16-735 Project Progress PresentationCoffee delivery mission

Nov, 13, 2007NSH 3211

Hyun Soo Park, Iacopo Gentilini

1

Landmark detection

• Changed the robot design to get better resolution (camera closer to the mirror):

• Used three landmark detection in “good regions”

2

L12 L3

L2

13

FLAN algorithm

3

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

6

Scheduling

- Kalman filtering- position level filtering using sonar data and wheel encorder

- Combining image processing algorithm