Implementation of arbitrary path constraints using dissipative passive haptic displays
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Implementation of Arbitrary Path Constraints using Dissipative Passive Haptic Displays. Davin K. Swanson PhD Defense George W. Woodruff School of Mechanical Engineering April 2, 2003. Committee:Wayne Book, ME, Chair Tom Kurfess, ME Kok-Meng Lee, ME Julie Jacko, ISyE

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Implementation of Arbitrary Path Constraints using Dissipative Passive Haptic Displays

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Implementation of Arbitrary Path Constraints using Dissipative Passive Haptic Displays

Davin K. Swanson

PhD Defense

George W. Woodruff School of Mechanical Engineering

April 2, 2003

Committee:Wayne Book, ME, Chair

Tom Kurfess, ME

Kok-Meng Lee, ME

Julie Jacko, ISyE

Chris Shaw, CoC


Haptic Displays

  • Definition: a physical man-machine interface which interacts with a user’s sense of touch

  • Types of haptic effects

    • Kinesthetic: movement of hands, limbs; point forces and torques

    • Tactile: fine touch; texture, temperature

Swanson PhD Defense – April 2, 2003

Introduction


Energetically Active Haptic Displays

  • Most haptic displays are active

    • Electric motors

    • Pneumatics

    • Hydraulics

    • Voice coils

  • Advantages of active devices

    • May generate wide array of control efforts, haptic effects

    • Amplification of human effort

    • Rich control literature

  • Disadvantages of active devices

    • Machine failure or instability can lead to uncommanded motion

    • High forces may cause injury

    • Delicate environments may be damaged

Swanson PhD Defense – April 2, 2003

Introduction


Energetically Passive Haptic Displays

  • Passive displays may only dissipate, redirect, store energy

    • Brakes, clutches, dampers (dissipative)

    • Continuously variable transmissions / CVTs (steerable)

  • All motive energy comes from user

  • Advantages of passive devices

    • Safety

    • Better acceptance by some operators (surgeons, astronauts)

  • Disadvantages of passive devices

    • Limited by passive constraint

    • May not generate arbitrary control efforts

    • Difficult to control; conventional controls not always suitable

Swanson PhD Defense – April 2, 2003

Introduction


indirect coupling between user and environment

Applications of Haptic Displays

  • Teleoperation – force-reflective masters

  • Virtual reality

  • Synergistic devices

    • Direct contact between payload/tool, user, interface

    • Example: cooperative manipulation

Swanson PhD Defense – April 2, 2003

Introduction


Passive Haptics as Synergistic Devices

  • Passive devices are attractive for synergistic applications due to safety advantages

  • Tasks required of synergistic devices:

Investigated previously by Swanson, Book

Focus of this work

Swanson PhD Defense – April 2, 2003

Introduction


Goals of this Research

  • Implementing path constraints is a weakness of dissipative devices (compared to steerable)

  • How well can dissipative devices perform this task?

  • How to fully evaluate performance?

  • Goals:

    • Develop control methodologies to implement path following on dissipative passive devices

    • Generate performance measurements to evaluate these controllers

    • Use human subject testing to evaluate these controllers

    • Correlate physical measurements with qualitative user opinion

Swanson PhD Defense – April 2, 2003

Introduction


Overview of Presentation

  • Background

  • Controller Development

  • Experimental Testbed

  • Human Subject Testing – Design of Experiments

  • Human Subject Testing – Data Analysis

  • Conclusions

Swanson PhD Defense – April 2, 2003

Overview


PTER

“Scooter”

Existing Passive Haptic Devices

  • PTER – Passive Trajectory Enhancing Robot

    • Charles, Book

    • 2 DOF

    • 2 dissipative, 2 coupling actuators

    • Used in this work

  • Cobots

    • Colgate, Peshkin, et.al.

    • Steerable devices

    • Use CVTs or steerable casters

Swanson PhD Defense – April 2, 2003

Background


PADyC

Existing Passive Haptic Devices

  • PADyC – Passive Arm with Dynamic Constraints

    • Troccaz, et.al.

    • Overrunning clutches limit velocities

  • Large workspace brake-actuated device

    • Matsuoka, Miller

    • 3 DOF (2 rotational, 1 prismatic)

    • particle brakes

Swanson PhD Defense – April 2, 2003

Background


Existing Passive Haptic Devices

  • Florida 6 DOF hand manipulator

    • Will, Crane, Adsit

    • Particle brakes

  • PALM-V2

    • Tajima, Fujie, Kanade

    • Variable dampers

  • That’s about it…

Swanson PhD Defense – April 2, 2003

Background


Control of Dissipative Devices

  • PTER path following control (Davis, Gomes, Book)

    • Modified impedance controller

    • Velocity controller; computed desired forces

  • PTER obstacle avoidance (Swanson, Book)

    • Gomes velocity controller

    • Single degree-of-freedom (SDOF) control; selective actuator locking

  • PALM-V2

    • Change damping to control velocity

    • Does not deal with sign differences between actual, desired velocity

  • Brake-actuated lower body orthosis (Goldfarb, Durfee)

    • Power comes from stimulated muscle contraction

    • PD / adaptive control of position and velocity

    • Applied force will always be in direction of desired velocity

Swanson PhD Defense – April 2, 2003

Background


Control of Dissipative Devices

  • PADyC

    • Free motion, position constraint, region constraint

    • Trajectory constraint

      • Only velocity limits may be controlled

      • Define “box” of possible future endpoint positions

      • Velocity limits alter shape, size of box

  • Large-scale 3 DOF display (Matsuoka, Miller)

    • Viscous fields

    • Stiffness modeling

    • Virtual walls (similar to SDOF control)

Swanson PhD Defense – April 2, 2003

Background


Control of Dissipative Devices

  • Very limited previous work in path-following control of dissipative interfaces

    • PALM-V2 does not address situations where force and velocity signs differ

    • Controlled brake orthosis always has force and desired velocity of same sign

    • PADyC has unique actuators (velocity magnitude constraints)

  • No directed work at providing path-following control for:

    • Arbitrary path shapes

    • Unknown external motive forces

    • Dissipative passive haptic displays

  • The door is wide open!

Swanson PhD Defense – April 2, 2003

Background


Overview of Presentation

  • Background

  • Controller Development

  • Experimental Testbed

  • Human Subject Testing – Design of Experiments

  • Human Subject Testing – Data Analysis

  • Conclusions

Swanson PhD Defense – April 2, 2003

Overview


Path Following Control

  • Goal: Allow user free motion along an arbitrary path while preventing motion orthogonal to that path

  • Conventional control methods

    • Assume active device

    • Typically calculate forces / torques to be applied

    • Example: impedance control

Swanson PhD Defense – April 2, 2003

Controller Development


Velocity Field Control

  • Choice of high level controller

  • Control velocities rather than forces / torques

  • “Passive VFC” used by Li, Horowitz to control active manipulators

  • Define velocity field based on desired path

  • Low-level controller deals with achieving desired velocity

  • Velocity direction controlled, magnitude left to the user

Swanson PhD Defense – April 2, 2003

Controller Development


Low Level Controllers

  • Form bulk of control work

  • Must drive link velocities towards desired velocity specified by velocity field

  • Three control concepts:

    • Velocity ratio control

    • Velocity ratio control with coupling elements

    • Optimal controller

Swanson PhD Defense – April 2, 2003

Controller Development


Velocity Ratio Controller

  • Desired velocity may be transformed into link-space

  • Magnitude is unimportant… direction should be controlled

  • Control velocity ratios

    • Reduces controlled DOF by one

    • Makes sense! User has control of DOF along desired path

Swanson PhD Defense – April 2, 2003

Controller Development


Velocity Ratio Controller

  • Compute ratio vector

  • Members represent amount each link must slow down

    • Lower number means more deceleration required

    • Negative number means direction change is necessary

Swanson PhD Defense – April 2, 2003

Controller Development


Velocity Ratio Controller

  • Normalize the ratio vector by largest positive member

  • Goal of controller: guide system towards populated with all ones

  • Special case: no positive elements in

    • All axes must change direction

    • Solution: immobilize device

  • Use to generate control law

Swanson PhD Defense – April 2, 2003

Controller Development


Velocity Ratio with Coupling Elements

  • Some interfaces may contain both dissipative and steerable elements

  • 2 DOF testbed used in this work

    • Two purely dissipative actuators

    • Two dissipative/coupling actuators

    • Allows for greater control flexibility

  • If coupling actuators are feasible, they are preferred

  • Strategy

    • Use a coupling actuator if feasible

    • Otherwise, fall back to standard velocity ratio controller

Swanson PhD Defense – April 2, 2003

Controller Development


Velocity Ratio with Coupling Elements

  • Scale desired velocity for kinetic energy equivalence

  • Generate vector of signs of required accelerations

  • Compute matrix which represents effect of each actuator on each link velocity (-1, 0, or 1)

  • If any row of equals , the actuator represented by that row will be used

  • Otherwise, fall back on velocity ratio controller

Swanson PhD Defense – April 2, 2003

Controller Development


Optimal Controller

  • In previous controller, dissipative and coupling elements separated

  • Use optimal control

    • Single control law dealing with both types of actuators

    • Often used to control “overactuated” systems

  • Minimize a cost function

  • Normally done offline to compute gains or control law

    • Dissipative haptic interfaces have serious nonlinearities

    • Signs of control efforts dependent on signs of link velocities

  • Perform minimization at every time step

    • States considered constant

    • Nonlinearities fall out

Swanson PhD Defense – April 2, 2003

Controller Development


Optimal Controller

  • Optimization at each timestep

    • System is linear

    • If linear cost function is chosen, linear programming can be used

    • Fast, accurate, achievable

  • Goals of cost function

    • Drive system towards desired velocity

      • Primary goal of controller

    • Minimize energy loss

      • Secondary goal to favor coupling elements

  • Constraints

    • EOM of system

    • Actuator limits

Swanson PhD Defense – April 2, 2003

Controller Development


Optimal Controller – CF Elements

  • Velocity control element

    • Controller must be free to deviate from desired velocity direction

    • Set of optimal inputs are control efforts and “optimal” desired velocities

    • Minimize angle between desired velocity and “optimal” desired velocity

    • To make it linear, maximize the numerator

Swanson PhD Defense – April 2, 2003

Controller Development


Optimal Controller – CF Elements

  • Energy element

    • Minimize the reduction in kinetic energy

    • Use negative time derivative as member in the cost function

    • Simple, effective way to favor the coupling actuators

    • Use “optimal” desired velocity and actual velocity to estimate link accelerations

  • Final cost function

Swanson PhD Defense – April 2, 2003

Controller Development


Overview of Presentation

  • Background

  • Controller Development

  • Experimental Testbed

  • Human Subject Testing – Design of Experiments

  • Human Subject Testing – Data Analysis

  • Conclusions

Swanson PhD Defense – April 2, 2003

Overview


PTER – Experimental Testbed

  • PTER – Passive Trajectory Enhancing Robot

Swanson PhD Defense – April 2, 2003

Experimental Testbed


PTER – Experimental Testbed

  • Five-bar linkage; two DOF

  • Actuators: electromagnetic friction brakes

    • Two dissipative (1, 2)

    • Two dissipative/coupling (3, 4)

  • PWM power supplies

  • 6-axis force/torque sensor on handle

  • Digital encoders (50,000 count/rev)

Swanson PhD Defense – April 2, 2003

Experimental Testbed


PTER – Dynamics and Clutch Effects

Swanson PhD Defense – April 2, 2003

Experimental Testbed


Unfiltered Velocity Estimate

Filtered Velocity Estimate

Position

PTER – Control Software

  • Pentium II/450 with Servo-to-Go 8-axis interface card

  • QNX RTOS v6.1

  • Serial port for force sensor

  • 500 Hz update rate

  • Link velocities computed from encoder measurements

    • Backwards difference + 25 Hz 4th order digital Butterworth filter

Swanson PhD Defense – April 2, 2003

Experimental Testbed


0.85

Desired Path

Starting Point

0.8

Applied Force

0.75

0.7

Y position (m)

0.65

0.6

0.55

0.5

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

X position (m)

PTER – Controller Verification

  • Proof-of-concept tests of the three control concepts

  • Desired path: line at y=0.6 m

  • Starting point: (-0.1, 0.8)

  • Force applied by hand, roughly in (3, -1) direction

  • 5cm “buffer distance”

Swanson PhD Defense – April 2, 2003

Experimental Testbed


PTER – Controller Verification

  • Two actuation smoothing routines; used to improve feel

  • Low velocity smoothing

    • Reduces chattering due to velocity sign changes

    • Velocity limit = 0.11 rad/s

  • Velocity direction error smoothing

    • Reduces chattering due to switching sides of the desired velocity vector

    • Angle limit = 0.10 rad

Swanson PhD Defense – April 2, 2003

Experimental Testbed


PTER – Velocity Field Controller

Swanson PhD Defense – April 2, 2003

Experimental Testbed


PTER – VF Controller w/Coupling Elements

Swanson PhD Defense – April 2, 2003

Experimental Testbed


PTER – Optimal Controller

Swanson PhD Defense – April 2, 2003

Experimental Testbed


Overview of Presentation

  • Background

  • Controller Development

  • Experimental Testbed

  • Human Subject Testing – Design of Experiments

  • Human Subject Testing – Data Analysis

  • Conclusions

Swanson PhD Defense – April 2, 2003

Overview


Motivation for Human Subject Testing

  • Controller evaluation

    • Any haptic device has a human in the control loop

    • Human is very difficult to model

    • Comprehensive evaluation of controllers requires human subjects

  • Quantitative measurement of user opinion

    • User opinion important part of device operation

    • Typically requires multiple subjects, survey questions

    • Physical measurements are more accessible, predictable

    • Correlate survey responses with measured physical data

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Design of Experiments


Experimental Design

  • Task: point-to-point motion while following path

    • User instructed to move from start box to end box:

      • As quickly as possible

      • While following path

Focus more on speed

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Design of Experiments


Template Design

  • Four templates representing different paths, areas of workspace

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Design of Experiments


Experimental Setup

  • Templates plotted full-scale

  • Locating board positioned on floor

  • Laser pointer provides visual feedback to user

  • Three locating pins to position templates

  • For each condition, user performs task six times

    • First 2 trials of each condition are practice

  • Data file recorded for each trial

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Design of Experiments


Experimental Conditions

  • Four templates

  • Nine control configurations

    • No control

    • Velocity ratio controller – low and high gains

    • Velocity ratio controller w/coupling elements – low and high gains

    • Optimal controller with no force input – low and high gains

    • Optimal controller with force input – low and high gains

  • Each subject uses all 36 combinations of conditions

    • Four templates presented in random order

    • For each template, nine control setups presented in random order

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Design of Experiments


Recorded Data

  • Physical data recorded for each trial

    • Positions

    • Endpoint forces

    • Actuator commands

  • Survey questions after each condition

    • NASA Task Load Index (TLX)

    • User ranks components of workload on 0-20 scale

      • Physical Demand (PD)

      • Mental Demand (MD)

      • Temporal Demand (TD)

    • Weighted combination of these used to calculate total workload

      • Weights based on subjects’ opinions of importance of each component

    • “Smoothness” component added (not used in workload computation)

  • Effort (E)

  • Performance (P)

  • Frustration (F)

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Design of Experiments


Overview of Presentation

  • Background

  • Controller Development

  • Experimental Testbed

  • Human Subject Testing – Design of Experiments

  • Human Subject Testing – Data Analysis

  • Conclusions

Swanson PhD Defense – April 2, 2003

Overview


Collected Data

  • Nine total subjects

    • Three female, six male

    • Eight right-handed, one left-handed

    • Age: 19 – early 30s

  • 1292 total analyzed trials

    • Nine subjects

    • Four templates

    • Nine conditions

    • Four trials per condition

      • One set of four trials corrupted – not used

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Physical Measurements

  • Path-average path error

    • Accuracy

  • Average desired-path velocity

    • Velocity estimated with six-step balanced difference + smoothing filter

    • Speed

  • Time-average endpoint force

    • Effort / fatigue

  • Endpoint acceleration FFT sum

    • Smoothness

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Statistical Methods

  • Compute sample means of data by group

  • Compute confidence intervals based on standard error

    • 95% C.I.

  • Compare confidence intervals to determine whether population means of different groups are different

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Controllers – Path Error

  • All controlled cases better than uncontrolled

    • VCLo better with a 90% C.I.

  • High gains better than low gains, except for optimal controllers

  • All optimal similar to VLo and VCLo

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Controllers – Path Error

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Controllers – Path Speed

  • VHi and VCHi slower than all other conditions

  • Other controllers’ speeds similar to uncontrolled case

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Controllers – Tip Force

  • Non controlled case lowest

  • VHi and VCHi significantly higher

  • All others slightly higher than uncontrolled

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Controllers – High and Low Gain Cases

  • Gain makes a big difference in velocity ratio controllers

  • Gain does NOT make a big difference in optimal controllers

  • Why?

  • Gains tuned by hand to have similar “feel” across same-gain controllers

  • One subject used for this tuning

  • Not an ideal way to adjust gains for accuracy / feel trade-off

  • If optimal controller high gains were set even higher, difference between high and low gain conditions would be seen

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Survey Data – Total Workload

  • Average tip force shows best correlation

  • Strong linear trend

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Survey Data – Total Workload

  • Secondary influences?

  • No trend with path error

  • Downward trend with path speed

    • Likely a secondary effect

    • Higher endpoint forces = lower path speed

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Survey Data - Smoothness

  • TipaAcceleration FFT sum showed strongest correlation

  • Very strong, linear downward trend

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Survey Data by Controller

  • Workload vs. Controller

    • VHi and VCHi significantly higher

    • No difference between other controllers and non-controlled case

  • Smoothness vs. Controller

    • VHi and VCHi significantly lower

    • Uncontrolled case very high

    • Other cases similar

Swanson PhD Defense – April 2, 2003

Human Subject Testing – Data Analysis


Overview of Presentation

  • Background

  • Controller Development

  • Experimental Testbed

  • Human Subject Testing – Design of Experiments

  • Human Subject Testing – Data Analysis

  • Conclusions

Swanson PhD Defense – April 2, 2003

Overview


Conclusions – Controller Development

  • Three path following controllers proposed…

  • All shown to work on experimental testbed

  • May be applied to any dissipative display with or without coupling elements

  • Velocity ratio controllers do not require a dynamic model

  • Optimal controllers require dynamic model

Swanson PhD Defense – April 2, 2003

Conclusions


Conclusions – Controller Performance

  • Controlled cases result in better path-following performance

    • Higher path following accuracy

    • Same average speed

    • No change in workload

    • Higher endpoint forces

  • Differences between controllers are slight

    • Use of coupling actuators not significant

    • Likely due to nature of task

      • User aware of desired path

      • User attempting to follow path

    • Proof-of-concept test shows use of coupling actuators better when input force and desired velocity are dissimilar

  • Gain tradeoffs

    • High = better accuracy, slower, higher forces

    • Low = reduced accuracy, faster, lower forces

Swanson PhD Defense – April 2, 2003

Conclusions


Conclusions – Survey Metrics

  • Two physical measurements with very strong correlation to survey data

    • Total workload: average tip force

    • Smoothness: tip acceleration FFT sum

  • Controller effects

    • Workload not effected with low gain controllers compared to uncontrolled case

    • Low gain controllers resulted in lower smoothness compared to uncontrolled case

    • High gain velocity ratio controllers had high workload, low smoothness

Swanson PhD Defense – April 2, 2003

Conclusions


Contributions of this Work

  • Three arbitrary path-following controllers which may be applied to any dissipative passive haptic display with or without coupling elements

  • Set of performance metrics to evaluate such controllers

  • Set of physical metrics which may be used to measure or predict user opinion about perceived workload and smoothness

  • Human subject testing framework for evaluation of path-following haptic displays

Swanson PhD Defense – April 2, 2003

Conclusions


Future Directions

  • Application of controllers to different dissipative devices

    • Higher numbers of degrees of freedom

    • Active device could be used to simulate passive actuators, virtual coupling elements

  • Application of controllers to different tasks

    • Surface simulation / virtual walls

    • Obstacle avoidance (expand on previous work – SDOF controller)

  • Evaluate workload and smoothness measurements with other tasks

    • Surface exploration

    • Impedance simulation

    • Teleoperation

  • Improvement of optimal controller

    • Nonlinear optimization

    • Other terms in cost function (perhaps based on workload/smoothness?)

  • Determine if physical demand still primary source of workload on smaller interfaces

  • Investigate use of coupling actuators in other tasks

Swanson PhD Defense – April 2, 2003

Conclusions


Questions?

Swanson PhD Defense – April 2, 2003


Extra slides

Swanson PhD Defense – April 2, 2003


Classes of Passive Displays

  • Dissipative

    • Remove energy from system

    • Resist motion of the device

    • Focus of this work

  • Steerable

    • Constrain one or more DOF

    • Kinematic DOF < Workspace DOF

  • Hybrid

    • Contains both types of elements

    • Typically one type is dominant

    • Addressed in this work

Swanson PhD Defense – April 2, 2003

Introduction


Applications of Synergistic Devices

  • 6 DOF version of PADyC for surgical tool positioning

  • Automobile assembly

    • Many active applications

    • Scooter cobot

Swanson PhD Defense – April 2, 2003

Background


Applications of Synergistic Devices

  • Active surgical robots

  • Kazerooni material handling systems

  • USAF active munitions handler

  • Human-robot load sharing

Swanson PhD Defense – April 2, 2003

Background


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