Visual servoing using 2DOF helicopter

model Chayatat Ratanasawanya

Min HeApril 6, 2010

Recall previous presentation The goal Progress report

◦ Image processing◦ depth estimation◦ Camera placement

Obstacles◦ Combine image processing and control Simulink

models Idea for the next step Questions/Comments

Overview

4 visual-servoing structures◦ Dynamic position-based look-and-move◦ Dynamic image-based look-and-move◦ Position-based visual servoing (PBVS)◦ Image-based visual servoing (IBVS)

Implemented a simulation of the dynamic position-based look-and-move system.

Implemented a Simulink model to locate the centroid of a ping-pong ball in image.

Previous presentation

Implement the system using PBVS, IBVS, or both techniques.

Tasks to tackle:◦ Image processing◦ Depth estimation◦ Camera placement on the helicopter model◦ Combine image processing and control Simulink

models.◦ Jacobian matrix derivation

The goal

Image processing

Use the diameter of the ball in image to estimate the depth

Depth estimation

Depth, D

Focal lengthF=538 pixel

Center of projection

Actual ball diameter db=40mm

Ball diameter on image, dimg

img

b

imgb

ddfD

df

dD

Depth estimation

area 6349 4384 3199 2386 1879 1492 1120 985 1082 975 1032 1122 1075

dm 92 76 64 56 49 44 38 36 38 35 37 40 39

real dis 234.0 284.8 335.6 386.4 437.2 488 538.8 589.6 589.6 589.6 589.6 589.6 589.6

Cal dis 234.4 282.2 335.0 386.0 436.0 487.6 565.6 601.9 561.6 608.3 577.3 542.1 552.8

0 2 4 6 8 10 12 140.0

10.020.030.040.050.060.070.080.090.0

100.0

diameter

Camera placement

Combining our image processing model and the control model has been a challenging task.

Obstacles

Global variable

Idea for the next step ex

ey

feD

e

ddfD

imgreal

img

b

Increment in pitch and yaw angles

LQR controller

Questions/comments are welcome

Thank you