Robots - Getting out of factories and into fields
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Robot technology has been around for a long time, and robots are amazingly good at doing the same thing over and over again. Typically used in factory environments. Hard objects, exact dimensions, known locations. Industrial automation
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Robots - Getting out of factories and into fields
- 1. 17 July 2015 Industrial Automation 9th June Chris Roberts
- 2. 17 July 2015 2 Background
- 3. 17 July 2015 3 Overview of Cambridge Consultants We are a world leader in technology and product innovation 400 engineers, scientists, designers and consultants working from our offices in UK and US For clients world wide, we – develop breakthrough products & systems – create and license intellectual property – provide business consulting in technology critical issues 70% of our work is repeat business – we become trusted partners for our clients Cambridge MA Cambridge UK Singapore
- 4. 17 July 2015 4 Agenda What’s the problem we’re trying to solve here? Robots are excellent at – Repetitive tasks – Hard objects – Exact dimensions – Controlled environments
- 5. 17 July 2015 5 Agenda What’s the problem we’re trying to solve here? Robots are NOT excellent at – Tasks that change – Objects that can’t be gripped firmly – Objects with varying sizes and shapes – Environments that change
- 6. 17 July 2015 6 Future world of work research– 2020 to 2030 Will robots steal jobs? Mature territories Aging workforce, receding retirement age, expecting better jobs Networked, integrated warehouse systems Growing Territories Highly-mobile workforce with high expectations of a good work-life balance Increased uptake of control and automation Emerging Territories High turnover of young staff with low technical skills Remote monitoring of warehouses
- 7. 17 July 2015 7 Robotics and machine vision Science vs Engineering
- 8. 17 July 2015 8 Robotics case study – Amazon picking challenge “ The winning design was capable of picking up 12 objects in 20 minutes ”
- 9. 17 July 2015 9 Machine vision case study – ArcAid Measures the arc of the throw in real time Offers rating, advice
- 10. 17 July 2015 10 Machine vision case study – ArcAid
- 11. 17 July 2015 11 Cambridge Consultants Robotics Project
- 12. 17 July 2015 12 Agenda Why are we doing this now? Robotics experience Low cost sensors and powerful image processing algorithms are available Embedded processing power
- 13. 17 July 2015 13 So, what’s the challenge?
- 14. 17 July 2015 14 There are three main technical challenges to picking fruit or vegetables in a warehouse or field: Grippers / actuators need to cope with softer objects Algorithms and control systems that can cope without exact models of the environment and objects Robots that can interact safely with humans This project focusses on the first two challenges
- 15. 17 July 2015 15 Gripper
- 16. 17 July 2015 16 Custom gripper design
- 17. 17 July 2015 17 Custom gripper design
- 18. 17 July 2015 18 Custom gripper design
- 19. 17 July 2015 19 Vision Processing
- 20. 17 July 2015 20 Vision system Determine which object is on top of a pile of similar objects Real time processing of the images and depth maps No precise description of the object exists The objects are similar but not identical Low cost, commodity hardware Determine where to place gripper
- 21. 17 July 2015 21 Vision system Load Objects Step Process Images Step
- 22. 17 July 2015 22 Vision system 20 40 60 80 100 120 140 160 180 200 20 40 60 80 100 120 140 160 180 20 40 60 80 100 120 140 160 180 200 20 40 60 80 100 120 140 160 180 Process Objects Step
- 23. 17 July 2015 23 Vision system Select Objects Step
- 24. 17 July 2015 24 Conclusions
- 25. 17 July 2015 25 Getting robots into the field? Not quite yet – intermediate step The proof of concept stage is not the hard one Engineering is as important as the science
- 26. 17 July 2015 26 Getting robots into the field? Embedded processor RTOS Vision Driver Image Processing Object Detection Robot Movement Gripper Vision Sensor Object Sensor Driver Low -level Robot control Vacuum Control Gripper Control Robot Arm Vacuum Pump and Valves Depth Sensor
- 27. 17 July 2015 27 Getting robots into the field? Is it worth it? For some applications it will be – but there needs to be a business case Development will be expensive
- 28. 17 July 2015 Cambridge Consultants Ltd Science Park, Milton Road Cambridge CB4 0DW England Cambridge Consultants Inc 101 Main Street Cambridge MA 02142 USA Tel: +44 (0)1223 420024 Fax: +44 (0)1223 423373 Tel: +1 617 532 4700 Fax: +1 617 532 4747 Registered No. 1036296 England Cambridge Consultants is part of the Altran group, a global leader in Innovation. www.Altran.com info@CambridgeConsultants.com www.CambridgeConsultants.com
Editor's Notes
- I’m a physicist by background – I started off in the space industry before moving to product design Experience in chip design – lead multiple mixed signal ASIC developments Now system lead, interested in low power designs Commercial applications of VR Robotics, machine vision
- Robot technology has been around for a long time, and robots are amazingly good at doing the same thing over and over again. Typically used in factory environments Hard objects, exact dimensions, known locations
- Where robots traditionally struggle is doing not quite the same thing, over and over again Fields, shopping (e.g. Amazon picking challenge) Softer objects, no CAD model, no exact locations
- Why try to replace humans? Do they even want to be replaced? Are we stealing jobs? Future world of work 2020-2030 research. examine factors that will influence the way work is done in the manufacturing workplace of the future, and in the associated logistics operations Divided into three territories Mature, e.g. USA, Czech Republic Growing, e.g. India, Botswana Emerging, e.g. Tanzania, Honduras Principle time line is 2020-2030 Mature: see high levels of automation in the manufacturing sector in 2020, but there will be issues with an ageing workforce Growing: will see rapid technology advancements and pressure on employers due to rise in workforce expectations Emerging: Not yet ready for automation But in both the mature and growing territories people will be starting to reject the kind of jobs that can be done by robots.
- There are lots of projects that from a technical point of view are very impressive. The Amazon picking challenge – a robot that can pick any object off the shelf without damaging it – amazing. But if you take a step back and look what the end result was – no where near as good as a human. It’s a HARD technical problem, and the end result was not as good as a human Compare this to the Kiva robots that move the shelves around – technically EASY, but the engineering was HARD
- This is a demo we did for CES last year It was part of elite sports training campaign You took five free throws at the basket, and the app rated your throw in real time and offered training advice Nowhere near as hard as the Amazon picking challenge
- But the image processing was actually pretty easy – spotting a solid coloured circle in an image is a simple algorithm And then taking away the heads , signs and other objects that didn’t move like a ball was also easy This was an EASY technical problem but the ENGINEERING was HARD – getting it to work in the CES environment
- There are some micro influences that are pushing us to do this now Robotics experience We’ve done some really clever things with robots that we can’t talk publicly about yet… …but this is something we can Many projects recently have hinged on combining Low cost sensors with some really clever algorithms running on increasingly capable commodity hardware.
- Cambridge Consultants has been working on a way of getting robots to determine the location of fruit or vegetables and pick them up
- Algorithms and control systems that can cope without exact models of the environment and objects Real time processing Object sensing and categorisation Adaptive control Why is it hard? The science is difficult but not impossible It’s the engineering that’s tricky
- Surveyed existing technology Fixing on fruit and vegetables gives us a limited space to explore don’t have to cope with everything Multiple bellows approach object from all sides Control system only applies suction where there is a seal
- The machine vision processing begins with an optical image and several depth maps generated through structured light Load Objects Step Import images and depth map Pre-processing Process Images Step Selection of area of interest Cropping and scaling
- Process Objects Step Troughs between objects determined Objects segmented and numbered This is where the hardest part is – working out which potato is which
- Orders objects by height Determines where to place gripper
- IF it’s going to succeed in the field, it needs to be robustly engineered Not just a bunch of raspberry pis and image processing running on matlab Low cost, commodity hardware Unnecessary links removed A single processing engine – custom silicon if necessary