真，提交設(shè)計文件。四、應(yīng)收集的資料及主要參考文獻(xiàn)1 張錦榮,趙茜. 四足機(jī)器人結(jié)構(gòu)設(shè)計與運(yùn)動學(xué)分析J. 現(xiàn)代制造工程. 2009(08):146-149.2 孫群,桑春蕾,林寶龍. 四足機(jī)器人機(jī)械系統(tǒng)虛擬設(shè)計及轉(zhuǎn)彎機(jī)構(gòu)理論分析J. 機(jī)械設(shè)計與制造. 2009(08):183-185.3 何冬青，馬培蓀，曹曦等. 四足機(jī)器人對角小跑起步姿態(tài)對穩(wěn)定步行的影響J. 機(jī)器人，2004，26（6）：5295324 黃博，王鵬飛，孫立寧. 基于行為模式的復(fù)合運(yùn)動方式四足機(jī)器人研究J.中國機(jī)械工程，2007，18（18）：215921625 陳學(xué)東，郭鴻勛，渡邊桂吾. 四足機(jī)器人爬行步態(tài)的正運(yùn)動學(xué)分析J. 機(jī)械工程學(xué)報，2003，39（2）： 8126 聞邦椿.機(jī)械設(shè)計手冊M.北京:機(jī)械工業(yè)出版社,2010.7 秦大同,謝里陽.現(xiàn)代機(jī)械設(shè)計手持M.化學(xué)工業(yè)出版社,2011.8 張軒，王停戰(zhàn)，郭旭偉. AutoCAD 2007機(jī)械制圖基礎(chǔ)與工程范例M.北京：清華大學(xué)出版社，2008.9 鄭文緯，吳克堅(jiān)主編.機(jī)械原理（第七版）M.北京：高等教育出版社，1997.10 Siegwart, R., Nourbakhsh, I.R.: Introduction to Autonomous Mobile RobotsM. Cambridge: The MIT Press, 2004.5、 試驗(yàn)、測試、試制加工所需主要儀器設(shè)備及條件1、計算機(jī)一臺；2、相關(guān)設(shè)計軟件。 Roland SIEGWARTIllah R. NOURBAKHSHIntroduction toAutonomous Mobile RobotsIntelligent Robotics and Autonomous AgentsRonald C. Arkin, editorRobot Shaping: An Experiment in Behavior Engineering, Marco Dorigo and Marco Colombetti, 1997Behavior-Based Robotics, Ronald C. Arkin, 1998Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer, Peter Stone, 2000Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines,Stefano Nolfi and Dario Floreano, 2000Reasoning about Rational Agents, Michael Wooldridge, 2000Introduction to AI Robotics, Robin R. Murphy, 2000Strategic Negotiation in Multiagent Environments, Sarit Kraus, 2001Mechanics of Robotic Manipulation, Matthew T. Mason, 2001Designing Sociable Robots, Cynthia L. Breazeal, 2002Introduction to Autonomous Mobile Robots, Roland Siegwart and Illah R. Nourbakhsh, 2004Roland Siegwart and Illah R. NourbakhshA Bradford Book The MIT PressCambridge, Massachusetts London, England 2004 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechan- ical means (including photocopying, recording, or information storage and retrieval) without permis- sion in writing from the publisher.This book was set in Times Roman by the authors using Adobe FrameMaker 7.0. Printed and bound in the United States of America.Library of Congress Cataloging-in-Publication DataSiegwart, Roland.Introduction to autonomous mobile robots / Roland Siegwart and Illah Nourbakhsh.p. cm. (Intelligent robotics and autonomous agents)“A Bradford book.”Includes bibliographical references and index. ISBN 0-262-19502-X (hc : alk. paper)1. Mobile robots. 2. Autonomous robots. I. Nourbakhsh, Illah Reza, 1970 . II. Title. III. Series.TJ211.415.S54 2004629.892dc222003059349To Luzia and my children Janina, Malin and Yanik who give me their support and freedom to grow every day RSTo my parents Susi and Yvo who opened my eyes RS To Marti who is my love and my inspiration IRNTo my parents Fatemeh and Mahmoud who let me disassemble and investigate everything in our home IRNSlides and exercises that go with this book are available on:http:/www.mobilerobots.orgContentsAcknowledgmentsxiPrefacexiii1 Introduction11.1 Introduction11.2 An Overview of the Book102 Locomotion132.1 Introduction132.1.1 Key issues for locomotion162.2 Legged Mobile Robots172.2.1 Leg configurations and stability182.2.2 Examples of legged robot locomotion212.3 Wheeled Mobile Robots302.3.1 Wheeled locomotion: the design space312.3.2 Wheeled locomotion: case studies383 Mobile Robot Kinematics473.1 Introduction473.2 Kinematic Models and Constraints483.2.1 Representing robot position483.2.2 Forward kinematic models513.2.3 Wheel kinematic constraints533.2.4 Robot kinematic constraints613.2.5 Examples: robot kinematic models and constraints633.3 Mobile Robot Maneuverability673.3.1 Degree of mobility673.3.2 Degree of steerability713.3.3 Robot maneuverability72viiiContents3.4Mobile Robot Workspace743.4.1 Degrees of freedom743.4.2 Holonomic robots753.4.3 Path and trajectory considerations773.5Beyond Basic Kinematics803.6Motion Control (Kinematic Control)813.6.1 Open loop control (trajectory-following)813.6.2 Feedback control824 Perception894.1 Sensors for Mobile Robots894.1.1 Sensor classification894.1.2 Characterizing sensor performance924.1.3 Wheel/motor sensors974.1.4 Heading sensors984.1.5 Ground-based beacons1014.1.6 Active ranging1044.1.7 Motion/speed sensors1154.1.8 Vision-based sensors1174.2 Representing Uncertainty1454.2.1 Statistical representation1454.2.2 Error propagation: combining uncertain measurements1494.3 Feature Extraction1514.3.1 Feature extraction based on range data (laser, ultrasonic, vision-based ranging)1544.3.2 Visual appearance based feature extraction1635 Mobile Robot Localization1815.1 Introduction1815.2 The Challenge of Localization: Noise and Aliasing1825.2.1 Sensor noise1835.2.2 Sensor aliasing1845.2.3 Effector noise1855.2.4 An error model for odometric position estimation1865.3 To Localize or Not to Localize: Localization-Based Navigation versus Programmed Solutions1915.4 Belief Representation1945.4.1 Single-hypothesis belief1945.4.2 Multiple-hypothesis belief196Contentsix5.5 Map Representation2005.5.1 Continuous representations2005.5.2 Decomposition strategies2035.5.3 State of the art: current challenges in map representation2105.6 Probabilistic Map-Based Localization2125.6.1 Introduction2125.6.2 Markov localization2145.6.3 Kalman filter localization2275.7 Other Examples of Localization Systems2445.7.1 Landmark-based navigation2455.7.2 Globally unique localization2465.7.3 Positioning beacon systems2485.7.4 Route-based localization2495.8 Autonomous Map Building2505.8.1 The stochastic map technique2505.8.2 Other mapping techniques2536 Planning and Navigation2576.1 Introduction2576.2 Competences for Navigation: Planning and Reacting2586.2.1 Path planning2596.2.2 Obstacle avoidance2726.3 Navigation Architectures2916.3.1 Modularity for code reuse and sharing2916.3.2 Control localization2916.3.3 Techniques for decomposition2926.3.4 Case studies: tiered robot architectures298Bibliography305Books305Papers306Referenced Webpages314Interesting Internet Links to Mobile Robots314Index317AcknowledgmentsThis book is the result of inspirations and contributions from many researchers and students at the Swiss Federal Institute of Technology Lausanne (EPFL), Carnegie Mellon Univer- sitys Robotics Institute, Pittsburgh (CMU), and many others around the globe.We would like to thank all the researchers in mobile robotics that make this field so rich and stimulating by sharing their goals and visions with the community. It is their work that enables us to collect the material for this book.The most valuable and direct support and contribution for this book came from our past and current collaborators at EPFL and CMU. We would like to thank: Kai Arras for his con- tribution to uncertainty representation, feature extraction and Kalman filter localization; Matt Mason for his input on kinematics; Nicola Tomatis and Remy Blank for their support and assistance for the section on vision-based sensing; Al Rizzi for his guidance on feed- back control; Roland Philippsen and Jan Persson for their contribution to obstacle avoid- ance; Gilles Caprari and Yves Piguet for their input and suggestions on motion control; Agostino Martinelli for his careful checking of some of the equations and Marco Lauria for offering his talent for some of the figures. Thanks also to Marti Louw for her efforts on the cover design.This book was also inspired by other courses, especially by the lecture notes on mobile robotics at the Swiss Federal Institute of Technology, Zurich (ETHZ). Sincere thank goes to Gerhard Schweitzer, Martin Adams and Sjur Vestli. At the Robotics Institute special thanks go to Emily Hamner and Jean Harpley for collecting and organizing photo publica- tion permissions. The material for this book has been used for lectures at EFPL and CMU since 1997. Thanks go to all the many hundreds of students that followed the lecture and contributed thought their corrections and comments.It has been a pleasure to work with MIT Press, publisher of this book. Thanks to RonaldC. Arkin and the editorial board of the Intelligent Robotics and Autonomous Agents series for their careful and valuable review and to Robert Prior, Katherine Almeida, Sharon Deacon Warne, and Valerie Geary from MIT Press for their help in editing and finalizing the book.Special thanks also to Marie-Jo Pellaud at EPFL for carefully correcting the text files and to our colleagues at the Swiss Federal Institute of Technology Lausanne and Carnegie Mellon University.PrefaceMobile robotics is a young field. Its roots include many engineering and science disci- plines, from mechanical, electrical and electronics engineering to computer, cognitive and social sciences. Each of these parent fields has its share of introductory textbooks that excite and inform prospective students, preparing them for future advanced coursework and research. Our objective in writing this textbook is to provide mobile robotics with such a preparatory guide.This book presents an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual and cognitive layers that comprise our field of study. A collection of workshop proceedings and journal publications could present the new student with a snapshot of the state of the art in all aspects of mobile robotics. But here we aim to present a foundation a formal introduction to the field. The formalism and analysis herein will prove useful even as the frontier of the state of the art advances due to the rapid progress in all of mobile robotics sub-disciplines.We hope that this book will empower both the undergraduate and graduate robotics stu- dent with the background knowledge and analytical tools they will need to evaluate and even critique mobile robot proposals and artifacts throughout their career. This textbook is suitable as a whole for introductory mobile robotics coursework at both the undergraduate and graduate level. Individual chapters such as those on Perception or Kinematics can be useful as overviews in more focused courses on specific sub-fields of robotics.The origins of the this book bridge the Atlantic Ocean. The authors have taught courses on Mobile Robotics at the undergraduate and graduate level at Stanford University, ETH Zurich, Carnegie Mellon University and EPFL (Lausanne). Their combined set of curricu- lum details and lecture notes formed the earliest versions of this text. We have combined our individual notes, provided overall structure and then test-taught using this textbook for two additional years before settling on the current, published text.For an overview of the organization of the book and summaries of individual chapters, refer to Section 1.2.Finally, for the teacher and the student: we hope that this textbook proves to be a fruitful launching point for many careers in mobile robotics. That would be the ultimate reward.1 Introduction1.1 IntroductionRobotics has achieved its greatest success to date in the world of industrial manufacturing. Robot arms, or manipulators, comprise a 2 billion dollar industry. Bolted at its shoulder to a specific position in the assembly line, the robot arm can move with great speed and accu- racy to perform repetitive tasks such as spot welding and painting (figure 1.1). In the elec- tronics industry, manipulators place surface-mounted components with superhuman precision, making the portable telephone and laptop computer possible.Yet, for all of their successes, these commercial robots suffer from a fundamental dis- advantage: lack of mobility. A fixed manipulator has a limited range of motion that depends KUKA Inc. SIG Demaurex SAFigure 1.1Picture of auto assembly plant-spot welding robot of KUKA and a parallel robot Delta of SIG Demau- rex SA (invented at EPFL 140) during packaging of chocolates.Introduction9on where it is bolted down. In contrast, a mobile robot would be able to travel throughout the manufacturing plant, flexibly applying its talents wherever it is most effective.This book focuses on the technology of mobility: how can a mobile robot move unsu- pervised through real-world environments to fulfill its tasks? The first challenge is locomo- tion itself. How should a mobile robot move, and what is it about a particular locomotion mechanism that makes it superior to alternative locomotion mechanisms?Hostile environments such as Mars trigger even more unusual locomotion mechanisms (figure 1.2). In dangerous and inhospitable environments, even on Earth, such teleoperated systems have gained popularity (figures 1.3, 1.4, 1.5, 1.6). In these cases, the low-level complexities of the robot often make it impossible for a human operator to directly control its motions. The human performs localization and cognition activities, but relies on the robots control scheme to provide motion control.For example, Plustechs walking robot provides automatic leg coordination while the human operator chooses an overall direction of travel (figure 1.3). Figure 1.6 depicts an underwater vehicle that controls six propellers to autonomously stabilize the robot subma- rine in spite of underwater turbulence and water currents while the operator chooses posi- tion goals for the submarine to achieve.Other commercial robots operate not where humans cannot go but rather share space with humans in human environments (figure 1.7). These robots are compelling not for rea- sons of mobility but because of their autonomy, and so their ability to maintain a sense of position and to navigate without human intervention is paramount.Figure 1.2The mobile robot Sojourner was used during the Pathfinder mission to explore Mars in summer 1997. It was almost completely teleoperated from Earth. However, some on-board sensors allowed for obstacle detection. (http:/ranier.oact.hq.nasa.gov/telerobotics_page/telerobotics.shtm). NASA/JPLFigure 1.3Plustech developed the first application-driven walking robot. It is designed to move wood out of the forest. The leg coordination is automated, but navigation is still done by the human operator on the robot. (http:/www.plustech.fi). Plustech.Figure 1.4Airduct inspection robot featuring a pan-tilt camera with zoom and sensors for automatic inclination control, wall following, and intersection detection (http:/asl.epfl.ch). Sedirep / EPFL.Figure 1.5Picture of Pioneer, a robot designed to explore the Sarcophagus at Chernobyl. Wide World Photos.Figure 1.6Picture of recovering MBARIs ALTEX AUV (autonomous underwater vehicle) onto the Icebreaker Healy following a dive beneath the Arctic ice. Todd Walsh 2001 MBARI. Figure 1.7Tour-guide robots are able to interact and present exhibitions in an educational way 48, 118, 132, 143,. Ten Roboxes have operated during 5 months at the Swiss exhibition EXPO.02, meeting hun- dreds of thousands of visitors. They were developed by EPFL 132 (http:/robotics.epfl.ch) and com- mercialized by BlueBotics (http:/www.bluebotics.ch).Figure 1.8Newest generation of the autonomous guided vehicle (AGV) of SWISSLOG used to transport motor blocks from one assembly station to another. It is guided by an electrical wire installed in the floor. There are thousands of AGVs transporting products in industry, warehouses, and even hospitals. Swisslog.frontbackFigure 1.9HELPMATE is a mobile robot used in hospitals for transportation tasks. It has various on-board sen- sors for autonomous navigation in the corridors. The main sensor for localization is a camera looking to the ceiling. It can detect the lamps on the ceiling as references, or landmarks (http:/ www.pyxis.com). Pyxis Corp.Figure 1.10BR 700 industrial cleaning robot (left) and the RoboCleaner RC 3000 consumer robot developed and sold by Alfred Krcher GmbH & Co., Germany. The navigation system of BR 700 is based on a very sophisticated sonar system and a gyro. The RoboCleaner RC 3000 covers badly soiled areas with a special driving strategy until it is really clean. Optical sensors measure the degree of pollution of the aspirated air (http:/www.karcher.de). Alfred Krcher GmbH & Co.Figure 1.11PIONEER is a modular mobile robot offering various options like a gripper or an on-board camera. It is equipped with a sophisticated navigation library developed at SRI, Stanford, CA (Reprinted with permission from ActivMedia Robotics, http:/www.MobileRobots.com).Figure 1.12B21 of iRobot is a sophisticated mobile robot with up to three Intel Pentium processors on board. It has a large variety of sensors for high-performance navigation tasks (http:/www.irobot.com/rwi/). iRobot Inc.Figure 1.13KHEPERA is a small mobile robot for research and education. It is only about 60 mm in diameter. Various additional modules such as cameras and grippers are available. More then 700 units had already been sold by the end of 1998. KHEPERA is manufactured and distributed by K-Team SA, Switzerland (http:/www.k-team.com). K-Team SA.For example, AGV (autonomous guided vehicle) robots (figure 1.8) autonomously deliver parts between various assembly stations by following special electrical guidewires using a custom sensor. The Helpmate service robot transports food and medication throughout hospitals by tracking the position of ceiling lights, which are manually specified to the robot beforehand (figure 1.9). Several companies have developed autonomous clean- ing robots, mainly for large buildings (figure 1.10). One such cleaning robot is in use at the Paris Metro. Other specialized cleaning robots take advantage of the regular geometric pat- tern of aisles in supermarkets to facilitate the localization and navigation tasks.Research into high-level questions of cognition, localization, and navigation can be per- formed using standard research robot platforms that are tuned to the laboratory environ- ment. This is one of the largest current markets for mobile robots. Various mobile robot platforms are available for programming, ranging in terms of size and terrain capability. The most popular research robots are those of ActivMedia Robotics, K-Team SA, and I- Robot (figures 1.11, 1.12, 1.13) and also very small robots like the Alice from EPFL (Swiss Federal Institute of Technology at Lausanne) (figure 1.14).Although mobile robots have a broad set of applications and markets as summarized above, there is one fact that is true of virtually every successful mobile robot: its design involves the integration of many different bodies of knowledge. No mean feat, this makes mobile robotics as interdisciplinary a field as there can be. To solve locomotion problems, the mobile roboticist must understand mechanism and kinematics; dynamics and control theory. To create robust perceptual systems, the mobile roboticist must leverage the fields of signal analysis and specialized bodies of knowledge such as computer vision to properlyemploy a multitude of sensor technologies. Localization and navigation demand knowl- edge of computer algorithms, information theory, artificial intelligence, and probability theory.Figure 1.15 depicts an abstract control scheme for mobile robot systems that we will use throughout this text. This figure identifies many of the main bodies of knowledge associ- ated with mobile robotics.This book provides an introduction to all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques. The intended audience is broad, including both undergraduate and graduate students in intro- ductory mobile robotics courses, as well as individuals fascinated by the field. While not absolutely required, a familiarity with matrix algebra, calculus, probability theory, and computer programming will s