Perception and Control in Humanoid Robotics using Vision
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Perception and Control in Humanoid Robotics using Vision Using position-based visual servoing, Metalman has the ability to perform simple manipulation tasks; the sequence below shows Metalman autonomously locating and stacking three randomly placed blocks. Future work will include

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Perception and Control in Humanoid Robotics using Vision

Using position-based visual

servoing, Metalman has the ability

to perform simple manipulation

tasks; the sequence below shows

Metalman autonomously locating

and stacking three randomly placed

blocks. Future work will include

servoing two arms cooperatively to

perform even more complex tasks!

Geoffrey Taylor

Supervisors: A/Prof Lindsay Kleeman

A/Prof R Andrew Russell

0 s

20 s

Imagine you had a domestic humanoid robot servant,

then consider what you would like it to do …

It quickly becomes clear that a practical domestic robot must

possess a basic ability to find and grasp objects in a dynamic,

cluttered environment (ie. your house!). To address this issue,

we have developed a self-calibrating, position-based visual

servoing framework. Metalman, the Monash

upper-torso humanoid robot, provides a

platform for this and other exciting

humanoid robot experiments.

35 s

3D hand pose

measurement gives

the relative position

and orientation

between hand

and head

This is the actual stereo view seen by

Metalman while tracking its hand

Biclops

active

head

It’s a visual thing …

Visual servoing is a feedback control

technique using visual measurements

to robustly regulate the motion of a

robot. Metalman uses stereo cameras to

estimate the 3D pose (position and

orientation) of its hands, by observing

bright LEDs attached in a known pattern

and feeding the data into a Kalman tracking

filter. Other objects are similarly localized via attached

coloured markers. Depending on the desired action

(eg. grasp an object), Metalman uses this pose information

to generate actuating signals that drive the arm to the required

pose. Because Metalman continuously estimates the pose of

its hands, the system is completely self-calibrating.

80 s

LED markers on

the hand facilitate

pose tracking

Metalman

uses pose

information

to drive hand

in desired direction

Final hand pose

depends on the

desired action

100 s

Progress time indicated at top-right of each frame

160 s

Even robots get lonely! Metalman

must interact with humans to be

truly useful. The experiment

below demonstrates simple

interaction using motion cues: the

user taps on a random block, and

Metalman places a finger above

the selected object.

Where has all the data gone?

In a complex system such as Metalman, the

interaction of various components can generate unwanted dynamics such as

dead time delays. For instance, the graph below plots the position of the head

during a sinusoidal motion: the red line indicates joint encoder data, and the

blue line shows data from the cameras. The apparent 30 ms delay between

these devices can degrade

Metalman’s dynamic

performance. In this work,

we develop simple matching

and prediction techniques

that allow Metalman to

autonomously estimate

and reduce these effects.

For more information, check the IRRC web page atwww.ecse.monash.edu.au/centres/IRRC

Electrical and Computer Systems Engineering

Postgraduate Student Research Forum 2001


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