建構自主性機器人的利器 LabVIEW for Robotics 功能介紹

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LabVIEW Robotics Module 函式庫介紹

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  • Different Robotics Applications: Robots are everywhereBig industry in the future. Robots are complex, so we need better tools to help us design.
  • Let’s start with “sense”, the component simply made up of sensors. There’s a huge range of sensor types, from simple IR sensors to more sophisticated stereo vision systems and laser-range finders.Questions you should understand for your system:What type of perception/sensor resolution do you need for your application? A lot depends on how much autonomy will be in your system and what type of environment your device will be moving within. Will your system be 100% autonomous – or mostly tele-op? Will your robot be outdoors and indoors – requiring special purpose coordinate sensors beyond a standard GPS? Will your robot be in smokey rooms/situations where standard vision should be replaced with sonar and IR sensors?Looking at this sample list of sensors – we can get a general idea of the type of data you can easily extract from your robot system’s environment. [transition] Let’s look at a simple photoresistor first…
  • The “think” area of s,t,a is where most of the research resides today…from planning and navigation to machine learning and AI universities, research labs and military groups are working hard to find the right kind of “think” for their robotic needs.
  • Finally, let’s discuss the act component of our STA system. Here you find manipulators such as robotic arms and grippers as well as drive / locomotion systems. Some really interesting research is emerging here in new types of movement – even so much as to look at nature for interesting new ways to get around from snakes to fish to worms resulting in exciting biomimetic designs.This section too could be many, many presentations in itself because of the complexity of drive systems, mechanical parts design and manipulator dynamics. [transition] However, we do not have time to discuss all of the details – so let’s briefly cover an essential and sometimes confusing component: motors.
  • Kinematics is the most common motion control in robotics. Kinematics describes the motion of objects without consideration of the causes leading to the motion. In thinking about robotic arms – with fewer “moving parts than mobile robots” (no pun intended), forward Kinematics enables you to determine where the robot’s hand will be if all joint variables are known. While inverse Kinematics will enable you to calculate what each joint variable must be if you desire the hand to be located at a particular point and have a particular orientation.There are some great software packages on the market today that provide robust “act” algorithms like inverse kinematics including Energid, AgilePlanet, SolidWorks and more. In an ideal world, your algorithm design or mechanical design package would easily connect to your implementation tool – to make the transition from theory to prototype smooth. Currently, there are connections in-work between Energid, AgilePlanet and SolidWorks with NI LabVIEW – making this step much easier for you as a designer.
  • And THAT (the demo) scales very well into unmanned vehicles SUCH AS DARPA VTOf course, DARPA is not our ONLY success in robotics…. Make sure you draw attention yourself to the fact that Nexans is NOT unmanned, and then talk about FIRST being (see below)FIRST is an IMPLIED industry success > Validates us as a solution to Mentors, observing engineer professionals, (segue with = and we’ve been playing on THAT strategy in a number of arenas….)
  • Testament that there are design tools and commercially available technologies out there that enable the roboticists to focus on robotics challenges
  • 这是获得第三名Vtech的参赛车辆和第四名MIT的参赛车辆无人驾驶系统比较。使用LabVIEW和NI cRIO开发的无人驾驶平台与C语言40核计算机平台相比,平台集成度更高,接线简洁,逻辑拓扑结构清晰,更易于开发和调试。并且需要指出的是获得第三名的VirTech参赛队员是15名机械系学生,而MIT有超过40位计算机系学生参与了这个赛事的准备,并且MIT花费的经费和预算也更高。因此赛后连MIT领队老师承认LabVIEW是更适合于智能移动机器人开发的平台。I didn’t know you could design a vehicle that simple…. Return with ‘I didn’t know you could make a DARPA vehicle, THAT complicated!” And after VT came in 3rd and MIT 4th or 5th… (smile) David Barrett of course was forced to take a bit closer look…
  • For the 2009 competition, FIRST Robotics will standardize on LabVIEW & cRIO for their next generation Mobile Device ControllerImpact of the New Controller Key Points: Next-generation control system enables students to build more sophisticated robotics systemsSolve more complex challengesHands-on learning helps connect theory and the real world.Industry-standard technology means students gain knowledge for university studies and professional careersDiscuss that NI will provide the product that will be the standard for FTC and FRC
  • 建構自主性機器人的利器 LabVIEW for Robotics 功能介紹

    1. 1. Graphical System Design for Robotics Applications<br />National Instruments 美商國家儀器<br />行銷部技術經理 吳維翰<br />
    2. 2. Mobile Service Robot<br />Underwater Robot<br />Autonomous Forklift<br />Military Robot<br />Autonomous Ground Vehicles<br />Space Rover Robots<br />
    3. 3. A robot simplified…<br />
    4. 4. In reality…<br />Really messy<br />Lots of stuff to integrate<br />And you don’t even know if it works<br />Need to simply the problem<br />Levels of abstraction<br />Don’t get lost in the “details”<br />
    5. 5. The graphical approach<br />Vision<br />Ultrasonic<br />Microphone<br />GPS<br />LIDAR<br />Object Tracking<br />Avoidance<br />SLAM<br />Navigation<br />PID<br />PWM<br />Closed-loop control<br />Fuzzy Control<br />Adaptive Control<br />
    6. 6. 何謂 LabVIEW?<br />圖形化編程環境<br />業界量測與自動化標準<br />豐富函式庫,包含:<br />視覺檢測<br />運動控制<br />資料擷取<br />儀器控制<br />進階運算<br />報表產生<br />和更多 …<br />整合週邊軟硬體和其他工具<br />
    7. 7. LabVIEW圖形化編程環境<br />
    8. 8. “What is” LabVIEW Robotics 2009?<br />
    9. 9. IP Offering<br />Sensor Integration<br />Algorithms<br />Action and control<br />Robotic Arm Library<br />Others<br />Connectivity, FPGA IP<br />
    10. 10.
    11. 11.
    12. 12. Infrared<br />Sonar<br />LIDAR<br />Electronic Compass<br />GPS<br />IMU<br />
    13. 13. Hokuyo Sensor 1-2-3<br />DEMO<br />
    14. 14.
    15. 15.
    16. 16. Occupancy Grid<br />A*, AD*<br />Voronoi Diagram<br />Vector Field Histogram (VFH)<br />
    17. 17. Vector Field Histogram 1-2-3<br />DEMO<br />
    18. 18. Vision Capabilities<br />Color tracking<br />Target tracking<br />Customized vision analysis<br />NI Vision Development Module<br />
    19. 19.
    20. 20.
    21. 21. Mecanum Steering<br />Input: <br />Wheel Radius<br />Separation Distance<br />Ackerman Steering<br />Output: <br />Wheel velocities<br />
    22. 22. Robotic Arm Functions<br />Serial arm definition<br />Jacobian calculation<br />Torque calculation<br />Kinematics<br />Forward Kinematics<br />Inverse Kinematics<br />3D Display<br />DEMO<br />
    23. 23. Other Functions<br />Connectivity<br />MobileRobots, Skilligent API<br />FPGA Digital Interfaces<br />I2C<br />SPI<br />RS-232<br />NEMA GPS decoding<br />Examples and guides<br />
    24. 24. Hardware Compatibility<br />
    25. 25. Case Studies<br />DARPA Challenge<br />RoMeLaDARwIn<br />LEGO<br />FIRST FRC<br />Other applications<br />cRIO named “BestDesign Platform”<br />
    26. 26. Case Study: Virginia Tech/TORC Technologies Develop Odin<br />
    27. 27. The Background of Odin<br />Created by Team Victor Tango<br />Development partnered between Virginia Tech and TORC Technologies<br />Won third place at the 2007 DARPA Urban Challenge<br />Vehicle: 2005 Ford Escape Hybrid<br />
    28. 28.
    29. 29. DARPA Challenge – 3rd and 4th place<br />MIT :<br />C , 40-core Linux system<br />Team: 40Comp Sci Students<br />Virginia Tech:<br />LabVIEW, PXI, CompactRIO<br />Team: 15 Mech. Eng. Students<br />
    30. 30. Virginia Tech: DARwInDynamic Anthropomorphic Robot with Intellligence<br />Theory<br />Design<br />Prototype<br />Deploy<br />
    31. 31.
    32. 32. Example: LEGO NXT Vision Tracking<br />
    33. 33. FIRST Robotics Competition Selects National Instruments CompactRIO for Next-Generation Robot Control System <br />Over 50,000 High School Students to use cRIO, powered by NI LabVIEW<br />
    34. 34. 淡江大學: 全國首創機器人研究所<br />進階<br />初級<br />大學一年級:<br />LabVIEW & NXT 課程<br />提供學員創意平臺,快速學習<br />LabVIEW操作和概念。<br />大學三年級,研究所:<br />NI CompactRIO 用於機器人設計課程<br />快速原型開發和演算法測試<br />歷年FIRA世界盃機器人足球賽得獎組<br />指導教授:電機系主任 翁慶昌<br />
    35. 35. 台灣大學機械系: <br />7-DOF人形機器手臂<br />將LabVIEW程式下載至 sbRIO以達成低耗電,嵌入式控制<br />使用 CompactRIO進行初步演算法驗證<br />指導教授:機械系主任 黃漢邦<br />
    36. 36. 業界案例: 機器人整合<br />微星科技創新前瞻研究中心<br />智慧型影音互動導覽/服務機器人<br />內建紅外線與超音波偵測系統<br />計算軌跡並自主性避開障礙<br />“在導入 LabVIEW 之前,整個機器人專案皆使用 C 語言在做開發,但這之後,我們開始嘗試並大膽採用 LabVIEW 。主要的原因是 LabVIEW 的相容性高,甚至可以整合大部分的語言程式,不會寫 C 的同事可以藉由 LabVIEW 來設計所負責的專案。”<br />微星科技洪士哲副理<br />
    37. 37. 從小學到業界的圖形化機器人設計平臺<br />LabVIEW FPGA Single-Cycle Timed Loop<br />LabVIEW Simulation Loop<br />LabVIEW Real-Time Timed Loop<br />LabVIEW While Loop<br />LEGO MINDSTORMS® NXT Loop<br />LEGO® Education WeDo Loop<br />
    38. 38. Questions? Comments?Thank you for your time!<br />吳維翰 / John<br />行動電話: 0910-611-802<br />電子郵件: wei-han.wu@ni.com<br />
    39. 39.
    40. 40. Holonomic Robot Case Study<br />
    41. 41. Case study: holonomic robot<br />Rotation matrix calculation<br />Simultaneous PID for 4 motors<br />Laser rangefinder for obstacle avoidance<br />Navigation planning<br />Wireless communicationand HMI<br />In order to do this, you MUST look at the entire system<br />
    42. 42. Acquiring Hokuyo Sensor data<br />PID closed-loop control, 4 motors<br />Rotation Matrix Calculation<br />Obstacle Avoidance<br />Wireless control, USB joystick interface (PC)<br />
    43. 43. NTU example<br />
    44. 44. The Challenge<br />Robots start at “Start Point”<br />Navigate to Waypoint 1. Wait for 5 seconds.<br />Navigate to Waypoint 2. Wait for 5 seconds.<br />Navigate back to “Start Point”<br />Penalties:<br />Touch cone<br />Touch wall<br />Reset robot<br />
    45. 45. NTU movie<br />
    46. 46. Results<br />13 student surveys<br />Q : 此實習課程有沒有讓您對機器人技術系統整合更加了解?<br />13 responded yes<br />Other feedback:<br />易上手,programming easy<br />將所有部份整合一起時,資源安排和流程分配需要多加考慮<br />

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