Implementation of Arbitrary Path Constraints using Dissipative Passive Haptic Displays

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

29. PTER – Experimental Testbed • PTER – Passive Trajectory Enhancing Robot Swanson PhD Defense – April 2, 2003 Experimental Testbed

30. 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

31. PTER – Dynamics and Clutch Effects Swanson PhD Defense – April 2, 2003 Experimental Testbed

32. 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

33. 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

34. 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

35. PTER – Velocity Field Controller Swanson PhD Defense – April 2, 2003 Experimental Testbed

36. PTER – VF Controller w/Coupling Elements Swanson PhD Defense – April 2, 2003 Experimental Testbed

37. PTER – Optimal Controller Swanson PhD Defense – April 2, 2003 Experimental Testbed

38. 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

39. 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

40. 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

41. Template Design • Four templates representing different paths, areas of workspace Swanson PhD Defense – April 2, 2003 Human Subject Testing – Design of Experiments

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

48. 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

49. 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

50. Controllers – Path Error Swanson PhD Defense – April 2, 2003 Human Subject Testing – Data Analysis