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robotlocalization_02
- 1. Robot Localization Péter Borkuti 2014. 08 What is it(1) Motivation(7) Introduction(4) Maps(4) Localization algorithm I.(9) Localization algorithm II.(15) Rotary encoders(17) Demonstration (5 minutes)
- 3. Robot Localization ● Definition Localization is the ability for a machine to locate itself in it's world ● Usage – Autonomous car – Moving robots
- 5. Robot Localization Motivation 3 short videos • The 6th Day • Google self driving car • Google self driving car testing
- 6. What's all the fuss about localization?
- 7. GPS ● Global Positioning System ● Issues?
- 8. GPS ● Global Positioning System ● Accuracy – 10 m ● Jamming, interferences, disturbances ● Satellites must be seen ● Service outages
- 9. GPS accuracy – 10 m self driving car
- 10. GPS accuracy – 10 m self driving car
- 11. Why Localization Is Important? ● Autonomous car – crashes ● Robot in a factory – breaks everything ● Robot in a hospital – gives pills to the wrong patient ● ...
- 13. Two Questions ● Imagine – you woke up in an unknown city – You can not speak to anybody – You have to go to an address in that city
- 14. Two Questions ● What does the World look like?What does the World look like? ● Where I am?Where I am?
- 15. Answers ● What does the World look like? – Map ● Where I am? – Localization method
- 16. Answers ● What does the World look like? – Map ● Where I am? – Localization method ● Find your place on Map based on sensed data – Sensors ● Sensing surroundings
- 17. We will learn about Maps Localization algorithms
- 19. Theoretical Example Localization with elevation data
- 20. Simplify the map ● Elevation map – Topography: graphic representation of a feature of points of a map – Geographic topography: the points are geographic points – Elevation map: the feature is the height above or below of the Earth's sea level – Graphic representation: coloring
- 21. Elevation map
- 22. Simplify more ● 3D → 2D – Moving only in the X axis – Colors → numbers
- 23. Robot Localization Part V. Localization Algorithm I.
- 24. Initial Belief
- 26. New turn - move
- 27. Sense – update belief
- 28. More sure
- 29. move
- 30. Sense – update belief
- 31. Same certainty
- 32. Localization Algorithm ● Repeat – Read sensors – Update guesses – Move – Update guesses
- 33. Robot Localization Part VI. Localization Algorithm II
- 34. Simplify even more Binary Map landmarks
- 35. Binary Map - Landmarks
- 38. Localization Algorithm ● Repeat – Read sensors – Update guesses – Move – Update guesses
- 39. Your turn! ● Make pairs ● Choose 2 maps and some robots ● One (A) puts the robot somewhere ● The other (B) tries to figure out, where the robot is
- 40. Your turn! ● REPEAT – A tells to B the sensor value (Black/White) – B thinking and put robots to the appropriate places in their map – IF B is sure about the location of the robot, tells it to A ● If B fails, new round with same roles ● If B is right, game ends, change the roles – ELSE B tells to A :move the robot (step right/step left)
- 41. Issues with the algorithm?
- 42. Problems of localization ● Algorithm is not fail-safe ● There is no exact data in the Real World ● Sensors behave strangely ● Switches bounce ● Moves are not exact
- 43. Problems of localization Localization can not be exact, just a guess with more or less preciseness To solve this: Probability theory https://www.udacity.com/course/cs373
- 44. Histogram Localization ● What we did (without proper steps) A histogram shows the probabilities of the location of the robot
- 47. Robot Localization Part VII. Rotary Encoders
- 48. How I intend to apply it? ● Rotary encoder – electro-mechanical device that converts the angular position of a shaft to a digital code
- 49. Rotary Encoders ● Incremental – Direction of rotation (+-1 click) ● Absolute – Absolute position of the shaft ● Usage of Rotary Encoders
- 55. Absolute encoder discs Standard binary encoding
- 58. Histogram rotary encoder ● Incremental encoder + histogram encoder disk +
- 59. How to create a good H.E.D.? ● We need a good Map – “good” is not exact
- 60. H.E.D Maps
- 61. H.E.D Maps
- 62. Algorithms to create the best maps ● Have you any idea?
- 63. Algorithms to create the best maps ● Count the repetitions – Slow – Up to 14 bits ● Count the number of steps for 100% certain – Worked for 24 bits – '001011110101000011001110', 4.66 ● [4, 5, 4, 5, 4, 5, 4, 5, 5, 5, 4, 5, 4, 5, 5, 4,5,..., 5] – '000000000000111101001011', 5.83 ● [12, 12, 11, 10, 9, 8, 7, 6, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4,..., 4]
- 64. How to print H.E.D.? Inkscape
- 65. Demonstration
- 66. Demonstration ● Sensors not perfect – ultrasonic sensor data ● Switches bounce – Switch and Logic Analizer – How to solve bounces? – Consequences of debounce (video) ● Histogram Rotary Encoder
Hinweis der Redaktion
- ROBOcUP COMPETITIONS WITH FULLY AUTONOMOUS, COOPERATIVE ROBOTS Brazlian Localization: a process to determine its position on a map
- I will teach you a piece of software which is a fundemantal part of every moving robot, especially of the google self driving car
- What do you think? Imagine autonomous car. What do you use for localization?
- Accuracy: theoretical worst case is 7.8 m with 95% confidence Jamming: GPS is subject to interference from both natural and human-made sources. For example: solar flares, radio stations, electricity, terrorists, Uncovered areas: underground, indoor, underwater, urban area, between high buildings Tokyo
- Accuracy: theoretical worst case is 7.8 m with 95% confidence Jamming: GPS is subject to interference from both natural and human-made sources. For example: solar flares, radio stations, electricity, terrorists, Uncovered areas: underground, indoor, underwater, urban area, between high buildings Tokyo
- Lombard street, San Francisco
- Highway, 404, Ontario, Canada Lane width: 2.7 m – 4.6 m. Typically 3.7 m for interstate highways. With 10 ms of accuracy, a car can be everywhere, even on the pavement or the lanes for opposite direction. For safe driving, the accuracy must be at least 10 cm, not 10 m!
- I'd like you to create these two questions. What do you have to have? What do you need to know?
- kuki
- 3D X/Y: position Colors: elevation/altitude How high is the point above the Sea-level
- Numbers: easier to process than colors
- Without measuring, we can say, that we are somewhere on the map. We can be Equal probability on every place. So we are totally unsure about our location.
- Without measuring, we can say, that we are somewhere on the map. We can be Equal probability on every place. So we are totally unsure about our location. Let's measure! Measure from the red window What can be our location based on the measured distance from the ground?
- We are more sure about our position than at the start, but it is not enough. Airplane has moved to the right 1 We have to update our belief of our location based on the moving SPEAK about moving is not perfect, so our belief should be decreasing. Show this with coloring more bars.
- Let's measure! Based on the distance from the ground, the elevation is 4. Update our belief!
- Our belief is pretty sure, but let us move on to the right 1 block!
- SPEAK about move uncertainty! But assume, now the move is exactly 1 to the right.
- Let's measure. Unfortunately, our belief has not been more certain. Let's move to the right!
- Let's measure. Unfortunately, our belief has not been more certain. Let's move to the right!
- Update belief of your location based on the sensed data and your map Update belief according to your move
- Instrument panel
- Imagine a corridor, where the robot should find a room. The landmarks are the doors. The robot can sense if there is a door in front of itself or not. The robot can move only sideways, left or right. The size of one robot-step is the same as the size of a door.
- We can create a map of doors and no-doors and we can code it with ones and zeroes. DRAW the doors
- The robot (wall-e) can move sideways. Moreover, if it is moves toward one-end of the map, it magically enters into the other side of the map, in both direction.
- Update belief of your location based on the sensed data and your map Update belief according to your move
- Let's imagine, that A (who owns the robot), sometimes pick up the robot and put it into a random place on the map. Let's imagine, that A does not behave according to the commands. Sometimes does not move, sometimes moves 2 steps or in an opposite direction.
- Easy to implement Runs fast for 1D case (moving only on one axis) Unfortunately, it consumes much memory for higher dimensions For an airplane, the location is X,Y,Z, however the direction is other 3 dimension (yaw, pitch, roll)
- Where we use rotary encoders? Hi-Fi Car radio Micro wave ovens PC Mouse Light dimmers Fan – rotational speed controller Any knob could be an encoder
- Worn out Gunk Contacts Surface scratchy, scarred, not smooth
- We should code differently all the 8 sectors. For the sake of simplicity, we are choosing binary coding. How many bits we need? How do you code the sectors? How many tracks will be?
- What is resolution? How many clicks are per revolution? How many sensors needs it? The More the number of sensors are, the more expensive the encoder is. Reduce size, number of sensors, number of electorinc parts. Number of tracks.
- One sensor One encoder
- What will happen with a robot on a bad map? What are the attributes of a good map?
- Which one is better than the others? What are the attributes of a good map? How can we compare the maps? Is there the best of maps?