the next industrial revolution - cambridge consultants · we’re showing three innovations here...
TRANSCRIPT
1 October 2015
The Next Industrial Revolution
Giving machines autonomy – and the ability to make decisions
Simon Jordan, Cambridge Consultants
Sensors and Instrumentation 2015
1 October 2015 2 Commercially Confidential
Introduction
Cambridge Consultants is a multidisciplinary
product development company.
We have been active in robotics for a while –
our largest project is a warehouse automation
system for a well known name.
This work is confidential – but today is about
another robot development and gives a view on
how robots fit an industrial society.
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The robot worker – how does it fit in the value chain?
Aside from inventing economics, Adam
Smith is well known for the example of the
pin factory.
The idea is that people can become
specialised and efficient in one small task.
Existing robots take this to an extreme –
they typically take one small task and repeat
it to tight tolerances.
But – does this make them ‘useful idiots’?
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But robots aren’t as good as the general public expect
Interaction with the real world is hard, and only narrow areas have been shown to be
useful.
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‘Robots’ in a wider setting
‘Robot’ isn’t a useful word since it covers everything from this to this:
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‘Robots’ in a wider setting
… so let’s be clear, by ‘robot’ I mean:
– A machine which can help a person be more economically productive by ‘doing
the boring stuff for them’.
And some tasks which a machine might do are:
– Driving to work
– Checking out groceries
– Assembling a camera lens
– Filing a management report
But management need to see a business case before signing a capex for a new
machine – and this means it needs to be cheaper (not necessarily faster or better)
than a human.
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Early 21st Century Robotics
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Factory robots
Typical usage is to plan (or teach), store
the program and repeat when triggered by
the PLC.
External sensors sometimes used to take
up tolerances or to fine tune positioning.
The performance is only as good as the
program – it’s really just a CNC machine.
The flexibility is at the capital rather than
operational level – one type of machine
can fulfil many roles.
First tool-changer CNC machine
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Self-driving cars
Huge advances have been made in
the last few years, notably image
classification and collision
avoidance. It’s an amazing
achievement to do this.
Cars process a lot of information
(LIDAR and multiple cameras): they
have much higher ‘awareness’ than
a human driver.
But, there’s a key problem here…
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Self-driving cars – map data
The map of the road is known in advance!
(is this really any better than the 1960’s paper tape?)
http://cdn2.alphr.com/sites/alphr/files/styles/insert_main_wide_im
age/public/4/95//hud_nvidia_px.png?itok=N8roiw-U
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Where are the places where robots can make big gains?
An ideal area is:
– Has a high total value (can be low value but
done in massive quantity)
– Has been tricky to automate and so is
reliant on people.
– Is margin sensitive so improvements
change the bottom line.
– The businesses in the field are large
enough to invest in doing things a different
way and meet the cost of ownership1.
Look at the hierarchy of needs:
1 this probably excludes domestic robots!
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ANSWER….
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Getting robots out of the factories
and into the fields
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What does a robot need to work here?
1. To be at the right point in the value chain.
Performing complex tasks like vine pruning is
possible (at an academic level) but isn’t anywhere
near a real farm yet.
2. To be able to work without any form of drawing as
it’s dealing with random objects and not
engineered parts. This means better sensing of the
environment and the ability to make decisions
without supervision.
3. To have an effector which is useful in the real world
– human hands are amazing but haven’t been
beaten by engineers yet.
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Picking a point in the value chain
We need a point where we can make a difference – the
engineering NRE will be repaid by cost savings within a
sensible timeframe.
Planting? Not really needed as seed is cheap and we can
waste some.
Weeding? Already happening but could be limited by
ability to discriminate between plands
Harvesting? Getting closer, but much is done by machine.
Also, seasonal which makes machines hard to amortise.
Fruit and vegetable handling – yes! Lots of human labour,
can be indoors, predictable enough, massively price
sensitive. Also, some high value crops (specialist fruit,
almonds) where early adopters can see a good return.
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Existing machine vision
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Existing machine vision
This, again, has advanced massively. The examples
shown fulfil some well defined categories:
– Binary classification (‘is this bottle full?’)
– Data extraction (‘what serial number is this?’)
– Guidance (‘what angle are the threads at?’)
But these work on a narrow problem space – what is
expected is very well defined and so highly
programmable / teachable.
Also, 3D is new-ish to this field and could be
essential.
http://s.hswstatic.com/gif/pc-3-d-brain.jpg
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The effector
Horses have no technology – at least
because they cannot use tools.
http://enlightenedequine.com/wp-content/uploads/2013/11/Horse-Human-Limb-Comparison.png
Humans use tools and so can control
their environment. However, our brains
had to be powerful enough to use them.
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The fruit picking robot
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So what are we showing?
We want to show a demo which:
– Puts robots into a new context
– Shows an innovative step such as new machine vision capability
– Has good system integration…
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We’re showing three innovations here today
1. Novel application. Bin picking (the generic problem of getting objects out of stacks
or piles) is cutting edge robotics and is typically only attempted for engineered parts.
2. A flexible effector. Can pick up a screwdriver, but also handle a peach without
bruising.
3. Cutting edge machine vision – identification and guidance for random objects
without extensive training.
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Gripper design decisions
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Vacuum suckers
Suckers / grippers are well established – but there are some difficult circumstances:
http://biology.clc.uc.edu/fankhauser/Classes/Bio_103/Apple_calyx_stamen_P2210018.JPG
http://cdn4.pacifichorticulture.org/wp-content/uploads/2009/07/Barn.01.jpg http://www.teknoscienze.com/Contents/Articoli/Images/2013/-%20AF3%202013/Lourith/dragon-fruit.jpg
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How to deal with this is a system design question
High mechanical complexity?
High per-unit costs
Likely to be fragile and therefore
expensive to own.
Tricky to adapt to other tasks
May actually have adverse software
requirements…
High software complexity?
Long development time.
Will need extensive testing if it isn’t
to ‘keep getting suck’.
Low per-unit costs once it’s written.
Flexible (if you can afford the NRE)
We’ve gone for a compromise – the gripper is soft so it can work from an
imprecise location yet wasn’t excessively complex to build.
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Multi-finger vacuum gripper
© Cambridge Consultants
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Vision system requirements
Aim is to pick fruit and vegetables from a pile.
Vision system has to work out:
– How the objects are connected (if at all)
– Which object is best to pick (near the top but not jammed)
– Where it is
– What’s the best angle to approach from.
This requires depth perception – 3D camera.
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Kinect Sensor
Projects an array of dots onto the subject, their displacement from their
expected position is used to calculate depth.
Not an ideal sensor as the voxels are a bit big but it already exists.
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Image Segmentation
Well understood technique for conventional images to detect connected regions.
Can also be applied to depth map to detect and separate objects.
We have developed this technique and applied to this problem.
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Image segmentation
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Bringing it all together
Videos from Cambridge Filmworks
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What were the key challenges?
Machine vision – no standard algorithms to do this, needed some research and then
new code writing.
The gripper – getting the right balance between flexible but highly complex and
simple but limited in its application.
Bringing it all together – some standard components, but system integration and
getting a smooth outcome is quite time consuming.
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Conclusions
This project shows an appropriate match between ‘what engineers can achieve’ and
‘what the customer needs’.
This is only possible by thinking at a system level, for example:
– Right compromise between software and mechanical complexity.
– Integrated vision, commercial arm and novel mechanics
New applications for robotics may not be a result of huge software and infrastructure
investments – more a result of novel algorithm and signal processing solutions.
1 October 2015
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1 October 2015 34 Commercially Confidential
Image Credits
Introduction: CC
The Robot Worker: http://www.gutenberg.org/files/38367/38367-h/images/fig319.png http://www.thwink.org/sustain/deadlock/E2_WealthOfNations.jpg http://australianroboticsreview.com/wp-
content/uploads/2015/08/Robot-Integration.jpg
But Robot’s Aren’t… https://www.youtube.com/watch?v=W7NjFGZ3tz8 http://www.cityam.com/article/how-intelligent-robots-stand-poised-change-all-our-lives-better
Robots in a wider setting https://upload.wikimedia.org/wikipedia/commons/c/c8/Sir_Killalot_spear.jpg http://www.mdpi.com/robotics/robotics-02-00054/article_deploy/html/images/robotics-02-
00054-g004-1024.png
Factory Robots: http://bobcad.com/wp-content/uploads/2014/05/Milwaukee-Matic-II-FirstToolChanger.jpg https://upload.wikimedia.org/wikipedia/commons/0/00/PaperTapes-5and8Hole.jpg
Self Driving Cars: http://cdn2.alphr.com/sites/alphr/files/styles/insert_main_wide_image/public/4/95//hud_nvidia_px.png?itok=N8roiw-U
Where are the places: http://theskooloflife.com/wordpress/wp-content/uploads/2009/05/maslows-hierarchy.gif http://spectrum.ieee.org/img/armar-kit-1387486756966.jpg
Answer: http://www.qlocal.co.uk/southport/news_list/Gangmasters_Abuse_Southport_Workers-50527792.htm http://www.earthcollective.net/wp-content/uploads/2012/03/Packhouse2.jpg
https://www.peaceriverpacking.com/harvesting.html
What does a robot need? http://abe-research.illinois.edu/faculty/grift/research/biosystemsautomation/agrobots/AgBoHiRes2.JPG
Picking a point http://extension.oregonstate.edu/umatilla/mf/sites/default/files/imagecache/preview_500/pictures/tns_packing_line.jpg
Existing machine vision http://www.microscan.com/en-us/technology/machinevisionsystems/AppsforAutomotive.aspx http://www.conceptauto.com.au/images/services/bc-2.jpg
http://www.automation.com/images/article/omron/MVWP3.jpg
Kinect Sensor: http://userpage.fu-berlin.de/~latotzky/wheelchair/wp-content/uploads/kinect1_cropped.png https://graphics.stanford.edu/~mdfisher/Images/KinectIR.png
Image Segmentation: http://cs.nyu.edu/~silberman/images/nyu_depth_v1_preprocess.jpeg
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Notes
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Notes #1
TITLE: “The next industrial revolution' - giving machines autonomy and the ability to make
decisions.”
S1: machines struggle with situations they haven’t been programmed to expect. Neural
networks / deep learning can cope to an extent but there’s a lengthy training time.
S2: Huge steps made (up to self driving cars) but they depend on massive infrastructure (and
who owns your data?)
C: This is about being cheaper than a human not faster / more reliable. (S.D. cars are about
overall system costs ie not owning a car)
Q? Which area spends a lot of money on people but is already ‘cost optimised’?
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Definitions
IoT
Industry 4.0 – connecting machines, workpieces and systems: create an intelligent value chain.
Better status / health info to management. JIT maintenance, near-zero downtime.
IoS – cloud computing.
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Why did we do Scrumping?
Robots repeatable not accurate, keeping them as CNC machines really.
Taken picking as a challenge, using new machine vision
Machine can now take decision of how to pick
– Which one
– Position and angle
– How hard to suck
– Has it got it?
Integrates maths, s/w and mechanics.