February 20-22, 2013 | Orlando World Marriott Center | Orlando, Florida USA

Intelligent Flexible AutomationDavid Peters

Chief Executive OfficerUniversal Robotics

Trends in AI and Computing Power

Convergence of Artificial Intelligence Capabilities Hardware (Computers)1 Software (Artificial Intelligence)2

2nd Generation ’57 – ’63 Transistors 1st Generation ’56 – ’74 Initial artificial intelligence 3rd Generation ’64 – ’71 Integrated circuits

4th Generation ’72 – Now Microprocessors2nd Generation ’75 – ’87 Expert systems

3rd Generation ’88 – Now AI for specific industries & problems

5th Generation Now – Future AI devices with massive parallel processing

4th Generation Now – Future Intelligence based on learning pattern of living beings

 

[1] http://www.webopedia.com/DidYouKnow/Hardware_Software/2002/FiveGenerations.asp [2] http://en.wikipedia.org/wiki/Artificial_intelligence

Overlapping Technology VectorsNvidia computing: X teraflopUniversal Robotics intelligence: Neocortex

Big Data – It’s all a matter of perspective

• Flexible intelligence requires handling lots of data, but…• Big is not big for algorithms and computers• Data reduction examples:

• 80KB of data for individual face recognition• Cartons: 20KB of data for unique carton/package recognition

• Single bar code: 3KB data for specific label information• Volume:

• 12,500 U.S. large distribution centers (> 100K SQ FT)• Throughput: 5M cartons/yr/DC @ 12,500 = 62.5B cartons/yr

• Data on every carton for a year = 780 TB• nVidia Parallel processor Tesla Kepler 10

• Process simple calculation on all 780 Terabytes in under 3 minutes!!

Algorithm that Mimic Learning

• Artificial Intelligence uses sensor input to learn• Sensory Motor learning loop: (act sense react)• Bottom-up design• Hardware agnostic• Simplifies complexity/chaos• Improves process via operational insight• BIG DATA reduced for comprehension• It’s the Way the Real World WorksTM\

• Use: Both data analysis and automated control

3-D Vision

• Animals with stereo vision understand depth intuitively• Disparity

• Algorithms need Cartesian coordinates – x, y, z• Point Clouds – 3D coordinates on an object surface

• E.g. – UR combined in real-time 4 point clouds for composite 3-D• Resolution – the distance between the points• Vision analysis uses traditional operators – blob, edge

detection, matching, measuring• Sensors – Structured Light, Camera pairs (Stereopsis),

Laser, Light Detection And Ranging (LIDAR)• Processing time >500ms (human reaction time 250ms)

Motor Control

• Real-time kinematics, path planning and obstacle avoidance

• High speed interface• Machine reacts to variations of task based on sensing• Any type of actuation – whatever is necessary for the job

from this: to this:

Automating IntelligenceTM

1. 3-D Sensing to find randomly placed objects• Spatial Vision Robotics uses sensors for data analysis• Maps 3-D space• Scalable 3-D precision by utilizing a range of sensors• Provides accurate 3-D vision guidance and 3-D inspection

2. Motor Control to drive machines reactively• Autonomy software automates robot programming• Integrates kinematics, path planning, & obstacle avoidance

3. Intelligence to learn new tasks• Neocortex learns how to handle never-seen-before objects• New form of Artificial Intelligence • Responds dynamically to change with real-time sensory input• Uses memory to match what is known with what it is learning

Intelligent Flexible Automation Applications

• Random Pick of Difficult Objects with Inspection• Deformable objects – bags partially filled• Semi-rigid objects – rubber blocks• Cosmetic bottles – clear, metallic, odd shapes

• Random 3-D Inspection• Package tracking & sorting - random objects & labels, locations

• Random Depalletization• Unlimited quantity of boxes – mixed pallet• Varying location and orientation - 6 DOF

David PetersChief Executive Officer

Universal Robotics, Inc.

PO Box 171062Nashville, Tennessee 37217 USA

Phone: (615) 366-7281davidpeters@universalrobotics.com

www.universalrobotics.com

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