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Navigation and Control LabVIEW Robotics.ppt
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- 2. ni.com | NICONFIDENTIAL Navigation and Control with LabVIEW Robotics Alex Barp
- 3. 3 ni.com | NICONFIDENTIAL Robotics, Vision and Control • Prof. Peter Corke, QUT • Editor in Chief, IEEE Robotics and Automation Magazine • IEEE Fellow
- 4. 5 ni.com | NICONFIDENTIAL Robotics, Vision and Control and LabVIEW
- 5. 6 ni.com | NICONFIDENTIAL LabVIEW Robotics Module Graphical system design software framework Deployment to Real-Time and FPGA hardware IP for navigation, steering, kinematics, and more Connectivity to various sensors and actuators Tools for integrating text- based algorithms Physical Simulation of real-world applications
- 6. 8 ni.com Todays Session Topic •RVC Content 1. Introduction 2. Position / Orientation 3. Time and Motion 4. Mobile Vehicles 5. Navigation 6. Localization 2 3 4
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- 8. 10 ni.com A – Positionand Orientation
- 9. 11 ni.com A – Positionand Orientation
- 10. 12 ni.com A – 2DPosition and Orientation
- 11. 13 ni.com A – 2DPosition and Orientation
- 12. 14 ni.com A – 3DPosition and Orientation
- 13. 15 ni.com A – 3DPosition and Orientation
- 14. 16 ni.com A – 3DOrthogonal Rotation Matrix
- 15. 17 ni.com A- 3D OrthogonalRotation Matrix
- 16. 18 ni.com A – RotationMatrix RotX(pi/2)*RotY(pi/2) not equal to RotY(pi/2)*RotX(pi/2)
- 17. 19 ni.com A – 3Angles Representation • Euler Angle: • Eulerian type (Proper Euler Angles): o Repetition, but not successive, of rotations of a particular axis: XYX, XZX, YZY, YXY, ZXZ, ZYZ • Cardanian type (Tait-Bryan Angles): o Rotation of all 3 axis: XYZ, XZY, YXZ, YZX, ZXY, ZYX • Active (alibi) and Passive (alias) transformation • Intrinsic and Extrinsic For ZYZ
- 18. 20 ni.com A – 3Angles Representation
- 19. 21 ni.com A – 3Angles Representation • Roll-Pitch-Yaw (RPY):
- 20. 22 ni.com A – 3Angles Representation • Euler and RPY have “guimbal lock” problem: • Other representations to avoid this problem: • Two Vector Representation • Rotation of abitrary vector • Quaternion
- 21. 23 ni.com A – Twovector representation
- 22. 24 ni.com A – Rotationof Arbitrary Vector
- 23. 25 ni.com A – UnitQuaternions
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- 25. 27 ni.com B – TrajectoryGeneration • Smooth one-dimensional trajectories
- 26. 28 ni.com B – TrajectoryGeneration
- 27. 29 ni.com B – TrajectoryGeneration • Smooth one-dimensional trajectories with limits
- 28. 30 ni.com B – TrajectoryGeneration • Smooth multi-dimensional trajectories with limits
- 29. 31 ni.com B – TrajectoryGeneration • Interpolation of orientation in 3-D
- 30. 32 ni.com B – TrajectoryGeneration • Interpolation of orientation in Cartesian
- 31. ni.com | NICONFIDENTIAL Vehicle control
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- 34. 36 ni.com Control for MobileRobots “the process of directing (controlling) a vehicle so as to reach the intended destination (setpoint) based on its sensors measurements” Controller Gc(s) Plant G(s) Desired Value Process Output Process Input - + Error
- 35. 37 ni.com Type of vehiclecontrol
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- 40. 42 ni.com Moving to aline Moving towards a line defined by:
- 41. 43 ni.com Follow a path Followa circle:
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- 45. ni.com | NICONFIDENTIAL Robotic Navigation
- 46. 48 ni.com | NICONFIDENTIAL Navegation for Mobile Robots “the process of directing a vehicle so as to reach the intended destination” - IEEE Standard 172-1983
- 47. 49 ni.com | NICONFIDENTIAL Navigation – Reactive • Reactive Navigation “a goal oriented machine that can sense, plan and act” • Algorithms Examples: • Bug1 • Bug2 • Heading toward light • Line follow • Wall follow
- 48. 50 ni.com | NICONFIDENTIAL Navigation – Reactive example • Braitenberg Vehicles • Direct connection between sensor and motor • No internal map or planning
- 49. 51 ni.com | NICONFIDENTIAL Navigation – Reactive example • Bug Vehicles • Sense goal proximity, like Braitenberg • But use state logic and have memory • Bug2 Algorithm
- 50. 52 ni.com | NICONFIDENTIAL Navigation – Map-based Planning • Map-based Navigation “a goal oriented machine that can sense, plan and act” • Key Concepts • Occupancy Grid • Start & Goal • Cost • Assumptions Robot occupies 1 cell and can move to any neighbor cell Robot knows goal and map and can compute its pat
- 51. 53 ni.com | NICONFIDENTIAL Navigation – Map Planning Distance Transform • Distance Transform:
- 52. 54 ni.com | NICONFIDENTIAL Navigation – Map Planning methods • Algorithms Examples: • A* • D* • Dijkstra • Voronoi Map A* Dijkstr a
- 53. 55 ni.com | NICONFIDENTIAL Navigation – Map Planning methods • Algorithms Examples: • A* • D* • Dijkstra • Voronoi Map A* Dijkstr a
- 54. 56 ni.com | NICONFIDENTIAL Navigation – Map Planning Efficiency • It is important to remember computational efficiency/cost • A* Algorithm • Will find the optimal (admissible) solution - if it exists • Finding the optimal solution has a cost Admissible A* ε-Admissible A*
- 55. 57 ni.com | NICONFIDENTIAL Navigation – Map Planning Efficiency • It is important to remember computational efficiency/cost • A* Algorithm • Will find the optimal (admissible) solution - if it exists • Finding the optimal solution has a cost Admissible A* ε-Admissible A*
- 56. 58 ni.com | NICONFIDENTIAL Navigation – Map Planning D* • D* Overview • Maintains list of nodes like A* and Dijkstra • But, has the ability to incrementally replan o Helps when encountering new obstacles • D* Applications
- 57. 59 ni.com | NICONFIDENTIAL Navigation – Map Planning Voronoi • Voronoi Overview • Create a network of path free obstacles • Reduce the cost of recalculating a path - comparatively
- 58. 60 ni.com | NICONFIDENTIAL Navigation – Overall Recommendations • How does everything compare? • R – Reactive • M – Map-based Method Complexity Advantage Disadvantage BraitenbergR Low Simple w/ sensorimotor No logic Bug2R Low Simple w/ state logic No planning DijkstraM Medium Brute force calculation “slow” calculation A*M High Optimal solution Planning cost D*M High Incremental replanning Planning cost VoronoiM High Efficient recalculation Less optimal path
- 59. 61 ni.com | NICONFIDENTIAL Navigation – Bug2 LabVIEW Examples • Reactive Method Example • Bug2 in LabVIEW
- 60. 62 ni.com | NICONFIDENTIAL Navigation – D* LabVIEW Examples • Map-based Method Example • D* in LabVIEW
- 61. ni.com | NICONFIDENTIAL Localization
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- 64. 66 ni.com | NICONFIDENTIAL Navigation – What comes next? • Navigation is only one piece of the puzzle • What about, Sensors? Localization? Deployment? Mobility? Simulation?
- 65. 67 ni.com | NICONFIDENTIAL Navigation – What comes next? • Witty End slide…
- 66. 68 ni.com | NICONFIDENTIAL <Blank>
Editor's Notes
- #3 Presentation Title: Designing an Intelligent Navigation Algorithm with LabVIEW Robotics Presentation Abstract: A discussion on implementing Robotic Navigation, Mobility, and Localization using LabVIEW. The presentation focuses on topics discussed in Peter Corke's Robotics, Vision & Control that are solved using LabVIEW.
- #4 So, how is it that you approach these challenges and best utilize your tools? Well, you must first have a fundamental understanding of the task at hand. Peter Corkes Robotics, Vision and Control textbook is a leading resource in the field of robotics. One of the major strengths of Corkes RVC is that it not only discusses theory but it also focuses heavily on real world considerations and implementation. http://staff.qut.edu.au/staff/corkep/
- #5 **history of robotics **focus on the challenges we currently face with robotics 1495 – Leonardo de Vinci Humanoid 1900 - Nikolai Tesla RC 1921 - ROBOT (forced labour) Czech Karel Capek 1940 – Asimov outlines the three laws of robotics and coins the term robotics 1970 - German based company KUKA built the world's first industrial robot with six electromechanically driven axes, known as FAMULUS. 1980 – Deep Thought 1990 – RoboTuna, Deep Blue http://www.prweb.com/releases/service_robotics/professional_robots/prweb4240924.htm http://tommytoy.typepad.com/tommy-toy-pbt-consultin/2011/06/outlook-for-robotics-and-automation-for-2011-and-beyond-are-excellent-says-expert-.html
- #6 - Our goal is to marry the two components together. Take the pragmatic approach that RVC provides and show how LabVIEW can be used as a tool to solve complex robotics problems. <button press for animation> - Together, these resources will help prepare scientists and engineers to approach the grand challenges that we face in robotics.
- #7 - As many of you I’m sure are aware, our flagship software LabVIEW has enabled engineers to overcome many challenging tasks - The LabVIEW Robotics toolkit, specifically, has introduced IP that helps make robotic integration with software even easier However, no matter how many powerful tools you have in the shed, it doesn’t mean you know how to best take advantage of them. ------------ Within LabVIEW, there is a specialized toolkit called the LabVIEW Robotics Module. It follows the same graphical system design framework so if you know LabVIEW already, you will be able to utilize this module immediately. It allows integration with text-based algorithm/language such as .m files, but what makes the Robotics Module unique is the following features. It contains various IP for autonomous navigation, steering, and kinematics. In fact, we work with Prof. Peter Corke and integrate his robotics toolbox into LabVIEW It provides drivers for various sensors and actuators to help to get started with your application. These drivers are very unique and we will talk more about them in a little bit. Of course, everything that you’ve developed can be deployed to Real-time and FPGA embedded hardware 3. Lastly, with LabVIEW Robotics 2012, we introduced the Robotics Simulator that allows you to import your CAD model and create dynamic simlation of your robotics system. You can modify the environment and see how robust your algorithm is in different scenarios.
- #8 So what is our approach and what do we plan to develop?
- #9 - RVC covers a wide range of topics. However, today I want to focus specifically on the Navigation section. First I will walk through the theory and techniques presented in the book, and then show you how we then implement the discussed algorithms in LabVIEW.
- #37 It is natural for us to approach navigation from a human perspective. We build maps, landmarks, signs and carry references to the world around us. However, in robotics this technique Navigation for robots does not match
- #47 Any given robotics system has three ---- We can interpret it a bit deeper by the pryimad on the right. If I were to think back to my graduate work in bipedal walking robot, I spent roughly 80% of my time developing drivers for sensors and actuators, figuring out how to interact with the real world, and translating my control algortihm into mechine code that I can deploy to the hardware that I was using. In reality, I only spent about 20% of my time on innovating algorithms and the cognition code. But shoudn’t that be the other way around? In order to really accelerate robotics, scientists and reseachers should be spending majority of their time on innovating algorithms and methods. And this is how NI see itself contributing to the robotics community and promote innovation.
- #49 It is natural for us to approach navigation from a human perspective. We build maps, landmarks, signs and carry references to the world around us. However, in robotics this technique Navigation for robots does not match
- #50 Algorithm Examples – *Line following *Heading toward a light – use a senseor to guide a vehicle toward a light source *Heading toward a sound – use sensors to guide the vehicle to a sound source. Following intensity *Wall follow – Follow a wall until you reach your goal *Bug1 - *Bug2 - Physical Examples- *Roomba
- #51 Braitenberg vehicles are a classification of autonomous vehicles that navigate simply based on sensorimotor behavior. The system stores no internal map of the environment it traverses but simply reacts to the intensity of sensor measurements. A good example of this is a robot that searches for a light intensity, chemical concentration, or sound source.
- #53 The second type of navigation technique is called map-based planning. Now, instead of only sensing and acting to reach our goal, we are planning as well.
- #54 Distance Transform – The distance transform, is a representation of the distance from the goal point to any other point on the map. The color gradient represents the total number of steps or distance to the goal. <button press for animation> Another helpful way to view the distance transform is to actually view the 3rd dimensional gradient as transitioning in space as well as color. You can see that the path of least distance from the goal to any other point is also represented by moving down a hill. Although a Distance Transform does give us solutions for every point on the grid, for larger grids this approach is very inefficient. This is because of the time needed in order to calculate a solution for every point on the entirety of the grid.
- #55 In order to overcome the computational complexity with generating a solution for every cell on the grid, different algorithms have been developed for higher efficiency. <button press to make pictures appear> I wanted to give a quick overview of two example algorithms to show how they perform their calculations. Specifically we will look at Dijkstra’s and A*.
- #56 <Had to add the gif’s on another slide because they gifs are loaded into memory and begin to step through once they are loaded into memory with the slide, regardless of whether or not they are visible> In both cases the filled nodes represent explored sets and the unfilled nodes represent cells that remain to be explored. Dijkstras algorithm works by uniformly exploring each node (uniform cost assumption) in a relatively circular pattern from the start point which is usually referred to as a propagating wavefront. A* searches a bit different. It will first search in a straight path from the start node to the goal node. Upon hitting an obstacle, the algorithm then calculates alternative routes around the obstacle.
- #57 Admissable = never overestimates the solution Illustration of weighted A* search with , for faster, but sub-optimal path planning.
- #58 Admissable = never overestimates the solution Illustration of weighted A* search with , for faster, but sub-optimal path planning.
- #59 The key feature of D* is that it supports incremental replanning. This is important if, while we are moving, we discover that the world is different to our map. If we discover that a route has a higher than expected cost or is completely blocked we can incrementally replan to find a better path. The incremental replanning has a lower computational cost than completely replanning as would be required using the distance transform method just discussed.