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Mobile Robot Applications Textbook: T. Bräunl Embedded Robotics, Springer 2003 Recommended Reading:

Mobile Robot Applications Textbook: T. Bräunl Embedded Robotics, Springer 2003 Recommended Reading: 1. J. Jones, A. Flynn: Mobile Robots, 2nd Ed., AK Peters, 1999 → Hobbyist’s introduction, easy reading 2. R. Arkin: Behavior-based Robotics, → Overview of behavior-based robotics

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Mobile Robot Applications Textbook: T. Bräunl Embedded Robotics, Springer 2003 Recommended Reading:

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  1. Mobile Robot Applications • Textbook: • T. Bräunl Embedded Robotics, Springer 2003 • Recommended Reading: • 1. J. Jones, A. Flynn: Mobile Robots, 2nd Ed., AK Peters, 1999 • → Hobbyist’s introduction, easy reading • 2. R. Arkin: Behavior-based Robotics, • → Overview of behavior-based robotics • 3. Kernighan, Ritchie: The C Programming Language • alternatively: <any C programming book> • → C programming skills are important!

  2. Contents Topics: • Maze driving • Micro Mouse Contest • Mapping • Driving in unknown environments • Elementary Image Processing • Edge detection, color detection, color blobs • Robot Soccer • autonomous agents

  3. Mazes and Mapping robot Know where to go! Place p Explore while finding the connection.

  4. Mazes We won local competition in 1990 Two our teams did not complete the run 2004

  5. This is becoming a competition for sensors, motors and crazy ideas. • Algorithmic problems are already solved.

  6. Long rods for sensing

  7. Cell-based maze for mapping and motion planning

  8. In early contests you can win using this simple algorithm. Next it was changed to make contest more interesting • This will not find the object in the middle if there is much empty space around.

  9. Follow left wall Algorithm Explore_left:Many Probabilistic variants have been created x,y = coordinates, dir = direction flags See next page for these routines

  10. Depending on current direction, update x and y coordinates of the mouse

  11. Never finds the gold Idea to remember: there are good special algorithms for some kinds of mazes. If you deal with general space or irregular map of labyrinth, you have to use several algorithms and adapt. There are many recursive algorithms, we will illustrate one of them

  12. Left wall following recursion

  13. In backtrack point robot knows that it has done a bad decision This explains and illustrates the concept of backtracking that is fundamental to robotics and AI

  14. Explore will call itself recursively Mark x and y position Check situations if front open etc Set flags front open etc Use flags front open etc

  15. Recursive call of itself This part shows recursive calls in all situations : Front open, Left open and right open

  16. We can combine recursion and left -wall-following algorithms in several ways

  17. Discuss how it works. • How it is represented. This map shows calculating distances from the start for labyrinth from bottom left Using grid we start from here and go everywhere adding 1 at each step One approach to solve this are the Flood Fill Algorithms

  18. Flood Fill Algorithms • The idea of marking cells appears here again

  19. Algorithm continued

  20. continuation Example on next slide

  21. Phase 3 Phase 2 Phase 1 This is like breadth first search

  22. Next Stage of Flood Algorithm: Shortest Path • Now we have: • Explored the maze • Know the distance to goal from every cell • Missing: • Shortest path from start to goal • Idea: • Generate shortest path from goal backward to start

  23. What to visualize in maze algorithms • Path already done by robot • Map of labyrinth • Part of map that has been covered so far • Distances of cells from start position

  24. Real-world mazes (hospitals, universities) and labyrinths (forest, park, open battlefield) • Applications in hospitals, museums, mines, big government buildings. Learn from counting doors or information on walls

  25. Mapping • Mapping an unknown environment is similar to the maze problem • However, maze is very simple: • fixed size cells • only 90º angles • Now: let us look at general environments

  26. Mapping • Explore unknown environment • Use infra-red PSD and infra-red proxy sensors only • Apply DistBug algorithm for wall following once an obstacle is encountered • Enter sensor measurement data in map • Use visibility graph with configuration space representation

  27. Exploring cells of the map – grid based

  28. continued Exploring obstacles in the map - general maps, shapes, no grid.

  29. This slide explains how to use grids to draw the map based on sensor information and actions executed.

  30. This slide explains how to use grids to draw the map based on sensor information and actions executed. • Such parts can be next fixed based on general predetermined knowledge of the nature of walls, obstacles and sizes.

  31. The smaller the error the more accurate the map

  32. You should collect these kinds of data for your robot environment of the demo. Think in advance where our robots will be demonstrated. Deans attrium? Near elevators? Not the lab!!

  33. Conclusion • Now that you understand one application of search, go read again the slides about search algorithms and think how they can be used in this application. • What can be the cost (fitness) functions? • Think about other mapping algorithms. Can you use randomness?

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