Shared control for dexterous telemanipulation with haptic feedback
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Shared Control for Dexterous Telemanipulation with Haptic Feedback Weston B. Griffin Dissertation Defense Presentation May 1, 2003 Telemanipulation First systems developed ~ 1940’s handling radioactive materials Can provide access to dangerous environments

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Shared control for dexterous telemanipulation with haptic feedback l.jpg

Shared Control for Dexterous Telemanipulation with Haptic Feedback

Weston B. Griffin

Dissertation Defense Presentation

May 1, 2003


Telemanipulation l.jpg
Telemanipulation Feedback

  • First systems developed ~ 1940’s

    • handling radioactive materials

  • Can provide access todangerous environments

  • Benefit from natural human abilities

operator

master

slave

environment

[The E1 developed by Goertz at Argonne National Lab]


Telemanipulation3 l.jpg
Telemanipulation Feedback

  • Applications include:

    • underwater salvage

    • nuclear waste handling

    • space station repair

    • minimally invasive surgery

[Intuitive Surgical, Canadian Space Agency, Oceaneering International]


Telemanipulation frameworks l.jpg

position Feedback

force

Telemanipulation Frameworks

  • computer controlled electro-mechanical systems

    remote controlled robot <> feeding back information

  • several different architectures

Operator

Master System

Slave Controller

Slave Manipulator

extends a person’s sensing and/or manipulation ability to a remote location


Manipulation l.jpg

position Feedback

force

Manipulation

  • Desire to leverage human manipulation skills

    • immersive hand/finger based system

Master System

Slave Manipulator


Movie l.jpg
Movie Feedback

  • Remote Control by Andy Shocken

    • filmed 2002 in our lab

    • narrated by Mark Cutkosky


Issues in telemanipulation l.jpg

  • Master system design Feedback

    • difficult task considering complexity of human hands

    • active area of research

  • Enhance slave controller

    • by sharing control between operator and slave system

      - shared control

Issues in Telemanipulation

operator may feel remotely present

BUT

is not getting normal manipulation cues

  • Current telemanipulation limitations

    • force feedback (limited accuracy and fidelity)

    • limited tactile display


Contributions l.jpg
Contributions Feedback

  • Development a human-to-robot mapping method

    • map glove-based hand motions to a planar robot hand

  • Development and implementation of a shared control framework for dexterous telemanipulation

    • combining operator commands with a semi-autonomous controller

  • Investigation of an experimental telemanipulation system

    • results demonstrate benefits of shared control and need to choose carefully types of feedback to achieve a real improvement


Outline l.jpg
Outline Feedback

  • System overview

  • Human-to-robot mapping

  • Shared control framework

  • Experimental investigation

Development of dexterous telemanipulation system


Improving telemanipulation l.jpg
Improving Telemanipulation Feedback

  • Take advantage of the slave controller and local sensor information for improved dexterity

    • add “low-level” intelligence

Why?

  • can feedback sensor information by other means

  • robot can intervene in certain situations (fast response)

  • human and robot can share control for improved performance


Shared control l.jpg

force Feedback

position

commands

sensor feedback

Shared Control

bilateraltelemanipulation

high level

commands & feedback

semi-autonomous dexterous manipulation


Shared control12 l.jpg
Shared Control Feedback

combining operator high level and low level commands

with a remote controller

for improved manipulation


Master system l.jpg

Hand tracker Feedback

CyberGlove

CyberGrasp

Master System

  • CyberGlove™ instrumented glove

    • 22 bend sensors

    • calibrated for dexterous manipulation [Turner 2001]

  • CyberGrasp™ fingertip force feedback

    • lightweight exo-skeleton

    • uni-directional force feedback

  • Logitech hand tracker

    • ultrasonic transducers and sensors

    • 6 d.o.f. position and orientation

[CyberGlove and CyberGrasp are products of Immersion Corporation]


Slave system l.jpg
Slave System Feedback

  • Custom built robot hand

    • two fingers, two d.o.f. per finger

    • low inertia DC motors

    • cable capstan drive

  • Robot arm

    • Adept industrial arm, five d.o.f.

    • enlarges task workspace

  • Fingertip sensors

    • two-axis force sensors

    • contact location sensors


System architecture l.jpg

1000 Hz Feedback

200 Hz

1000 Hz

50 Hz

200 Hz

7 Hz

63 Hz

1000 Hz / 200 Hz

1000 Hz

System Architecture

Master

CyberGrasp

CyberGlove

Indirect Feedback

Wrist Tracker

GUI

QNX Node-to-Node

QNX-Node 1

QNX-Node 2

Adept Control

Slave

Slave Control


Outline16 l.jpg
Outline Feedback

  • System overview

  • Human-to-robot mapping

  • Shared control framework

  • Experimental investigation

Development of dexterous telemanipulation system


Human to robot mapping l.jpg
Human-to-Robot Mapping Feedback

  • Robot is non-anthropomorphic, symmetric, and planar

  • joint-to-joint mapping not possible

  • very different workspace


Human to robot mapping18 l.jpg
Human-to-robot Mapping Feedback

  • How do you control a non-anthropomorphic robot hand using a human hand and glove?

? ?


Virtual object mapping l.jpg
Virtual Object Mapping Feedback

  • Interpret human fingertip motions to be imparting motions to a virtual object held between the fingers

  • Virtual object parameters are mapped to robot

    • to produce fingertip positions OR motions of a grasped object

  • Parameters independently modified

    • to account for kinematic and workspace differences


Virtual object mapping20 l.jpg

0 Feedback

-0.05

-0.1

-0.15

Mapped Pinch Point Position

-0.2

-0.25

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Index Mapped Positions

Left Finger Boundary

Thumb Mapped Positions

Right Finger Boundary

Virtual Object Mapping

  • Match natural human manipulation motions to corresponding robot hand motions

  • good mapping?

    • operator can intuitively control robot and utilize robots workspace


Outline21 l.jpg
Outline Feedback

  • System overview

  • Human-to-robot mapping

  • Shared control framework

  • Experimental investigation

Development of dexterous telemanipulation system


Shared control22 l.jpg
Shared Control Feedback

  • Hannford et al. [1991]

    • force feedback joystick controlling robot arm/gripper

    • improved task completion time and resulted in lower forces

  • Michelman and Allen [1994]

    • sequencing primitives for dexterous hand control

      • joystick control, no provisions for haptic feedback

  • Williams et al. [2002]

    • NASA’s Robonaut project - robot arm and dexterous hand

    • force feedback joystick for control

      • reduced task peak forces


Shared control23 l.jpg
Shared Control Feedback

Next step: using shared control in a dexterous telemanipulation system with fingertip force feedback

How?

  • implement a semi-autonomous controller capable of dexterous manipulation

    • robot has force and tactile sensors and specialized control laws for manipulation


Dexterous manipulation l.jpg
Dexterous Manipulation Feedback

  • What does it mean to autonomously manipulate an object?

    • with sensors robot can detect the object and determine proper fingertip forces for:

manipulation


Dexterous manipulation25 l.jpg
Dexterous Manipulation Feedback

  • What does it mean to autonomously manipulate an object?

    • with sensors robot can detect the object and determine proper fingertip forces for:

manipulation


Dexterous manipulation26 l.jpg
Dexterous Manipulation Feedback

  • What does it mean to autonomously manipulate an object?

    • with sensors robot can detect the object and determine proper fingertip forces for:

manipulation

grasp force regulation


Object manipulation control l.jpg

Velocity Grasp FeedbackTransform

Ts

+

+

ZOH

Tactile Based Object Tracking

-

Tactile Sensing

+

Object ImpedanceController

+

+

Forward Grasp Transform

Finger Controller

RobotFinger

Internal ForceController

+

-

Internal ForceDecomposition

Object Manipulation Control

  • Utilize the Grasp Transform to determine robot fingertip forces [Mason & Salisbury 1985]


Object manipulation control28 l.jpg
Object Manipulation Control Feedback

  • Controlling internal force

Velocity Grasp Transform

Ts

+

+

ZOH

Tactile Based Object Tracking

-

Tactile Sensing

+

Object ImpedanceController

+

+

Forward Grasp Transform

Finger Controller

RobotFinger

Internal ForceController

+

-

Internal ForceDecomposition


Object manipulation control29 l.jpg

Velocity Grasp FeedbackTransform

Ts

+

+

ZOH

Tactile Based Object Tracking

-

Tactile Sensing

+

Object ImpedanceController

+

+

Forward Grasp Transform

Finger Controller

RobotFinger

Internal ForceController

+

-

Internal ForceDecomposition

Object Manipulation Control

  • Controlling object position


Shared control telemanipulation l.jpg
Shared Control Telemanipulation Feedback

  • What are the advantages to programming robot for dexterous manipulation?

    • robot can monitor operator’s object manipulation

    • if necessary, robot can intervene (take over control of object manipulation)

      • impedance modification, limit motion, prevent release

    • robot can warn/inform operator of manipulation status through indirect methods

      • using other feedback modalities (visual indicators, audio, or augmented haptic feedback)


Shared control telemanipulation31 l.jpg
Shared Control Telemanipulation Feedback

  • What are the advantages to letting robot take control over force regulation and/or object manipulation?

    • operator can focus on behavior of grasped object or tool

    • master commands are no longer essential to prevent unwanted slip or damaged objects

    • operator can still override to release or grasp more tightly


Shared control telemanipulation32 l.jpg
Shared Control Telemanipulation Feedback

Shared control implementation issues

  • as the robot assumes more control

    • concern the operator’s sense of presence will be reduced

      • we want to keep the operator “in the loop”

  • preserve operator’s intent

  • what type of indirect feedback is most effective?

  • does sharing control improve performance in an immersive fingertip force feedback system?

  • To answer these questions we perform a set a controlled experiments


  • Outline33 l.jpg
    Outline Feedback

    • System overview

    • Human-to-robot mapping

    • Shared control framework

    • Experimental investigation

    Development of dexterous telemanipulation system


    Previous experimental studies l.jpg
    Previous Experimental Studies Feedback

    • force feedback evaluation

      • Turner et al. 2000: block stacking and knob turning

        • force feedback with CyberGrasp not always a benefit

      • Howe & Kontarinis 1992: fragile peg insertion task

        • audio buzzer sounded if grasp force excessive

        • operators were not able to reduce force

    • shared control evaluation

      • Hannaford et al. 1991: peg insertion task

        • operator’s controlled position, shared orientation control

        • reduction in task completion time and insertion forces


    Experimental hypothesis l.jpg
    Experimental Hypothesis Feedback

    • Addition of a dexterous shared control framework will increase an operator’s ability to handle objects delicately and securely compared to direct telemanipulation


    Experiment description l.jpg
    Experiment Description Feedback

    • Motivating scenario: recovering an ancient Greek vase on the sea floor

    “fragile object handling” - user’s asked to carry an object with minimal force but without dropping the object



    Experiment description38 l.jpg

    If operator’s desired (commanded) force is too low Feedback

    robot can monitor and warn the operator

    OR

    robot can intervene and regulate grasp force

    to prevent object dropping

    Experiment Description

    • To assist operator in fragile object handling taskthe robot computes the minimum grasp force required


    Shared controlled task l.jpg
    Shared Controlled Task Feedback

    • Operator maintains manipulation control


    Shared controlled task40 l.jpg
    Shared Controlled Task Feedback

    • Operator maintains manipulation control

    • Robot and operator share control over internal force

      • robot monitors excessive force


    Shared controlled task41 l.jpg
    Shared Controlled Task Feedback

    • Operator maintains manipulation control

    • Robot and operator share control over internal force

      • robot monitors excessive force

      • robot can apply minimum internal force required to prevent slip


    Sharing control in fragile task l.jpg
    Sharing Control in Fragile Task Feedback

    • Target window with intervention can be wider: desired force can drop below fint,min without adverse effects

    • In theory, it is possible to always do better without intervention


    Question that arise l.jpg
    Question that arise... Feedback

    • Does warning the operator of a possible failure help?

    • Does task performance improve with robot intervention?

    • If robot intervenes, is it necessary to inform operator?

    • Is it helpful to feed back information of impending state changes (such as object release)?

    • With haptic feedback in a force control task, what forces should be fed back?


    Case effects l.jpg
    Case Effects Feedback

    • Audio Alarms - when operator’s desired force is too high or too low

    • Robot Intervention - robot assumes control when operator’s desired force falls below a threshold (safe minimum internal force)

    • Visual Indicator (fingertip LEDs) - to inform the operator of robot intervention

    • Force Feedback: actual vs. commanded - during robot intervention, forces to operator’s fingertip are reduced (reduced force feedback)



    Case effects46 l.jpg
    Case Effects Feedback


    Experimental procedure l.jpg
    Experimental Procedure Feedback

    • Diverse set of subjects

      • 11 subjects total

      • 8 males and 3 females

    • Two sessions

      • first - calibration and training

      • second - four trials for each case

    • Case order randomized

      • reduce possible learning and fatigue effects


    Evaluating performance l.jpg
    Evaluating Performance Feedback

    • Objective data analysis

      • measured internal force applied to object

        • fragile object task - lower is better

      • task failures (number of drops)

      • task completion time

    • Subjective data analysis

      • operator’s expressed preference

      • operator’s perceived difficulty


    Typical subject data l.jpg

    Measured Internal Force FeedbackDesired Internal Force

    Minimum Internal Force

    Case 1

    4

    Force [N]

    2

    0

    0

    5

    10

    15

    20

    25

    Time [sec]

    Measured Internal ForceDesired Internal Force

    101% of Minimum Internal Force

    Case 2

    Excessive Force Warning (Low Tone)

    Object Slip Warning (High Tone)

    4

    Force [N]

    2

    0

    0

    5

    10

    15

    20

    25

    Measured Internal ForceDesired Internal Force

    110% of Minimum Internal Force

    Excessive Force Warning (Low Tone)Object Release Warning (High Tone)

    Robot Intervention

    C

    Case 6

    A

    4

    D

    E

    B

    Force [N]

    2

    0

    0

    5

    10

    15

    20

    25

    Time [sec]

    Typical Subject Data


    Data analysis l.jpg

    3.0 Feedback

    Average of allsubjects for each case

    2.8

    2.6

    2.4

    Internal Force [N]

    2.2

    2.0

    1.8

    average minimum internal force to prevent object slippage

    1.6

    1

    2

    3

    4

    5

    6

    7

    Case Number

    Data Analysis

    • Measured internal force applied to the object

      • averages of each subject for each case (trial failures excluded)

    • Boxplot

      • medians and quartiles

      • observe trends

    • Is there a significant effect?


    Statistical analysis l.jpg
    Statistical Analysis Feedback

    • ANOVA - determines the probability that these results (differences in averages) are really due to random variation in data

    • Apply to averaged measured internal force

      • p = 0.003 (<< 0.05), indicating that there is a difference between the means

      • but which ones are different

    • Can’t use a simple t-test for multiple comparisons

      • increase probability of false-positive

    • Dunnett’s method - comparison to a control (Case 1)

      • Cases 4, 6, 7 have statistically different mean than Case 1

      • a reduction of approximately 15%


    Task failures l.jpg
    Task Failures Feedback

    • Number of failures that occurred for each case (dropped object)

    Number of Failures in Each Case - All Subjects

    8

    6

    Case 5 and 6 had least number of failures

    Number of Failures

    4

    2

    0

    1

    2

    3

    4

    5

    6

    7

    Case Number

    sub1

    sub2

    Number of Failures in Each Case

    N/A

    sub3

    sub4

    8

    sub5

    Case failures not dominated by one subject

    sub6

    sub7

    6

    sub8

    N/A

    sub9

    Number of Failures

    sub10

    4

    sub11

    2

    0

    1

    2

    3

    4

    5

    6

    7

    Case Number


    Objective data analysis results l.jpg

    Internal Force [N] Feedback

    3.0

    2.8

    2.6

    2.4

    2.2

    2.0

    1.8

    1.6

    1

    2

    3

    4

    5

    6

    7

    Case Number

    Objective Data Analysis Results

    • Robot intervention improves performance

      • presence and type of direct and indirect feedback had an effect

        • Cases 4, 6, and 7 had lower internal force

        • Case 3 and 5 did not


    Analysis results l.jpg
    Analysis Results Feedback

    • Robot intervention improved performance

      • presence and type of direct and indirect feedback had an effect

        • Cases 4, 6, and 7 had lower internal force

        • Case 3 and 5 did not

      • only informing of intervention not adequate

        • Case 7 had most failures

        • indicating alarms were helpful

    Number of Failures

    8

    6

    4

    2

    0

    1

    2

    3

    4

    5

    6

    7

    Case Number


    Analysis results55 l.jpg
    Analysis Results Feedback

    • Robot intervention improved performance

      • presence and type of direct and indirect feedback had an effect

        • Cases 4, 6, and 7 had lower internal force

        • Case 3 and 5 did not

      • only informing of intervention not adequate

        • Case 7 had most failures

        • indicating alarms were helpful

    • Reduced force feedback

      • compare Case 3 to 5

      • slight improvement in measured internal force (6%)

      • fewer failures in Case 5

        • Cases 4 and 6 show similar results

    Number of Failures

    8

    6

    4

    2

    0

    1

    2

    3

    4

    5

    6

    7

    Case Number


    Task time l.jpg

    Average of all Feedbacksubjects for each case

    Task Time

    • May reveal any physical or mental difficulties associated with the various conditions

    35

    no obvious trends

    p = 0.82 (i.e., no difference in means)

    30

    25

    Time [Sec]

    shared controldid not improve task completion time BUT did not make it worse

    20

    15

    10

    1

    2

    3

    4

    5

    6

    7

    Case Number


    Results l.jpg
    Results Feedback

    • Given objective data analysis performance criteria

      minimizing internal force but preventing failures:

      provided best overall performance compared to bilateral case

    Case 6 - shared control with multi-modal feedback

    • In post experiment surveys, subjects also generally ranked Case 6 highest in preferenceand ease-of-use


    Conclusions l.jpg
    Conclusions Feedback

    Answering our hypothesis

    • Can the addition of a dexterous shared control framework increase an operator’s ability to handle objects delicately and securely compared to direct telemanipulation?

    YES, shared control gives better performance but you need to:

    a) let the operator know when the intervention is active

    b) let the operator know of impending state changes

    c) feed back force based on commanded force and not actual forces (during intervention)


    Summary of contributions l.jpg
    Summary of Contributions Feedback

    • Development a human-to-robot mapping method

      • map glove-based hand motions to a planar robot hand that allows for intuitive hand control

    • Development and implementation of a shared control framework for dexterous telemanipulation

      • combining operator commands with a semi-autonomous controller

    • Investigation of an experimental telemanipulation system

      • results demonstrate benefits of shared control and need to choose carefully types of feedback to achieve a real improvement


    Future work l.jpg
    Future Work Feedback

    • Do the benefits of shared control extend to other situations and applications?

      • assembly tasks

      • e.g., steer-by-wire vehicles

    • Do the same requirements for shared control improvement hold?

      • informing the operator of intervention

      • notifying of impending state changes

      • modifying the forces fed back


    Acknowledgements l.jpg
    Acknowledgements Feedback

    • Mark Cutkosky

    • Defense Committee

    • Will Provancher

    • The DML

    • Eric (setting the pace in the final days)


    Shared control for dexterous telemanipulation with haptic feedback62 l.jpg

    Shared Control for Dexterous Telemanipulation with Haptic Feedback

    Weston B. Griffin

    Dissertation Defense Presentation

    May 1, 2003


    Slide63 l.jpg

    Backup Slides Feedback


    One slide statistics review l.jpg
    One Slide Statistics Review Feedback

    • statistical analysis

      • two competing hypotheses

        • null: cases have no real effect (all the means are the same)

        • alternate: at least one case is different (all means are NOT the same)


    One slide statistics review65 l.jpg
    One Slide Statistics Review Feedback

    • statistical analysis

      • two competing hypotheses

        • null: cases have no real effect (all the means are the same)

        • alternate: at least one case is different (all means are NOT the same)

    • ANOVA - analysis of variance

      • tests if difference in means of several samples is significant based on variances

    if ratio small then: all means are the same

    within

    if ratio large: at least one mean is different

    • how likely is it to have a t.s. as extreme as observed (p-Value)

    • compare to a significance level (95%)(e.g., reject null if p < 0.05)

    performance quantity

    between

    Case


    Manipulation66 l.jpg
    Manipulation Feedback

    • Desire to leverage human manipulation skills

      • immersive hand/finger based system

    position

    Human-to-robot Mapping

    Operator

    Slave Manipulator

    force

    Master System


    Telemanipulation67 l.jpg
    Telemanipulation Feedback

    • Glove based

      • Brunner et al. 1994, DLR dexterous robot hand

      • Li et al. 1996 - NASA DART project

      • Ambrose et al. 2000, NASA Robonaut project

    • Teleoperation / telemanipulation

      • Lawn and Hannaford 1993

      • Lawrence et al. 1993

      • Daniel and McAree 1998

      • Sherman et al. 2000

      • Speich and Goldfarb 2002


    Control architectures l.jpg
    Control Architectures Feedback

    • general four-channel one d.o.f. framework

    Fh

    C3

    Ve

    C4

    -

    +

    Ve

    Fh

    Vm

    +

    +

    +

    Zm-1

    C1

    Zs-1

    Fh*

    -

    -

    -

    -

    -

    Cm

    Cs

    Ze

    Zh

    +

    Fe

    Vh

    C2

    +

    Fe

    Fe*

    Human Operator

    MasterSystem

    Comm.Link

    SlaveSystem

    Environ-ment

    [Lawrence 1993]


    Mapping background l.jpg

    DLR hand Feedback

    Mapping Background

    • anthropomorphic

      • linear joint-to-joint [Kyriakopoulos et. al 1997]

      • fingertip position mapping

        • scaling [Fisher et a. 1998]

    • semi-anthropomorphic

      • pose matching [Pao and Speeter 1989]

        • joint angle transformation matrix

      • fingertip position mapping [Speeter 1992, Rholing et al. 1993]

        • forward kinematics, inverse kinematics

    • non-anthropomorphic

      • greater dissimilarities

        • grammar based

        • functional mapping[Speeter 1992]

    Utah/MIT hand

    JPL/Salisbury hand

    Dexter hand


    Point to point mapping l.jpg
    Point-to-Point Mapping Feedback

    • initial approach

      • planar projection of fingertip positions

      • standard planar frame transformation


    Mapping implementation l.jpg
    Mapping Implementation Feedback

    • compute virtual object parameters

      • 3D size to capture thumb motion

      • planar reduction

    • computing robot positions

      • based on planar virtual object


    Transformation to robot frame l.jpg
    Transformation to Robot Frame Feedback

    • must modify and scale parameters for desired correspondence

    kinematics

    • v.o. orientation

      • angular offset

    • v.o. midpoint

      • frame transformation

    workspace

    • v.o. midpoint & size

      • scaled


    Parameter determination l.jpg
    Parameter Determination Feedback

    • based on individual’s recorded hand motion

      • three simple poses/motions

    • defining

      • orientation offset

      • midpoint transformation variables

      • midpoint scaling

      • size scaling


    Mapping results l.jpg

    0 Feedback

    Point-to-Point Mapping

    -0.05

    -0.1

    Y-axis, [m]

    -0.15

    -0.2

    0

    -0.25

    -0.25

    -0.2

    -0.15

    -0.1

    -0.05

    0

    0.05

    0.1

    0.15

    0.2

    0.25

    X-axis, [m]

    -0.05

    -0.1

    Y-axis, [m]

    Virtual Object Mapping

    -0.15

    -0.2

    -0.25

    Mapped Pinch Point Position

    -0.25

    -0.2

    -0.15

    -0.1

    -0.05

    0

    0.05

    0.1

    0.15

    0.2

    0.25

    X-axis, [m]

    Index Mapped Positions

    Left Finger Workspace Boundary

    Thumb Mapped Positions

    Right Finger Workspace Boundary

    Mapping Results

    Virtual Object Mapping

    • improved achievable positions

    • pinch-point can be mapped to any point

    • fundamentally analytical

      • continuous, smooth, and predictable

    • fingertip-to-fingertip correspondence


    Modeling l.jpg
    Modeling Feedback

    • Model: averaged percent difference in measured internal force compared to Case 1

    Percent Difference (from Case 1 for each subject) in Mean Internal Force

    Means with Error Bars of Two Standard Deviations

    15

    10

    5

    0

    Percent Difference, [%]

    -5

    -10

    -15

    -20

    -25

    -30

    1

    2

    3

    4

    5

    6

    7

    Case Number


    Model analysis l.jpg
    Model Analysis Feedback

    • Look at residuals:

    Residuals due to Task Order (Learning and Fatigue Effects)

    Means with Error Bars of Two Standard Deviations

    0.4

    0.3

    0.2

    0.1

    Force, [N]

    0

    -0.1

    -0.2

    -0.3

    -0.4

    1

    2

    3

    4

    5

    6

    7

    Order


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