- Cambridge Consultants

The Next Industrial Revolution
Giving machines autonomy – and the ability to make decisions
Sensors and Instrumentation 2015
Simon Jordan, Cambridge Consultants
1 October 2015
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.
1 October 2015
Commercially Confidential
2
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’?
1 October 2015
Commercially Confidential
3
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.
1 October 2015
Commercially Confidential
4
‘Robots’ in a wider setting
 ‘Robot’ isn’t a useful word since it covers everything from this to this:
1 October 2015
Commercially Confidential
5
‘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.
1 October 2015
Commercially Confidential
6
Early 21st Century Robotics
1 October 2015
Commercially Confidential
7
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
1 October 2015
Commercially Confidential
8
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…
1 October 2015
Commercially Confidential
9
http://cdn2.alphr.com/sites/alphr/files/styles/insert_main_wide_im
age/public/4/95//hud_nvidia_px.png?itok=N8roiw-U
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?)
1 October 2015
Commercially Confidential
10
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!
1 October 2015
Commercially Confidential
11
ANSWER….
1 October 2015
Commercially Confidential
12
Getting robots out of the factories
and into the fields
1 October 2015
Commercially Confidential
13
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.
1 October 2015
Commercially Confidential
14
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.
1 October 2015
Commercially Confidential
15
Existing machine vision
1 October 2015
Commercially Confidential
16
http://s.hswstatic.com/gif/pc-3-d-brain.jpg
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.
1 October 2015
Commercially Confidential
17
http://enlightenedequine.com/wp-content/uploads/2013/11/Horse-Human-Limb-Comparison.png
The effector
 Horses have no technology – at least
because they cannot use tools.
1 October 2015
Commercially Confidential
 Humans use tools and so can control
their environment. However, our brains
had to be powerful enough to use them.
18
The fruit picking robot
1 October 2015
Commercially Confidential
19
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…
1 October 2015
Commercially Confidential
20
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.
1 October 2015
Commercially Confidential
21
Gripper design decisions
1 October 2015
Commercially Confidential
22
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
Vacuum suckers
 Suckers / grippers are well established – but there are some difficult circumstances:
1 October 2015
Commercially Confidential
23
How to deal with this is a system design question
High mechanical complexity?
High software complexity?
 High per-unit costs
 Long development time.
 Likely to be fragile and therefore
expensive to own.
 Will need extensive testing if it isn’t
to ‘keep getting suck’.
 Tricky to adapt to other tasks
 Low per-unit costs once it’s written.
 May actually have adverse software
requirements…
 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.
1 October 2015
Commercially Confidential
24
Multi-finger vacuum gripper
© Cambridge Consultants
1 October 2015
Commercially Confidential
25
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.
1 October 2015
Commercially Confidential
26
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.
1 October 2015
Commercially Confidential
27
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.
1 October 2015
Commercially Confidential
28
Image segmentation
20
40
60
80
100
120
140
160
180
20
40
1 October 2015
60
80
100
120
140
160
Commercially Confidential
180
200
29
Bringing it all together
 Videos from Cambridge Filmworks
1 October 2015
Commercially Confidential
30
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.
1 October 2015
Commercially Confidential
31
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
Commercially Confidential
32
Cambridge UK
Boston USA
Singapore
www.CambridgeConsultants.com
Cambridge Consultants is part of the Altran group, a global leader
in Innovation. www.Altran.com
1 October 2015
The contents of this presentation are commercially confidential and the
proprietary information of Cambridge Consultants © 2015 Cambridge
Consultants Ltd. All rights reserved.
Registered No. 1036296 England
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/wpcontent/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-0200054-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
1 October 2015
Commercially Confidential
34
Notes
1 October 2015
Commercially Confidential
35
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’?
1 October 2015
Commercially Confidential
36
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.

1 October 2015
Commercially Confidential
37
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.
1 October 2015
Commercially Confidential
38