Mul$-Layer Behavioral Mo$on for Complex Robo$c Control with >10 - - PowerPoint PPT Presentation

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Jan 15, 2024 371 likes •830 views

Mul$-Layer Behavioral Mo$on for Complex Robo$c Control with >10 DoF MR. KEVIN WAGNER & DR. JOHN WRIGHT -MILLERSVILLE UNIVERSITY- Need The future of robo+cs in the US Automate manufacturing processes Remain compe++ve in the

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Mul$-Layer Behavioral Mo$on for Complex Robo$c Control with >10 DoF

MR. KEVIN WAGNER & DR. JOHN WRIGHT
MILLERSVILLE UNIVERSITY-

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Need

The future of robo+cs in the US

Automate manufacturing processes
Remain compe++ve in the global marketplace

Robo+cs play a key role in that automa+on Humanoid Robots have a host of capabili+es that may reveal poten+al new uses in industry

Humanoids are not typically used in manufacturing or repeated processes
Humanoids CAN be used to develop new tes+ng and training methods for use manufacturing

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Overview

Prior work with NAO has focused on simple mo+on control and sensory percep+on. How do we develop advanced mo+on control with Timeline? The need for this type of control is most evident in industrial or humanoid robots

Those which u+lize 10 or more DoF
NAO plaKorm contains 25 DoF

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History of NAO Research at MU

2013 ATMAE - Ramos & Wright

Programming the NAO Robotic Humanoid with

Object-Oriented Programming Methodology

2014 ATMAE - Wells, Kilbourne & Wright

Advanced Dynamic Mo+on Control and Object

Tracking for Humanoid Robo+cs

2017 ATMAE – Wagner & Wright

Mul+-Layer Behavioral Mo+on for Complex Robo+c

Control

2012-2013 First NAO Purchased Reading / Object Recogni+on / Facial Recogni+on / Basic Mo+on 2013-2014 Two Addi+onal NAO Purchased Soccer Mo+on Using Timeline Single Behavior/Layer Techniques 2014-2015 Visual and Auditory Percep+on Classical AI Work Begin Assistant Tour Guide Work - Sabba+cal 2015-2016 Increased Mobility – Custom Mo+on using Timeline – Single Behavior/Layer Techniques

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Assistant Tour Guide Development (2014-2015)

u Classical AI Technique – Decision Trees u Speech Recogni+on u Vision Recogni+on u And a licle bit of humor!

hcps://www.youtube.com/watch?v=or_pN1ico9M&t=9s

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Assistant Tour Guide Development (2015)

u Music Files & Mobility

hcps://www.youtube.com/watch?v=lWMgYskFrOg hcps://www.youtube.com/watch?v=4YB9uKdqGGQ&t=3s

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Assistant Tour Guide Development (2016)

u Tracking with Custom Mo+on (Single Layer/Behavior (Timeline)

hcps://www.youtube.com/watch?v=vY1PkwXDSgg&t=1s

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Timeline Editor

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Demonstra$on of NAO

hcps://www.youtube.com/watch?v=edae7ny7r90

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Advantages of Layered Mo$on Programming

vs tradi$onal linear programming

Ease and efficiency in training

One operator may train a complex system
Only one part is trained at a +me

Ease and efficiency in program altera+ons

The en+rety of the program need not be retrained
Operator may change only one layer at a +me

Complex behaviors are now possible

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Future Work

NAO Scooter Tourguide

Hallway naviga+on
Line/Color tracking

Scrip1ng Implementa1on

Faster execu+on

OpenCV Vision Processing

Expanded vision processing capabili+es

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Thank You –

Ques$ons or Comments

Dr. John Wright

Email: John.Wright@Millersville.edu Site: hcp://sites.Millersville.edu/jwright Kevin Wagner Phone: (717) 725-1722 Email: klwagne1@Millersville.edu