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Published byDuane Padelford Modified about 1 year ago

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Sensor Based Planners Bug algorithms

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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.

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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..)

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

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

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Bug 0 (No memory) 1. Head toward goal 2. Follow obstacle until you can head toward goal again (left or right but not both) 3. continue

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Bug 0 - Example Assuming a left t turning robot

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What map will foil bug 0?

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Bug 1 1. Head toward goal 2. If an obstacle is encountered, circumnavigate it and remember how close you get to the goal 3. Return to the closest point (by wall- following and continue)

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Bug 1 - Example

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Bug 2 Call the line from the starting point to the goal the m-line 1. Head toward goal on the m-line 2. If an obstacle in the way, follow it until you encounter the m-line again. 3. Leave the obstacle and continue toward goal.

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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

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

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Tangent Bug

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Tangent bug relies on finding endpoints of finite, continuous segments of

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

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Tangent Bug – Motion to Goal 1. Move to in a straight line toward goal 2. If you “see” something in front of you 1. For any such that choose the point that minimizes

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Motion to Goal Example

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

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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

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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.

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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).

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Example – Zero Sensor Range

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Example – Finite Sensor Range

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Example – Infinite Sensor Range

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d_followed (M) is constantly updated

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