Biomimetic

bouncerarcheryAI and Robotics

Nov 14, 2013 (3 years and 9 months ago)

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Biomimetic Sensing for
Robotic Manipulation

Neil Petroff, Ph. D. Candidate

University of Notre Dame

Lerner Research Institute

Cleveland, OH

December 8, 2005

Outline


Me on Me


Grasping


biology as motivation for current work


Robotic Manipulation


Nonholonomic motion planning


Motion planning for stratified systems


Open
-
Chain Manipulators


Forward kinematics


Inverse kinematics


Biomimetic Robot Sensors


Vision, touch


Control Perspective on Deep Brain Stimulation


The Rest of the Story

Hand Orthosis

Target Group: C5
-

C7 SCI


3 Grasps


Fingertip, key, cylindrical


Increase Autonomy


Mercury Orthotics


Rehabilitation technology


therapeutic


quality of life




Grasping


Interaction


Creation


Task Execution

Grasping

Hand Orthosis Robotic Manipulation Fuzzy Logic Open
-
Chain Manipulators Biomimetic Robot Sensors Work
to Date

Grasping

Can we improve robotic manipulation by imbuing robots with
useful human characteristics?

Robots



Humans

Poor at fine motion

good at fine motion

No feedback



vision, proprioception

structured




adaptive

precise




robust

rapid





slow

strong





variable

stamina



need to rest

Grasping

Hand Orthosis Robotic Manipulation Fuzzy Logic Open
-
Chain Manipulators Biomimetic Robot Sensors W
ork to Date

Biological Motivation


Haptic Recognition


Force feedback


Compliance is Useful for Manipulation


Brain Model


Fuzzy logic


Hierarchical Control


Grasping Hand Orthosis Robotic Manipulation Fuzzy Logic Open
-
Chain Manipulators Biomimetic Robot Sensor
s Work to Date

Biological Control Loop

desired

task

motion planning

algorithm

inverse

kinematics

encoder

counts

PID

Robot

current

configuration

encoder

counts

sensor

readings

trajectory

adjustment

fuzzy

supervisor

Testbed

Robotic Motion Planning


Steering Using Piecewise Constant Inputs


This is a geometric analysis


Provides a systematic approach for establishing controllability


Applicable to underactuated systems with nonholonomic
constraints


Exact for nilpotent systems of the form



Driftless


Not all g
i
’s may exist


a system is nilpotent if all Lie brackets greater than a certain order are
zero


Lie bracket motions


allows the system to move in a new direction

1 1 2 2
( ) ( ) ( )
m m
x g x u g x u g x u
   
Lie Bracket Motions

Flow along
g
3

can be approximated by
flowing along
g
1

and
g
2

Higher order brackets can be generated,
e.g.




4 1 3 1 1,2
,,
g g g g g g
 
 
 
 
 
Example

Parallel parking a car

Example

Car equations

g
1

g
2

,
0
0
0
2
cos
1
3

















l
g
















0
0
2
2
cos
cos
cos
sin
4




l
l
g
1 1 2 2 3 1 4 2
x g u g u g v g v
   
Extended System





y
x
,
l

2
1
1
1
0
0
0
0
tan
sin
cos
u
u
y
x
l





















































Car Simulation

Why Didn’t it Work?


The Car Model is not Nilpotent


g
5
points in the same direction as g
3


Motion along lower order brackets induces motion
along higher order brackets


Solution


Iterate


Feedback nilpotentization


Other Drawbacks


Small Time or Small Inputs


obstacle avoidance


Open Loop


highly susceptible to modeling errors


no error correction

Stratified Systems


Extends motion planning algorithm to systems with discontinuities


Intermittent contact


locomotion


manipulation

S

1

2

S
1

S
2

g
1,1

g
1,2

-
g
1,1

-
g
2,1

g
2,2

g
2,1

M=S
0

Neither finger
in contact

finger 2 in
contact

finger 1 in
contact

Both fingers
in contact

stratum

Control Architecture

Desired

task

motion planning

algorithm

Open
-
Chain Manipulators

Forward kinematics





6 6
1 1
ˆ
ˆ
0
st st
g e e g




Product
-
of
-
exponentials formula

A configuration is of the form










1
0
0
0
P
R
g
s

P

T

Inverse Kinematics

The inverse kinematics solution is not unique


90
1


1

1

1

1

0
2



90
2


0
1


)
1
,
1
(
)
1
,
1
(
Inverse Kinematics


PUMA geometry makes an analytical solution tractable



b
w
st
d
b
w
p
p
g
g
p
p




0
e
1
ˆ
3


Inverse Kinematics

14” diameter circle

Control Architecture

Desired

task

motion planning

algorithm

inverse

kinematics

encoder

counts

PID

Robot

current

configuration

current

counts

fuzzy

supervisor

Biomimetic Sensing

Force Sensors


Feedback at Finger/Object Junction


Piezoelectric


Used in biomedical testing


Compliant


Tend to drift under static load


Flexiforce Sensor


Finding an Object

Control Architecture

desired

task

motion planning

algorithm

inverse

kinematics

encoder

counts

PID

Robot

current

configuration

encoder

counts

sensor

readings

fuzzy

supervisor

trajectory

adjustment

Summary


So Far


Built a closed loop system to perform robotic
manipulation


stratified motion planning


inverse kinematics solution


force feedback


To Do


Manipulation


Currently working on simulation


apply to robots


Control Perspective on DBS

(or “What the heck am I doing here?”)


Underlying manipulation technique is a geometric
approach to nonlinear controls


Nonlinear control lies at the forefront of modern control
methods


One of the most intriguing aspects of nonlinearity is that
of chaos


Nonlinear control techniques have been used to
suppress cardiac arrythmia, a chaotic process


Is neuron transmission chaotic?


at the heart of successful treatments using deep brain
stimulation is the ability to control chaos


Robust and nonlinear control techniques provide an
analytical foundation on which to study such systems


Soft computing techniques provide an additional
approach that while not at rigorous may yield equal or
better results

Open Questions on DBS


By approaching DBS from a control Theory
Standpoint, Can We


Control with external stimulation locally?


Filter the signals?


Characterize which signals cause which disruptions


stimulation can suppress dyskinesia


tremors tend to lessen during movement


Keep symptoms from returning with fatique?


Muscle spasticity


Completely eliminate meds?



The Rest of the Story


54,000 SCI


Additional 2,800 / yr at C5


C6 level


Parkinson’s affects 750,000


1 million people in the U.S.


Other Pathologies


Hemiplegic stroke


Multiple sclerosis


Muscular dystrophy


Rehab


Funding


Competition for startup money


Who Can Pay?


Hand Mentor from KMI


$3,950


Coverage from private insurance companies in only 2 states


Currently no medicare coverage


State of Indiana Home and Community Based Care Act


Provides funding for community and home
-
based care


2002: 84 / 16


Medicaid savings of $1,300 per client per month


Savings on the order of 3:1 when compared with institutional care


My Plea


As researchers, I believe we have a responsibility
to pursue noble goals


Obligation of the Engineer


“… conscious always that my skill caries with it the
obligation to serve humanity …”


Hippocratic Oath


“I will remember that I do not treat a fever chart, a
cancerous growth, but a sick human being, whose
illness may affect the person's family and economic
stability. My responsibility includes these related
problems, if I am to care adequately for the sick.”


“will remember that I remain a member of society, with
special obligations to all my fellow human beings,
those sound of mind and body as well as the infirm.”

On a Lighter Note