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</a>
of York U. and 
D. Wilkes at Ontario Hydro
</h4>
We are interested in elaborating a taxonomy for systems
of multiple mobile robots.  The specific issues we are foc
using on are the relationships between inter-robot
communication, sensing, and coordination of behaviour in the
context of position estimation and exploration.
A short paper describing a trial experiment in this context
is 
<!WA29><a href="ftp://ftp.cim.mcgill.ca/pub/mobile-robot/paper:simpl-convoy-w-vision.ps">
available in postscript form.
</a>

<p>
<li>
Mapping using weak information
<BR>
</a>
<h4>
G. Dudek in collaboration with Professors 
E. Milios and 
<!WA30><a href="http://www.cs.yorku.ca/People/jenkin/Welcome.html">
M. Jenkin
</a>
of York U. and 
D. Wilkes at Ontario Hydro
</h4>

Autonomous navigation using sensory information often depends 
on a usable map of the environment.  This work deals with the 
automatic creation of such a maps by an autonomous agent 
and the 
minimal requirements such a map must satisfy in order to be useful. 
One aspect of this work 
is the analysis of how uncertainty either in the map or in 
sensing devices relates to the reliability and cost of navigation and 
and path planning.  Another aspect is the development of sensing 
strategies and behaviours 
that facilitate reliable self-location and map construction.


<p>
<li>
Probabilistic sonar understanding
<BR>
<h4>
Simon Lacroix, Grogory Dudek
</h4>

<p>
<li>
Pose Estimation From Image Data 
Without Explicit Object Models
<BR>
<h4>
G. Dudek, Chi Zhang
</h4>
We consider the problem of locating a robot in an initially-unfamiliar 
environment from visual input. The robot is not given a map of the 
environment, but it does have access to a limited set of training 
examples each of which specifies the video image observed when the 
robot is at a particular location and orientation.  Such data might 
be acquired using dead reckoning the first time the robot entered an 
unfamiliar region (using some simple mechanism such as sonar to avoid 
collisions). In this paper, we address a specific variant of this 
problem for experimental and expository purposes: how to estimate a 
robot's orientation(pan and tilt) from sensor data.

Performing the requisite scene reconstruction needed to construct a 
metric map of the environment using only video images is difficult. 
We avoid this by using an approach in which the robot learns to 
convert a set of image measurements into a representation of its pose 
(position and orientation). This provides a {\em local} metric 
description of the robot's relationship to a portion of a larger 
environment.  A large-scale map might then be constructed from a 
collection of such local maps.  In the case of our experiment, these 
maps express the statistical 
relationship between the image measurements and camera pose. The 
conversion from visual data to camera pose is implemented using 
multi-layer neural network that is trained using backpropagation. 
For extended environments, a separate network can be trained for 
each local region. The experimental data reported in this paper 
for orientation information (pan and tilt) suggests the accuracy 
of the technique is good while the on-line computational cost is very 
low.  

<p>
<li>
Spatial abstraction and mapping
<BR>
<h4>
P. Mackenzie, G. Dudek
</h4>

This project involves the development of a formalism and 
methodology for making the transition from raw noisy 
sensor data collected by a roving robot to a map composed 
of object models and finally to a simple abstract map 
described in terms of discrete places of interest.  An important early stage 
of such processing the the ability to select, represent and 
find a discrete set of places of interest or landmarks that will make 
up a map.  Associated problems are those of using an map to accurately localize 
a mobile robot and generating intelligent exploration plans to 
verify and elaborate a map.
<!WA31><a href="ftp://ftp.cim.mcgill.ca/pub/mobile-robot/paper:RA94-model-based-localization.ps.Z">
Click here for a compressed postscript copy of a recent paper on this work.
</a>

<p>
<li>
Multi-sensor fusion for mobile robotics
<BR>
<h4>
MRL group members
</h4>
<!WA32><a href="ftp://ftp.cim.mcgill.ca/pub/mobile-robot/fusion.html">Click here for abstract (with picture)</a>


<p>
<li>
Spatial Mapping with Uncertain Data
<BR>
<h4>
G. Dudek
</h4>

As a sensor-based mobile robot explores an unknown environment it 
collects percepts about the world it is in.  These percepts 
may be ambiguous individually but as a collection they 
provide strong constraints on the topology of the environment. 
Appropriate exploration strategies and representations allow 
a limited set of possible world models to be considered as 
maps of the environment.  The structure of the real world and the 
exploration method used specify the reliability 
the final map and the computational and perceptual complexity 
of constructing it.  Computational tools being used to construct a 
map from uncertain data range from graph-theoretic to 
connectionist.



<p>
<li>
Human object recognition and shape integration
<BR>
<h4>
Gregory Dudek, 
Daniel Bub: Neurolinguistics, Montreal Neurological Inst., 
Martin Arguin: Phychology Dept., University of Montreal
</h4>

Computational vision is defined, to a large extent, with reference to 
the visual abilities of humans.  In this project we are examining the 
relationship between the characteristics of object shape and the 
abilities of humans to recognize these shapes.  
This includes 
the modelling of subjects with object recognition deficits due 
to brain damage as well as normal subjects.
<!WA33><a href="ftp://ftp.cim.mcgill.ca/pub/mobile-robot/paper:human-form-recog-capri.ps.Z">
Click here for a compressed postscript copy of a recent paper on this work.
</a>

<p>
<li>
Dynamic reasoning, navigation and sensing for mobile robots
<BR>
<h4>
Martin D. Levine, Peter Caines, Renato DeMori, Gregory Dudek, 
Paul Freedman (CRIM), Geoffrey Hinton (University of Toronto)
</h4>

The goal of this project is to develop both the theoretical basis 
and practical instantiation of a mobile robotic system 
will be able to reason about tasks, recognize objects in its environment, 
map its environment, understand voice commands, and navigate through the 
environment and perform the specified search tasks. This will be achieved 
in a dynamic environment, in that knowledge of a (possibly changing) world 
may be updated, and the tasks themselves may be radically altered during 
the system's operation.   Core research areas involved include 
perceptual modelling, control theory, neural networks, graph theory, 
attentive control of processing and speech understanding.

Among the key capabilities indended as outcomes of this 
project are:
    <ul>
    <li>  Integrated low (eg, points and lines) and high level (eg. places and 
rooms) descriptions of the environment. 
    <li>  Ability to deal with a changing environment.
    <li>  Ability to reason about multiple tasks and the changing environment.
    <li>  Ability to learn about the environment and the sensor characteristics.
    <li>  Ability to accept high level verbal commands (with a limited lexicon and
    </ul>
syntax) similar to those employed by humans (based on psychological data) 
and translate them into control actions for the robot and sensors.

<p>
<li>
Enhanced reality for mobile robotics
<BR>
<h4>
Kadima Lonji, G. Dudek
</h4>

This project involves the use of a synthetic scene model for
teleoperation or pose estimation.  Live video and synthetic model
information is fused to produce a composite image.


<p>
<li>
Natural language referring expressions in a 
person/machine dialogue.
<BR>
<h4>
G. Dudek, R. DeMori
</h4>
<!WA34><a href="http://www.cim.mcgill.ca/~dudek/mobile/language.html">Click here for abstract</a>

<p>
<li>
A FLEXIBLE BEHAVIORAL ARCHITECTURE 
FOR MOBILE ROBOT NAVIGATION
<BR>
<h4>
J. Zelek, M. D. Levine
</h4>

The intention of this study is to design an architecture that 
allows  the behavioral control strategy that is flexible, generalizable, and 
extendable.  The component dedicated to  behavioral activities should be able 
to 
attempt tasks with or without a reasoning module.  We are investigating 2D 
navigational tasks for a mobile robot possessing sonar sensors and a 
controllable TV camera mounted on a pan-tilt head. 
The major aspects  of our proposed behavioral architecture are as follows: 
- A natural language lexicon is used to represent spatial information 
and for defining task commands. The lexicon is used as a language for 
internal communications and user-specified commands. The task is to go to 
a location in space, either known or determined by locating a specific object. 
- An extension of a formalism, referred to as teleo-reactive (T-R) programs 
(Nilsson:94), is used for specifying behavioral control. 
The extensions of this approach involve dealing with real-time resource 
limitations and constraints.


</ul>

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<A NAME="links"></A>
<h2>
Some other (outside) information sources
</h2>

<P>
There is an archive for several general 
<!WA36><a href="ftp://ftp.cim.mcgill.ca/pub/techrep/README.html">
CIM Technical Reports</a>.

<P>
<!WA37><a href="http://piglet.cs.umass.edu:4321/robotics.html">
Robotics Internet Page at U. Mass.
</a>

<P>
<!WA38><a href="http://www.cup.cam.ac.uk/">
Cambridge University Press.
</a>

<P>
<!WA39><a href="http://mitpress.mit.edu/">
MIT Press.
</a>

<P>
<!WA40><a href="http://www.igs.net:80/precarn/">
The IRIS/PRECARN page.
</a>

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<A NAME="keywords"></A>
<h3>Keywords</h3>
<small>
mobile robot, mcgill, robots, autonomous, vision, perception, 
artificial intelligence, AI, robotics, telerobotics, computers,
computer science, engineering,
learning, environment mapping, map making, cartography, rendezvous,
intelligent machines, cognition, cognitive science, learning,
path planning, navigation, localization, positioning, modelling, modeling,
shape, form, recognition, graduate students, students, teaching, research,
canada, canadian, science, montreal, quebec, dudek, gregory, faculty,
newton, movie, movies, sex (gotta attract web-bots somehow), multimedia.

<A NAME="legalities"></A>
<h3>Legalities</h3>
<small>
<BLOCKQUOTE>
This document is Copyright (c) Gregory Dudek, 1996.
You are granted permission for the non-commercial use, 
reproduction, distribution or
display of this document in any format under the following restrictions.
Appropriate credit is given as to
its source and authorship. 
This permission is valid for a period of 45 (forty-five) days 
from the time this document was obtained from McGill University.  
All other rights reserved by the author(s).
</BLOCKQUOTE>
</small>
</body>
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