16-735 Project Progress Presentation Coffee delivery mission
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Transcript of 16-735 Project Progress Presentation Coffee delivery mission
16-735 Project Progress PresentationCoffee delivery mission
Nov, 13, 2007NSH 3211
Hyun Soo Park, Iacopo Gentilini
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Landmark detection
• Changed the robot design to get better resolution (camera closer to the mirror):
• Used three landmark detection in “good regions”
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L12 L3
L2
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FLAN algorithm
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a. Extraction of landmark features in the image:a. m_lmk : RGB mean of landmark region;
b. s_lmk : RGB standard deviation;
c. d_lmk : Euclidean distance of a pixel when its RGB is different from m_lmk about 1.96 · s_lmk .
b. Calculating landmark regionsa. d : calculate Euclidean distance d for each pixel to
m_lmk and scaling each component by s_lmk;
b. if d ≤ d_lmk pixel is in the landmark region and update value to 255 else update value to 0.
c. Get landmark position
a. for each pixel = 255 check neigh- borhood: if its dimension is bigger than the threshold r_lan, calculate the center of mass of the neighborhood
o position given by built in sensors
x calculated position using three landmark detection
Path planning : D* algorithm
- Nomad Simulator : MATLAB
Path planning : D* algorithm
- Program on Nomad according to simulator (C++)
- Problem- Noisy sonar : filter required- Robot size
Motion algorithm
a. LOAD map
b. SET D* backpointer
c. REPEAT
1. MOVE to TargetPoint
2. READ sonar;
3. state estimation using Kalman filtering;
4. if robot is in a landmark region, update state vector using landmark information;
5. UpdateMap and D*
6. UpdateTargetPoint
7. IF current position == goal position, THEN return
d. END REPEAT
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Scheduling
- Kalman filtering- position level filtering using sonar data and wheel encorder
- Combining image processing algorithm