1. Sensor Based Planners
Bug algorithms

2. Bug Algorithms
World:
The world is , has obstacles, starting point {S} and target point {T}
The obstacles are closed and simple.
Each point belongs at most to one obstacle.
The world contains a finite number of obstacles locally.

3. Bug Algorithms Robot The robot is a point (Configuration Space)
The robot knows his position
The robot knows the target position
Equipped with a sensor
Infinite memory (though not necessary..)

4. Bug Behaviors Bug behaviors are simple:
Move in a straight line to the target
Follow a wall (right or left)

5. Definitions
Start point
Target point
“Hit point”
“Leave point”

6. Bug 0 (No memory) Head toward goal
Follow obstacle until you can head toward goal again (left or right but not both)
continue

7. Bug 0 - Example
Assuming a left t turning robot

8. What map will foil bug 0?

9. What map will foil bug 0?

10. Bug 1
Head toward goal
If an obstacle is encountered, circumnavigate it and remember how close you get to the goal
Return to the closest point (by wall-following and continue)

11. Bug 1 - Example

12. Bug 2 Call the line from the starting point to the goal the m-line
Head toward goal on the m-line
If an obstacle in the way, follow it until you encounter the m-line again.
Leave the obstacle and continue toward goal.

13. Bug1 vs Bug2 Bug1 is an exhaustive search algorithm
It looks all the choices before committing
Bug2 is a greedy algorithm
It takes the first thing that looks better
In many cases Bug2 will outperform bug 1

14. Tangent Bug
Assume we have a range sensor (with a finite resolution and is noisy)

15. Tangent Bug

16. Tangent Bug
Tangent bug relies on finding endpoints of finite, continuous segments of

17. Tangent Bug
Tangent bug relies on finding endpoints of finite, continuous segments of

18. Tangent Bug – Motion to Goal
Move to in a straight line toward goal
If you “see” something in front of you
For any such that choose the point that minimizes

19. Motion to Goal Example

20. What if the distance starts to go up?
M is the point with shortest distance to goal

21. What if the distance starts to go up?
M is the point with shortest distance to goal
Start to act like a BUG! And follow boundary

22. d_reach and d_follow
d_follow: is the shortest distance between the boundary which had been sensed and the goal. (observed thus far)
d_reach: let A be all the points within line of sight of x with range R that are on the followed obstacle.

23. Tangent Bug – terminate boundary-following behavior
When
We found a point on the obstacle, which is closer to the goal than any point we sensed so far (on the currently followed obstacle).

24. Example – Zero Sensor Range

25. Example – Finite Sensor Range

26. Example – Infinite Sensor Range

27. d_followed (M) is constantly updated