Introduction to Robotics Shivam Goel A bit about me.. PhD student - - PowerPoint PPT Presentation

▶
introduction to robotics
SMART_READER_LITE
LIVE PREVIEW

Introduction to Robotics Shivam Goel A bit about me.. PhD student - - PowerPoint PPT Presentation

Jan 17, 2024 469 likes •1.08k views

Introduction to Robotics Shivam Goel A bit about me.. PhD student Advised by Dr. Diane J. Cook and Dr. Matthew E. Taylor Research interests: Computer vision, Reinforcement learning, Robotics Outline Introduction History of

slide-1
SLIDE 1

Introduction to Robotics

Shivam Goel

slide-2
SLIDE 2

A bit about me..

  • PhD student
  • Advised by Dr. Diane J. Cook and Dr. Matthew E. Taylor
  • Research interests: Computer vision, Reinforcement learning,

Robotics

slide-3
SLIDE 3

Outline

  • Introduction
  • History of Robotics
  • Successes and failure
  • Robot mapping
  • Simultaneous Localization and mapping (SLAM)
  • Some cool applications
  • Summary and Future directions
slide-4
SLIDE 4

History of robotics

  • In 1495, Leonardo da Vinci drew plans for a mechanical

man.

The model of Leonardo Da Vinci's robot with inner workings as displayed in Berlin

slide-5
SLIDE 5

History of Robotics

  • The acclaimed Czech playwright Karel Capek

(1890-1938) made the first use of the word ‘robot’, from the Czech word for forced labor or serf.

slide-6
SLIDE 6

History of Robotics

  • Science Fiction writer who moved into Robotics.

Was the first used the term "robotics' to refer to the study of robotic applications.

slide-7
SLIDE 7
slide-8
SLIDE 8

Definition of Robotics

  • A reprogrammable, multifunctional manipulator designed to move

material, parts, tools, or specialized devices through various programmed motions for the performance of a variety of tasks.

slide-9
SLIDE 9

Robotics: Successes

  • Space exploration: Mars Rover, Spirit
  • Self-driving cars: Google, Waymo, Uber
  • Household robotics: Roomba
  • Industrial robotics: KUKA
slide-10
SLIDE 10

Robotics: Failures

  • Robots in Space: Robonaut 2
  • Self-driving cars: Uber Self driving car kills

a pedestrian in Tempe, AZ

  • Some funny robot fails
slide-11
SLIDE 11

Outline

  • Introduction
  • History of Robotics
  • Successes and failures
  • Robot mapping
  • Simultaneous Localization and mapping (SLAM)
  • Some cool applications
  • Summary and Future directions
slide-12
SLIDE 12

Robot mapping

Real World Environment Perception Localization Cognition Motion Control Environment Model, Local Map Position Global Map Path

slide-13
SLIDE 13

Robot mapping

Perception Haptics Natural Language Processing Computer Vision

slide-14
SLIDE 14

Robot mapping

Localization Cognition Motion Control Environment Model, Local Map Position Global Map Path

slide-15
SLIDE 15

Localization example

slide-16
SLIDE 16
slide-17
SLIDE 17

Localization

  • Two types of approaches:
  • Iconic : use raw sensor data directly. Match current sensor readings with

what was observed in the past

  • Feature-based : extract features of the environment, such as corners and
  • doorways. Match current observations
slide-18
SLIDE 18

Mapping example

slide-19
SLIDE 19

SLAM example

slide-20
SLIDE 20

SLAM: applications

Indoors Space Undersea Underground

slide-21
SLIDE 21

SLAM

  • Simultaneous localization and mapping:

Is it possible for a mobile robot to be placed at an unknown location in an unknown environment and for the robot to incrementally build a consistent map of this environment while simultaneously determining its location within this map?

http://flic.kr/p/9jdHrL

slide-22
SLIDE 22

The SLAM problem

  • SLAM is a chicken-or-egg problem:
  • A map is needed for localizing the robot.
  • A pose estimate is needed to build the map.
  • Thus, SLAM is regarded as a hard problem in robotics
slide-23
SLIDE 23

Three Basic Steps

  • The robot moves
  • increases the uncertainty on robot pose
  • need a mathematical model for the motion
  • called motion model
slide-24
SLIDE 24

Three Basic Steps

  • The robot discovers interesting features in the environment
  • called landmarks
  • uncertainty in the location of landmarks
  • need a mathematical model to determine the position of the landmarks from

sensor data

  • called inverse observation model
slide-25
SLIDE 25

Three Basic Steps

  • The robot observes previously mapped landmarks
  • uses them to correct both self localization and the localization of all

landmarks in space

  • uncertainties decrease
  • need a model to predict the measurement from predicted landmark location

and robot localization

  • called direct observation model
slide-26
SLIDE 26

How to do SLAM

slide-27
SLIDE 27

How to do SLAM

slide-28
SLIDE 28

How to do SLAM

slide-29
SLIDE 29

How to do SLAM

slide-30
SLIDE 30

How to do SLAM

slide-31
SLIDE 31

How to do SLAM

slide-32
SLIDE 32

How to do SLAM

slide-33
SLIDE 33

How to do SLAM

slide-34
SLIDE 34

How to do SLAM

slide-35
SLIDE 35

*Slides adapted from Cyrill Stachniss

slide-36
SLIDE 36

*Slides adapted from Cyrill Stachniss

slide-37
SLIDE 37

Three main SLAM paradigms

  • Kalman filter
  • Particle filter
  • Graph based
slide-38
SLIDE 38

Graphical Model of Full SLAM:

) , | , (

: 1 : 1 : 1 t t t

u z m x p

Arrows = influences

*Slides adapted from Cyrill Stachniss

slide-39
SLIDE 39

39

Graphical Model of Online SLAM:

1 2 1 : 1 : 1 : 1 : 1 : 1

... ) , | , ( ) , | , (

  • òò ò

=

t t t t t t t

dx dx dx u z m x p u z m x p !

*Slides adapted from Cyrill Stachniss

slide-40
SLIDE 40

SLAM: Demo

  • Video
slide-41
SLIDE 41

Outline

  • Introduction
  • History of Robotics
  • Successes and failures
  • Robot mapping
  • Simultaneous Localization and mapping (SLAM)
  • Some cool applications
  • Summary and Future directions
slide-42
SLIDE 42

Bin Dog: Intelligent In-orchard Bin-Managing System for Tree fruit production

Center for Precision & Automated Agricultural Systems

Robotic Decision Making Laboratory

slide-43
SLIDE 43
  • Four-wheel-independent steering system (4WIS)
  • Passive suspension
  • Bin-loading system

43

Overview of Bin-Dog Hardware

Basic specifications of bin dog system Track 1.55 m Wheelbase 1.95 m Height (collapsed) 1.5 m Height (Fully extended) 2.1 m Engine Power 9.6 kW Max Speed 1.2 m·s-1 Max Steering rate 30 deg·s-1 Capacity of bin-loading system 500 kg

slide-44
SLIDE 44

Block diagram of bin-dog navigation system Data processing in Robot Operating System

Bin-Dog Autonomy

slide-45
SLIDE 45

Bin-Dog Autonomy

Sensors (GPS & IMU)

q GPS-based navigation system

Enter an alleyway

q Actual trajectories

  • Localization and path

planning

slide-46
SLIDE 46

q Laser-based navigation system

Detection of alleyway entrance

Bin-Dog Autonomy

slide-47
SLIDE 47

q Laser-based navigation system

Detection of bin in an alleyway Detection of tree rows

Bin-Dog Autonomy

slide-48
SLIDE 48

q Laser-based navigation system

Position Error computation Detection of tree rows

Bin-Dog Autonomy

slide-49
SLIDE 49

Fetch a target bin

Bin-Dog: showcase

slide-50
SLIDE 50

q Automated bin management

Bin-Dog: showcase

Place an empty bin

slide-51
SLIDE 51

Replace full bin with empty bin

Bin-Dog: showcase

slide-52
SLIDE 52

Robot Activity Support (RAS)

52

slide-53
SLIDE 53

Robot Activity Support (RAS)

53 Wilson, Garrett, et al. "Robot-enabled support of daily activities in smart home environments." Cognitive Systems Research 54 (2019): 258-272.

Camera module Tablet interface Navigation module

slide-54
SLIDE 54

SLAM in RAS

  • Navigation module built using

Google’s Cartographer system

  • SLAM system builds the map
  • f the environment and

determines the robot’s location on the map

slide-55
SLIDE 55

RAS: showcase

slide-56
SLIDE 56

RAS: showcase

slide-57
SLIDE 57

Future of Robotics

Source: https://www.therobotreport.com/10-biggest-challenges-in-robotics/

slide-58
SLIDE 58

Summary

  • History of robotics dates to 1495
  • In the past two decades robotics has seen many advances as well as some

failures.

  • Robot mapping is a crucial aspect of mobile robotics
  • SLAM = simultaneous localization and mapping
  • Kalman filter, particle filter, graph based
  • Full SLAM vs Online SLAM
  • Resources to study more about SLAM
  • Online lecture video series by Cyrill Stachniss
  • Springer “Handbook of Robotics”, Chapter on SLAM
  • Introduction to Mobile Robotics, Chapters 6 and 7
slide-59
SLIDE 59

Thank you