2. Mobile Robot ApplicationsMobile 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: –→ C programming skills are important!

3. Contents Topics: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

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

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

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

8. Long rods for sensing

9. Cell-based maze for mapping and motion planning

10. 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.

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

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

13. 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

14. recursion Left wall following

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

16. recursively 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

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

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

19. This map shows calculating distances from the start for labyrinth from bottom left 1.Discuss how it works. 2.How it is represented. Using grid we start from here and go everywhere adding 1 at each step One approach to solve this are the Flood Fill Algorithms

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

21. Algorithm continued

22. continuation Example on next slide

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

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

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

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

29. 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

30. 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

31. Exploring cells of the map – grid based

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

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

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

35. The smaller the error the more accurate the map

38. 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!!

39. Conclusion 1.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. 2.What can be the cost (fitness) functions? 3.Think about other mapping algorithms. Can you use randomness?