Robotics (2.008x Lecture Slides)

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Robotics (and automation)
MIT 2.008 / 2.008x – Prof. John Hart

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What is automation?
What is an industrial robot?

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Automation:
automatically controlled operation of an apparatus,
process or system by mechanical or electronic
devices that take the place of human labor*
*Wilson, Implementation of Robot Systems

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Automation in manufacturing systems
 Machines
 Material flow and handling
 Robotic manipulation
 Local controllers (machines / workcells)
 Factory network controllers (supervision, optimization)
Requirements for implementation
 Coordination of process rates
 Robustness against faults
 Online monotoring / control

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Automation: filling water bottles
https://www.youtube.com/watch?v=EjkOiBXI14o
Bottle molding: 2,250/hr https://www.youtube.com/watch?v=soiGsZj7hn0&list=PL167A9254F5245075&index=33
All videos from Krones: https://www.youtube.com/playlist?list=PL167A9254F5245075

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Automation:
automatically controlled operation of an apparatus,
process or system by mechanical or electronic
devices that take the place of human labor
Industrial robot:
an automatically controlled, re-programmable,
multipurpose manipulator programmable in three or
more axes, which may be either fixed in place or
mobile for use in industrial automation applications
(according to the international federation of robotics)
from Wilson, Implementation of Robot Systems

2.008-F16 | 7from Wilson, Implementation of Robot Systems
The first industrial robot, the ‘Unimate’
 Invented/built by Joseph Engelberger and
George Devol (company formed 1956)
 Hydraulically driven arm with instructions
read from magnetic drum
 Initial use to stack die cast parts at General
Motors plant in New Jersey
First major industrial robot installation (1969)
 Went from 40% to 90% automated spot welding
 3000 robots in use by 1973 (mfg partnership
between Unimation and Kawasaki of Japan)

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Today’s agenda
 Why automation and robotics in manufacturing?
 Common robotic manipulators used in manufacturing:
articulated, selective compliance (SCARA), delta
 Geometry and workspace
 Applications
 Comparing performance and capability tradeoffs
 Grippers (‘end effectors’)
 Emerging trends and technologies

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Why use robotics and automation in
manufacturing?

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Why use robotics and automation in mfg?
(Potentially)
 Improve product quality and consistency
 Improve worker safety/satisfaction (by doing heavy or
dangerous jobs)
 Increase production rate
 Increase production flexibility
 Reduce manufacturing cost
 Reduce waste
 Save space in high value areas
 Some of the above are coupled; rarely are all true
 Robots generally not good for operations requiring both
high force and high accuracy (e.g., machining); also takes
time to program and establish accurate path (calibration)

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How can we measure whether a process (or industry in
general) is appropriate for use of robotics?
?
?

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BCG ‘The Robotics Revolution’ https://www.bcgperspectives.com/content/articles/lean-manufacturing-innovation-robotics-revolution-next-great-leap-
manufacturing/

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Artaic: robotic assembly of custom mosaic tile
ARTAIC: https://artaic.com/

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Now >2 million industrial robots in use worldwide
Total number of robots in use: Asia:Europe:Americas = 3:1.5:1

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Predicted growth (source: Boston Consulting Group)
BCG ‘The Rise of Robotics’
https://www.bcgperspectives.com/content/articles/business_unit_strategy_innovation_rise_of_robotics/

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Robotic manipulators

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Articulated robot
Sciavicco, L.; Siciliano, B. Modelling and Control of Robot Manipulators; Advanced Textbooks in Control and Signal Processing;
Springer London: London, 2000; Burckhardt C. Industrial Robots : Proceedings = Robots Industriels : Comptes Rendus = Industrie-
Roboter : Tagungsberichte [e-book]. Basel : Birkhäuser Verlag, 1975
Spherical wrist for end-effector
Waist joint
Shoulder joint
Elbow joint
Wrist
 The articulated arm provides the most dexterity within the
working volume.
 Errors are cumulative due to the series architecture.
 Typical robot sizes range from a reach of 0.5 to over 3.5 m
and carrying capacities from 3 to over 1000 kg.
 The end-effector orientation can be independent of position
using a spherical wrist.

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Workspace of articulated manipulator
Sciavicco, L.; Siciliano, B. Modelling and Control of Robot Manipulators; Advanced Textbooks in
Control and Signal Processing; Springer London: London, 2000.
Working
envelopeEnd-
effector

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Kuka articulated robot arm
Heavy-duty robot

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Verifying the workspace [: don’t try this with
your robot]
https://www.youtube.com/watch?v=bxbjZiKAZP4 (see description)
There’s a real ride: https://www.youtube.com/watch?v=bSdA_oq1EgU

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Axes 1, 2, and 3
Motors

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Planetary gear
in Axis 6
Power transmission
belts for axes 4, 5, & 6
Motors and Transmissions of Axes 4, 5, and 6
Motors
Transmission

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Brushless AC Servomotor
• Rotor has a rotating permanent magnet and a fixed
armature
• Electronic controller replaces the brush/commutator
assembly of the brushed motor
• High torque to weight ratio, high efficiency and
reliability (compared to brushed motors)
• With windings in the housing, cooling is done by
conduction
AC Servomotor
(Kollmorgen AKM series used in KUKA robots)
Torque is kept (nearly)
constant with speed and
load changes
Reference

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Wave Generator
Flex Spline
Circular Spline
Strain Wave Gear Components
• Motor is connected to the Wave generator
• When the wave generator rotates CW by 3600, Flex Spline
rotates CCW by 2 teeth w.r.t. the Circular Spline (fixed)
• High gear reduction ratios in a small volume (30:1 to 320:1 is
possible Vs 10:1 from planetary gears)
• High positioning accuracy and repeatability (+/- 3 arc
seconds) [1 arc second = 1/3600th of a degree]
• High torque capacity and torsional stiffness
• Low tooth friction losses and wear  longer life and high
reliability
Strain wave gear
Operating Principle
Video : Strain Wave Gear Principle
Reference

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How is the robot structure made?

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Maintaining an accurate 3D path
True (exact) value
Repeatability
Accuracy
Probabilitydensity
Consider discrete vs.
continuous toolpaths (what are
some applications of each?)

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Serial kinematics by HTM (Homogeneous Transformation Matrices)
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What influences accuracy and repeatability of end
effector position?
 Structural design (stiffness)
 Actuator performance: accuracy, repeatability, stiffness
 Manufacturing and assembly tolerances
 Calibration
 Use case: loads, toolpath dynamics

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Small robot Heavy-duty robot

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Robot calibration using a laser tracker
https://www.youtube.com/watch?v=SLnFq431mJg

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Robot calibration using a laser
tracker
Corner cube
retroreflector: paths of
entry and exit always
parallel
Video: https://www.youtube.com/watch?v=SLnFq431mJg

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Articulated robots for spot welding
FANUC display at IMTS 2014

2.008-F16 | 36FANUC display at IMTS 2014
‘Learning’ vibration control (LVC) robot: uses sensor to adapt trajectory
for speed and accuracy

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A coordinated 3D path

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Lightweighting using 3D printing
http://spectrum.ieee.org/automaton/robotics/humanoids/boston-
dynamics-marc-raibert-on-nextgen-atlas

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SCARA (Selective Compliance Assembly Robot Arm)
Sciavicco, L.; Siciliano, B. Modelling and Control of Robot Manipulators; Advanced Textbooks in Control and Signal Processing; Springer
London: London, 2000; Burckhardt C. Industrial Robots : Proceedings = Robots Industriels : Comptes Rendus = Industrie-Roboter :
Tagungsberichte [e-book]. Basel : Birkhäuser Verlag, 1975
 Four-axis arm: positioning in x,y,z and rotation about z
 Very rigid in the vertical direction and with compliance in the horizontal plane;
useful for high accuracy positioning in x-y plane (e.g., part insertion)
Planar rotary
motion 1
Planar rotary
motion 2
Vertical motion
Working envelope
Base rotation

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SCARA for precision assembly and fastening

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Delta (parallel kinematics) robot
http://www.ohio.edu/people/williar4/html/pdf/DeltaKin.pdf; http://www.adept.com/products/robots/parallel/quattro-s650h/intro
Working envelope (example
 The parallel or delta configuration
differs from the articulated arm
because the constraints (or
degrees of mobility) are in parallel.
 3 degrees of freedom; typically
low payload capacity.
 Errors are non-cumulative unlike
the case of series constraint in the
kinematic arm. Also provides high
stiffness (relative to weight) and
high speed.
 Mainly used in pick and place
operations, especially in the food
industry and also in some
assembly applications.
Rotations
controlled by
actuators

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Delta robot
Pancakes: https://www.youtube.com/watch?v=v9oeOYMRvuQ
Salami snacks: https://www.youtube.com/watch?v=aPTd8XDZOEk

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How do the speed and
repeatability compare to an
articulated robot?

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0
20
40
60
80
100
120
140
0 2 4 6 8 10 12
Repeatability(µm)
Speed (m/s)
Speed vs Repeatability by Arm Type
6 DOF avg
SCARA avg
Delta avg
Speed vs repeatability
Articulated
SCARA
Delta picker

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Stiffness: serial versus parallel
Slocum

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Cartesian robots: Gantry (serial or parallel)
http://blenderartists.org/forum/attachment.php?attachmentid
=371530&d=1428277647&thumb=1
Shipbuilding crane
http://www.rockymountainwaterjet.com/small_jet.jpg
OMAX waterjet Stacking system
http://seamco.be/machine/
cartesian-robot-rc-600/137
Translation along
three axes
FDM printers

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Single-point metal forming (Ford)
 Pair of stiff parallel robots iteratively
deform sheet metal parts (= prototyping
method)
Operation
CAM software
Video: https://www.youtube.com/watch?v=iNQ40MYwZqw

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Standard grippers for material handling
http://www.ardelis.co.za/products-grippers/
http://www.motoman.com/products/peripherals/
adaptive-gripper.php
http://us.schmalz.com/aktuelles/produkte/vaku
umgreifsysteme/00932/
Conventional clamp Three-finger gripper
Vacuum suction pads

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Material handling: Nachi 2-armed robot
exhibit at IMTS 2014

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Tooling cost
+
Equipment cost
+
Material cost
+
Overhead cost
How would we determine cost of adding robots?
n
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C Roundup1

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BCG ‘The Robotics Revolution’ https://www.bcgperspectives.com/content/articles/lean-manufacturing-innovation-robotics-revolution-next-great-leap-
manufacturing/

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Picking and stocking warehouse shelves
(see ‘Amazon Picking Challenge’)
http://spectrum.ieee.org/automaton/robotics/industrial-
robots/team-delft-wins-amazon-picking-challenge

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http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/google-wants-robots-to-acquire-
new-skills-by-learning-from-each-other
https://research.googleblog.com/2016/03/deep-learning-for-robots-learning-from.html

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Flexible automation: Rethink Robotics “Baxter”
 Programmable manually (‘teach’ by holding the robot’s end effector and
moving it through the path)
 Tolerant to variabilities e.g. in part position on conveyor
 Force feedback enabling the robot to adapt to variations without damage  but LOW
STIFFNESS
 Working radius = 1210 mm; maximum payload (including end-effector) = 2.2 kg
 7 degrees of freedom per arm Degrees of mobility = 7 per arm
 Embedded vision system
http://www.rethinkrobotics.com
https://www.youtube.com/watch?v=DKg-GvPyNLc
https://www.youtube.com/watch?v=KpqaBKyZGeE&feature=youtu.be

2.008-F16 | 56exhibit at IMTS 2014

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References
1 Introduction
Tesla factory: Photo #54521 Copyright NOTCOT INC 2006-2016
iRobot Roomba cleaning robot image © Molaunhuevo.com
Clearpath robot with boxes: photo © Clearpath Robotics, 2015
Big saw robot: Photo © 2016 MIT. All rights reserved.
MIT campus Sean Collier memorial: Photo © John Hart. Used with permission.
Brick assembly robot: Photo from MIT Technology Review © MIT.
Automated Dynamics fiber placement machine: Image from Automated Dynamics ©
Agarik SAS.
Humanoid robot: Screenshot image © Copyright 2016 IEEE Spectrum
2 Automated Water Bottling
Krones water production video + product pictures © Krones AG 2016

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References
3 Why Automate
Early robot: Photo © 2016 Maplegate Media
Early car production plant: Photo © 2016 Maplegate Media
Robotics impact on industries graphic: Image Copyright © 2016 The Boston Consulting
Group. All rights reserved
Artaic mosaic robot © Copyright 2016 - Artaic LLC. Artaic® is a registered service mark
of Artaic LLC.
Automation potential graphic © 1996-2016 McKinsey & Company
Worldwide spending on robotics: Image Copyright © 2016 The Boston Consulting Group.
All rights reserved
Tesla Advanced Automation blog post © Tesla Motors, 2016

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References
4 Articulated Robots
Articulated Robot: Sciavicco, L.; Siciliano, B. Modelling and Control of Robot
Manipulators; Advanced Textbooks in Control and Signal Processing; Springer London:
London, 2000
Articulated Robot: Burckhardt C. Industrial Robots : Proceedings = Robots Industriels :
Comptes Rendus = Industrie-Roboter : Tagungsberichte [e-book]. Basel : Birkhäuser
Verlag, 1975 2.008-F16 | 20
Kuka robot: Photo © Kuka AG 2013
Robotic workspace: Sciavicco, L.; Siciliano, B. Modelling and Control of Robot
London, 2000
Kuka robot datasheet © Kuka AG 2016
Kuka robot ride recording: Video © Youtube user Iain Hendry, 2009
Axes pictures © YouTube user thegeekgroup, 2016. (CC BY) 3.0
Images of robot taken apart © YouTube user thegeekgroup, 2016. (CC BY) 3.0

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References
5 Accuracy and Calibration
Kuka robot: Photo © Kuka AG 2013
Image of servomotor assembly © Kollmorgen. All Rights Reserved
Graph of servomotor speed vs. torque © 2016 ORIENTAL MOTOR U.S.A. CORP. All
Rights Reserved.
Harmonic drive gear components: M. MasoumiH. Alimohammadi, An investigation into
the vibration of harmonic drive systems, Frontiers of Mechanical Engineering, 2013, Vol.
8, Issue 4, 409-419; Copyright 2014, The Institution of Engineering and Technology
Harmonic drive gear schematic drawing Image © Keiji Ueura, Rolf Slatter, "Development
of the Harmonic Drive Gear for Space Applications", ESMATS 1999
Microscope cam: Photo © John Hart. Used with permission.
Laser tracker calibration video Video © FARO Technologies / Youtube user FAROGB,
2013
Laser robot tracking schematic: Image © FARO Technologies
FARO laser tracker Image © FARO Technologies

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References
Corner cube retroreflector schematic: Image by User: chetvorno on Wikimedia. This work
is in the public domain.
Faro SMR: Image © FARO Technologies
Fanuc display at IMTS 2016: Photo © John Hart. Used with permission.
Fanuc vibration compensation schematic: Image © FANUC America Corporation
Four Fanuc robots welding shaft: Image © FANUC America Corporation
Boston Dynamics humanoid robots: Image © Boston Dynamics 2016
6 Scara, Delta, and Gantry Robots
Scara robot workspace: Sciavicco, L.; Siciliano, B. Modelling and Control of Robot
London, 2000; Burckhardt C. Industrial Robots : Proceedings = Robots Industriels :
Comptes Rendus = Industrie-Roboter: Tagungsberichte [e-book]. Basel : Birkhäuser
Verlag, 1975
Old Kuka scara robot: Photo © Kuka AG

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References
Epson scara robot: Photos Copyright © 2000-2016 Epson America, Inc., All Rights
Reserved
Delta kinematic: Image © Robert Williams, Ohio University, 2016
Adept Quattro S650h delta robot: Image © OMRON ADEPT Technologies Inc
Flexpicker delta robot pankaces pick'n'place demo © Andrew Jones, 2009, via YouTube
Flexpicker delta robot salami pick'n'place demo: Video © ABB / Youtube user
ABBRobotics, 2010
Gantry machine: Image © ATERA MANUFACTURERS GROUP, S.A.
Conventional mill: Photo © John Hart. Used with permission.
Gantry schematic: Sciavicco, L.; Siciliano, B. Modelling and Control of Robot
London, 2000; Burckhardt C. Industrial Robots : Proceedings = Robots Industriels :
Comptes Rendus = Industrie-Roboter : Tagungsberichte [e-book]. Basel : Birkhäuser
Verlag, 1975
Harbour freight gantry: Photo Courtesy of Newport News Shipbuilding of the U.S. Navy.
This work is in the public domain.

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References
OMAX Waterjet: Image © OMAX 2010
FDM printer: Photos © UIC 2015 via YouTube
Seamco cartesian robot: Image © Seamco NV
Single-point metal forming video screenshots: Screenshots © 2016 SAE International. All
rights reserved.
7 Gripping and Manipulators
Robotiq grippers: Image © Robotiq 2016
Schmalz vacuum grippers: Image © J. Schmalz GmbH 2016
Nachi IMTS 2016 exhibit: Photo © John Hart. Used with permission.
Robotics vs human labor by industry: Copyright © 2016; The Boston Consulting Group.
All rights reserved
Start Wars image of humanoid robots: Image © Disney

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References
Amazon Picking Challenge screenshots screenshots from Amazon Picking Challenge
2016; © Copyright 2016 IEEE — All rights reserved.
Google Research Blog: Deep learning for robots: Screenshots © Google Research Blog
2016
Rethink Robotics Baxter montage video Video © 2008–2016 Rethink Robotics. All rights
reserved.
Baxter IMTS 2016 exhibit: Photo © John Hart. Used with permission.
Google Research Blog: Deep learning for robots: Screenshots © Google Research Blog
2016
Rethink Robotics Baxter montage video Video © 2008–2016 Rethink Robotics. All rights
reserved.
Baxter IMTS 2016 exhibit: Photo © John Hart. Used with permission.

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