Lecture 1 Introduction No Video Part1

8/14/2019 Lecture 1 Introduction No Video Part1

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Introduction to Autonomous Mobile Robots

Prof. Yan Meng

Department of Electrical and Computer EngineeringStevens Institute of Technology


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Course Logistics

Instructor: Yan Meng

Office: Burchard 411

Phone: 201-216-5496

Email:[email protected]

Office hour: Tuesday 3:00pm-5:00pm

Course website:

http://www.ece.stevens-tech.edu/~ymeng/courses/CPE521/CPE521A.htm

Homework

Homework will be due one week later after it is assigned

Problem solutions will be posted on-line LATE HOMEWORK WILLNOT BE ACCEPTED AFTER THE SOLUTION IS POSTED

Grading Homework 20% Midterm 20% Final 30% Project 30%
mailto:[email protected]://www.ece.stevens-tech.edu/~ymeng/courses/CPE521/CPE521A.htmhttp://www.ece.stevens-tech.edu/~ymeng/courses/CPE521/CPE521A.htmhttp://www.ece.stevens-tech.edu/~ymeng/courses/CPE521/CPE521A.htmhttp://www.ece.stevens-tech.edu/~ymeng/courses/CPE521/CPE521A.htmmailto:[email protected]


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Course Syllabus

Required Textbook:

Roland Siegwart and Ilah Nourbakhsh, Introduction to Autonomous MobileRobots, MIT Press, April 2004, ISBN# 0-262-19502-X.

Textbook website: http://autonomousmobilerobots.epfl.ch/

Some reading materials and hands out will be distributed in class.

Recommended readings:

George A. Bekey, Autonomous Robots From Biological Inspiration toImplementation and Control,MIT Press, 2005. ISBN 0-262-02578-7.

Robin Murphy, An Introduction to AI Robotics,MIT Press, November 2000.ISBN 0-262-13383-0.

Stefano Nolfi and Dario Floreano, Evolutionary Robotics: The Biology,Intelligence, and Technology of Self-Organizing Machines, MIT Press,2000, ISBN 0-262-14070-5.

Thomas Braunl, Embedded Robotics: Mobile Robot Design andApplications with Embedded Systems, Springer-Verlag Berlin Heidelberg

New York, ISBN 3-540-03436-6.


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Some Robotics Links

http://www.ifi.unizh.ch/groups/ailab/links/robotic.html#companies

http://www.cooper.edu/~mar/robotics_links.htm

http://www.roboticsonline.com/links/ http://www.ieee-ras.org/

http://www.euronet.nl/users/ragman/link_64.html
http://www.ifi.unizh.ch/groups/ailab/links/robotic.html#companieshttp://www.cooper.edu/~mar/robotics_links.htmhttp://www.roboticsonline.com/links/http://www.ieee-ras.org/http://www.euronet.nl/users/ragman/link_64.htmlhttp://www.euronet.nl/users/ragman/link_64.htmlhttp://www.ieee-ras.org/http://www.roboticsonline.com/links/http://www.cooper.edu/~mar/robotics_links.htmhttp://www.ifi.unizh.ch/groups/ailab/links/robotic.html#companies


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Applications of Mobile Robots

Indoor Outdoor

Structured Environments Unstructured Environments

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Autonomous Mobile Robots

The three key questions in Mobile Robotics

Where am I ?

Where am I going ?How do I get there ?

To answer these questions the robot has to have a model of the environment (given or autonomously built)

perceive and analyze the environment

find its position within the environmentplan and execute the movement

Basic tasks: deal with Locomotion and Navigation (Perception,

Localization, Planning and motion generation)

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Control of Mobile Robots

Most functions forsave navigation arelocal not involvinglocalization norcognition

Localization andglobal path planning slower updaterate, only whenneeded

This approach ispretty similarto whathuman beings do.

global

local

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Raw data

Environment ModelLocal Map

"Position"Global Map

Actuator Commands

Sensing Acting

InformationExtraction

PathExecution

CognitionPath Planning

Knowledge,Data Base

MissionCommands

Path

Real WorldEnvironment

LocalizationMap Building

Motion

Control

Perc

eption


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Control Architectures / Strategies

Control Loop

dynamically changing

no compact model available

many sources of uncertainty

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"Position"Global Map

Perception Motion Control

Cognition

Real WorldEnvironment

Localization

PathEnvironment ModelLocal Map


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Two Approaches

Classical AI(model based navigation)

complete modeling

function based

horizontal

decomposition

New AI(behavior based navigation)

sparse or no modeling

behavior based vertical decomposition

bottom up

Possible Solution Combine Approaches

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Environment Representation

Continuos Metric -> x,y,

Discrete Metric -> metric grid

Discrete Topological -> topological grid

Environment Modeling

Raw sensor data, e.g. laser range data, grayscale images

o large volume of data, low distinctiveness

o makes use of all acquired information

Low level features, e.g. line other geometric features

o medium volume of data, average distinctiveness

o filters out the useful information, still ambiguities

High level features, e.g. doors, a car, the Eiffel tower

o low volume of data, high distinctiveness

o filters out the useful information, few/no ambiguities, not enough information

Environment Representation and Modeling:

The Key for Autonomous Navigation

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Odometry

not applicable

Modified

Environments

expensive,

inflexible

Feature-based

Navigation

still a challenge for

artificial systems

Environment Representation and Modeling: How we do it!

Corridorcrossing

Elevator door

Entrance

Eiffel Tower

Landing at nightHow to find a treasure

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C o u r t e s y K

A r r

a s

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Environment Representation: The Map Categories

Recognizable Locations Topological Maps

Metric Topological Maps Fully Metric Maps (continuos ordiscrete)

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Incrementally

(dead reckoning)

Odometric or initialsensors (gyro)

not applicable

Modifying the environments

(artificial landmarks / beacons)

Inductive or optical tracks (AGV)

Reflectors or bar codes

expensive, inflexible

Methods for Navigation: Approaches with Limitations1

C o u r t e s y K

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Methods for Localization: The Quantitative Metric Approach

1. A priori Map: Graph, metric

2. Feature Extraction (e.g. line segments)

3. Matching:

Find correspondence

of features

4. Position Estimation:

e.g. Kalman filter, Markov

representation of uncertainties optimal weighting acc. to a priori statistics

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Methods for Localization: The Quantitative Topological Approach

1. A priori Map: Graph

locally unique

points

edges

2. Method for determiningthe local uniqueness

e.g. striking changes on raw data levelor highly distinctive features

3. Library of driving behaviors

e.g. wall or midline following, blind step,enter door, application specific

behaviorsExample: Video-based navigation with

natural landmarks

Courtesy of [Lanser et al. 1996]

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Map Building: How to Establish a Map

1. By Hand

2. Automatically: Map Building

The robot learns its environment

Motivation:

- by hand: hard and costly

- dynamically changing environment

- different look due to different perception

3. Basic Requirements of a Map:

a way to incorporate newly sensed

information into the existing world

model information and procedures for

estimating the robots position

information to dopath planning and

othernavigation task(e.g. obstacleavoidance)

Measure of Quality of a map

topological correctness

metrical correctness

But: Most environments are a mixture of

predictable and unpredictable features hybrid approach

model-based vs. behaviour-based

predictability

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Map Building: The Problems

1. Map Maintaining: Keeping track ofchanges in the environment

e.g. disappearingcupboard

- e.g. measure of belief of eachenvironment feature

2. Representation andReduction of Uncertainty

position of robot -> position of wall

position of wall -> position of robot

probability densities for feature positions additional exploration strategies

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Map Building: Exploration and Graph Construction

1. Exploration

- provides correct topology

- must recognize already visited location

- backtracking for unexplored openings

2. Graph Construction

Where to put the nodes?

Topology-based: at distinctive locations

Metric-based: where features disappear orget visible

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o u r t e s y K

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