Autonomous Mobile Robots - Philadelphia University. Intro.p… · Autonomous Mobile Robots Zürich...

Autonomous Mobile Robots

Autonomous Systems Lab Zürich

ETH Master Course: 151-0854-00L

Autonomous Mobile

Robots

Lecture: Monday 14.15 - 16.00, HG D 3.2

Exercises: Tuesday 10.15 - 12.00, HG G 1

Roland Siegwart

Margarita Chli

Martin Rufli

Davide Scaramuzza

© R. Siegwart, ETH Zurich - ASL

1 - Introduction 1

2 Key Concepts in Autonomous Mobile Robotics Key Concepts in Autonomous Mobile Robotics

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/situation within the environment

plan and execute the movement

This course will deal with Locomotion and Navigation that includes:

Perception

Localization and Mapping

Planning

Motion Generation

1 - Introduction

Spring 2011

?


1 - Introduction

Raw data

Environment Model Local Map

"Position" Global Map

Actuator Commands

Sensing Acting

Information Extraction

Path Execution

Cognition Path Planning

Knowledge, Data Base

Mission Commands

Path

Real World Environment

Localization Map Building

Moti

on

Co

ntr

ol

Per

cepti

on

General Control Scheme for Mobile Robot Systems 1

3

Spring 2011


1 - Introduction

Control Architectures / Strategies

Control Loop

dynamically changing

no compact model available

many sources of uncertainties

Two Approaches

Classical AI

• complete modeling

• function based

• horizontal decomposition

New AI, AL

• sparse or no modeling

• behavior based

• vertical decomposition

• bottom up

"Position" Global Map

Perception Motion Control

Cognition

Real World Environment

Localization

Path Environment Model Local Map

1

4

Spring 2011


1 - Introduction

Two Approaches

Classical AI (model based navigation)

complete modeling

function based

horizontal

decomposition

New AI, AL (behavior based navigation)

sparse or no modeling

behavior based

vertical decomposition

bottom up

Possible Solution

Combine Approaches

1

5

Spring 2011


1 - Introduction

Mixed Approach Depicted into the General Control Scheme

Perception Motion Control

Cognition Localization

Real World

Environment

Perc

ep

tion

to

Action

Obsta

cle

Avoid

ance

Positio

n

Feedback

Path

Environment Model

Local Map

Local Map

Position

Position

Local Map

1

6

Spring 2011


“Understanding” the world

Raw Data Vision, Laser, Sound, Smell, …

Features Lines, Contours, Colors, Phonemes, …

Objects Doors, Humans, Coke bottle, car , …

Places / Situations A specific room, a meeting situation, …

•Models / Semantics • imposed

• learned

•Models • imposed

• learned

Navigation

Interaction

Servicing / Reasoning •Functional / Contextual

Relationships of Objects • imposed

• learned

• spatial / temporal/semantic

Fus

ing

& C

ompr

essi

ng I

nfor

mat

ion

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7

Spring 2011


1 - Introduction

Environment Representation

Continuous Metric -> x, y, q

Discrete Metric -> metric grid

Discrete Topological -> topological grid

Environment Modeling

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

• large volume of data, low distinctiveness

• makes use of all acquired information

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

• medium volume of data, average distinctiveness

• filters out the useful information, still ambiguities

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

• low volume of data, high distinctiveness

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

Environment Representation and Modeling:

The Key for Autonomous Navigation

1

8

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1 - Introduction

Odometry

not applicable

Modified Environments

expensive, inflexible

Feature-based Navigation

still a challenge for

artificial systems

Environment Representation and Modeling: How we do it!

12195

34

39

25 Corridor

crossing

Elevator door

Entrance

Eiffel Tower

Landing at night How to find a treasure

Co

urt

esy K

. A

rra

s

1

9

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1 - Introduction

Human Navigation: Topological with imprecise metric information

~ 400 m

~ 1 km

~ 200 m

~ 50 m

~ 10 m

Co

urt

esy K

. A

rra

s

1

10

Spring 2011


1 - Introduction

Environment Representation: The Map Categories

Recognizable Locations Topological Maps

2 km

100 km

200 m

50 km

y

x{W}

Metric Topological Maps Fully Metric Maps (continuos or

discrete)

Co

urt

esy K

. A

rra

s

1

11

Spring 2011


Reasoning in the presence of uncertainties and incomplete information

Combining preliminary information and models with learning from

experimental data

“Understanding” – Probabilistic Reasoning (e.g. Bayesian)

Picture Courtesy of Bessiere, INRIA Grenoble, France

1

12

1 - Introduction

Spring 2011


Reducing uncertainties

Improving belief state

by moving (SEE and ACT)

1

13

1 - Introduction

Spring 2011


Continuous, recursive and very compact

Metric Navigation: Probabilistic Position Estimation (Kalman Filter)

1

14

1 - Introduction

Spring 2011

ACT

SEE


State Prediction: Odometry

Incrementally (dead reckoning)

Odometric or initial sensors (gyro)

Drift

1 - Introduction

Spring 2011

1

15


1 - Introduction

Methods for Localization: The Quantitative Metric Approach

1. A priori Map: Graph, metric

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

x

y

wxr

wyr

{W}

lwqr

3. Matching:

Find correspondence

of features

4. Position Estimation:

e.g. Kalman filter, Markov

representation of uncertainties

optimal weighting acc. to a priori statistics

OdometryObservation

Co

urt

esy K

. A

rra

s

1

16

Spring 2011


1 - Introduction

Belief Representation

a) Continuous map

with single hypothesis

b) Continuous map

with multiple hypothesis

c) Discretized map

with probability distribution

d) Discretized topological

map with probability

distribution

Spring 2011


1 - Introduction

Grid-Based Metric Approach

Grid Map of the Smithsonian’s National Museum of American History in Washington DC. (Courtesy of Wolfram Burger et al.)

Grid: ~ 400 x 320 = 128’000 points

Courtesy S. Thrun, W. Burgard

1

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1 - Introduction

Autonomous Indoor Mapping

OLD NEW

Courtesy of Sebastian Thrun

1

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Spring 2011


“Understanding” Probabilistic 3D SLAM

photo of the scene

find a plane for every cell using RANSAC

one p

lane p

er

grid c

ell

decompose space into grid cells fill cells with data

raw 3D scan of the same

scene

raw data

fuse similar neighboring planes together

final segmentation

segmented planar segments

1 - Introduction 1

27

Spring 2011


“Understanding” Probabilistic 3D SLAM

close-up of a reconstructed hallway

close-up of reconstructed bookshelves

The same experiment as before but this time planar segments are visualized and integrated into the estimation

process

1 - Introduction 1

28

Spring 2011


along its way (140 m), the robot takes 90 3D scans; the total number of planar

segments is 244 (44696 data points / 299 polygons). This corresponds to a compression ratio of more than 99%

w.r.t. raw data (5212800 points).

Data Compression: 99%

the robot lacks sensors to estimate

3D trajectories – ICP or “laser-corrected

odometry” allows to simulate a 6D

odometry. This makes reconstruction of non-

flat environments possible

J. Weingarten

1 - Introduction 1

29

Spring 2011


PR2: Personal Robot 2

Robot for reasearch and

experimentation

Development platform:

Cameras, Laser scanners,

Accelerometer, Tactile sensors

16 CPU cores

Sophisticated joints design for safety

Variety of networking tools for

communicating data

ROS: Robot Operating System

free, open source, software

development platform integrating

libraries and tools

Cost: $400 000

1 - Introduction 1

37

Inside the PR2

Courtesy of


PR2: applications

1 - Introduction 1

38

Fold towels Fetch beer

Clean-up with cart Navigation

Courtesy of Willow Garage


1 - Introduction

Spring 2011


1 - Introduction 1

40 Content of the Course

1. Introduction

2. Locomotion

3. Mobile Robot Kinematics

4. Perception

5. Mobile Robot Localization and Mapping

6. Planning and Navigation

Other Aspects of Autonomous Mobile Systems

Applications


1 - Introduction 1

41 Slides and Lecture Notes

Slides and Exercises

http://www.asl.ethz.ch/education/master/mobile_robotics

Currently you can find there the last years slides and exercises. This year’s material will

gradually become available throughout the term

Relevant reading material:

Introduction to Autonomous Mobile Robots

Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza

• Intelligent Robotics and Autonomous Agents series

• The MIT Press

• Massachusetts Institute of Technology

• Cambridge, Massachusetts 02142

• ISBN 0-262-19502-X

http://www.mobilerobots.org

Can be purchased in our secretariat (CLA E31)

for 40 CHF.


1 - Introduction 1

42 Program

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