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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 - PowerPoint PPT Presentation
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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
Swanson PhD Defense – April 2, 2003
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
Introduction
Swanson PhD Defense – April 2, 2003
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
Introduction
Swanson PhD Defense – April 2, 2003
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
Introduction
Swanson PhD Defense – April 2, 2003
Applications of Haptic Displays
• Teleoperation – force-reflective masters
• Virtual reality
• Synergistic devices– Direct contact between payload/tool, user, interface
– Example: cooperative manipulation
Payload
Human Userguides payload
Synergistic Manipulatorsupports payload,constrains motion
Introduction
indirect coupling between user and
environment
Swanson PhD Defense – April 2, 2003
Passive Haptics as Synergistic Devices
• Passive devices are attractive for synergistic applications due to safety advantages
• Tasks required of synergistic devices:
Suitability to task
Task Dissipative Steerable
Free motion Excellent Average
Gravity compensation Below Average Below Average
Path following Average Excellent
Obstacle avoidance Good Excellent
Haptic effects Good Poor
Investigated previously by Swanson, Book
Focus of this work
Introduction
Swanson PhD Defense – April 2, 2003
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
Introduction
Swanson PhD Defense – April 2, 2003
Overview of Presentation
• Background
• Controller Development
• Experimental Testbed
• Human Subject Testing – Design of Experiments
• Human Subject Testing – Data Analysis
• Conclusions
Overview
Swanson PhD Defense – April 2, 2003
Existing Passive Haptic Devices
• PTER – Passive Trajectory Enhancing Robot– Charles, Book
– 2 DOF
– 2 dissipative, 2 coupling actuators
– Used in this work
Background
• Cobots– Colgate, Peshkin, et.al.
– Steerable devices
– Use CVTs or steerable casters
PTER
“Scooter”
Swanson PhD Defense – April 2, 2003
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
PADyC
Background
Swanson PhD Defense – April 2, 2003
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…
Background
Swanson PhD Defense – April 2, 2003
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
Background
Swanson PhD Defense – April 2, 2003
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)
Background
Swanson PhD Defense – April 2, 2003
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!
Background
Swanson PhD Defense – April 2, 2003
Overview of Presentation
• Background
• Controller Development
• Experimental Testbed
• Human Subject Testing – Design of Experiments
• Human Subject Testing – Data Analysis
• Conclusions
Overview
Swanson PhD Defense – April 2, 2003
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
Controller Development
Swanson PhD Defense – April 2, 2003
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
Controller Development
Swanson PhD Defense – April 2, 2003
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
Controller Development
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Swanson PhD Defense – April 2, 2003
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
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Controller Development
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Swanson PhD Defense – April 2, 2003
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
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Controller Development
Swanson PhD Defense – April 2, 2003
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
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Controller Development
Swanson PhD Defense – April 2, 2003
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
Controller Development
Swanson PhD Defense – April 2, 2003
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
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Controller Development
Swanson PhD Defense – April 2, 2003
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
Controller Development
Swanson PhD Defense – April 2, 2003
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
Controller Development
Swanson PhD Defense – April 2, 2003
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
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Controller Development
Swanson PhD Defense – April 2, 2003
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
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Controller Development
Swanson PhD Defense – April 2, 2003
Overview of Presentation
• Background
• Controller Development
• Experimental Testbed
• Human Subject Testing – Design of Experiments
• Human Subject Testing – Data Analysis
• Conclusions
Overview
Swanson PhD Defense – April 2, 2003
PTER – Experimental Testbed
• PTER – Passive Trajectory Enhancing Robot
Experimental Testbed
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Swanson PhD Defense – April 2, 2003
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)
Experimental Testbed
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Swanson PhD Defense – April 2, 2003
PTER – Dynamics and Clutch Effects
Experimental Testbed
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Swanson PhD Defense – April 2, 2003
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
Experimental Testbed
Position Unfiltered Velocity EstimateFiltered Velocity Estimate
Swanson PhD Defense – April 2, 2003
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”
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X position (m)
Y p
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m)
Desired PathStarting PointApplied Force
Experimental Testbed
Swanson PhD Defense – April 2, 2003
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
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Experimental Testbed
Swanson PhD Defense – April 2, 2003
PTER – Velocity Field Controller
Experimental Testbed
Swanson PhD Defense – April 2, 2003
PTER – VF Controller w/Coupling Elements
Experimental Testbed
Swanson PhD Defense – April 2, 2003
PTER – Optimal Controller
Experimental Testbed
Swanson PhD Defense – April 2, 2003
Overview of Presentation
• Background
• Controller Development
• Experimental Testbed
• Human Subject Testing – Design of Experiments
• Human Subject Testing – Data Analysis
• Conclusions
Overview
Swanson PhD Defense – April 2, 2003
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
Human Subject Testing – Design of Experiments
Swanson PhD Defense – April 2, 2003
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
Human Subject Testing – Design of Experiments
Focus more on speed
Swanson PhD Defense – April 2, 2003
Template Design
• Four templates representing different paths, areas of workspace
Human Subject Testing – Design of Experiments
Swanson PhD Defense – April 2, 2003
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
Human Subject Testing – Design of Experiments
Swanson PhD Defense – April 2, 2003
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
Human Subject Testing – Design of Experiments
Swanson PhD Defense – April 2, 2003
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)
Human Subject Testing – Design of Experiments
• Effort (E)
• Performance (P)
• Frustration (F)
Swanson PhD Defense – April 2, 2003
Overview of Presentation
• Background
• Controller Development
• Experimental Testbed
• Human Subject Testing – Design of Experiments
• Human Subject Testing – Data Analysis
• Conclusions
Overview
Swanson PhD Defense – April 2, 2003
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
Human Subject Testing – Data Analysis
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X (m)
Y (
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Desired PathActual Path
Swanson PhD Defense – April 2, 2003
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
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Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
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
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Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
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 NoCon VLo VHi VCLo VCHi OnFLo OnFHi OFLo OFHi
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Mean Average Path Error vs. Controller with 95% CI
Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
Controllers – Path Error
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Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
Controllers – Path Speed
• VHi and VCHi slower than all other conditions
• Other controllers’ speeds similar to uncontrolled case
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Mean Average Path Speed vs. Controller with 95% CI
Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
Controllers – Tip Force
• Non controlled case lowest
• VHi and VCHi significantly higher
• All others slightly higher than uncontrolled
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Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
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
Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
Survey Data – Total Workload
• Average tip force shows best correlation
• Strong linear trend
1 2 3 4 5 6 7 8 9 10 11 12 13 14 1610
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Total W orkload (0-20)
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Mean Average Tip Force vs. W orkload with 95% CI
Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 165
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Total W orkload (0-20)
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Mean Average Path Error vs. W orkload with 95% CI
1 2 3 4 5 6 7 8 9 10 11 12 13 14 160
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0.2
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0.3
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0.4
0.45
0.5
Total W orkload (0-20)
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an
Av
era
ge
Pa
th S
pe
ed
(m
/s)
Mean Average Path Speed vs. W orkload with 95% CI
Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
Survey Data - Smoothness
• TipaAcceleration FFT sum showed strongest correlation
• Very strong, linear downward trend
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 200
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Smoothness (0-20)
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Mean Tip Accel FFT Integral vs. Smoothness with 95% CI
Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
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
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5
6
7
8
9
10
11
Controller
To
tal
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(0
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)
W orkload Rating vs. Controller with 95% CI
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6
8
10
12
14
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Controller
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Smoothness Rating vs. Controller with 95% CI
Human Subject Testing – Data Analysis
Swanson PhD Defense – April 2, 2003
Overview of Presentation
• Background
• Controller Development
• Experimental Testbed
• Human Subject Testing – Design of Experiments
• Human Subject Testing – Data Analysis
• Conclusions
Overview
Swanson PhD Defense – April 2, 2003
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
Conclusions
Swanson PhD Defense – April 2, 2003
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
Conclusions
Swanson PhD Defense – April 2, 2003
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
Conclusions
Swanson PhD Defense – April 2, 2003
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
Conclusions
Swanson PhD Defense – April 2, 2003
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
Conclusions
Swanson PhD Defense – April 2, 2003
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
Introduction
Swanson PhD Defense – April 2, 2003
Applications of Synergistic Devices
• 6 DOF version of PADyC for surgical tool
positioning
• Automobile assembly– Many active applications
– Scooter cobot
Background
Swanson PhD Defense – April 2, 2003
Applications of Synergistic Devices
• Active surgical robots
• Kazerooni material handling systems
• USAF active munitions handler
• Human-robot load sharing
Background
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