Mobile Haptic Interface Progress From the Last Year and Future Plan

1. Mobile Haptic InterfaceProgress From the Last Year and Future Plan Presenter: In Lee HVR Lab Summer Workshop 2010.08.04

2. Mobile Haptic Interface

3. Issues on MHI - Precise and smooth MP tracking - Reduction of force induced by MP motion Haptic Interface (HI) Local pose of HI Force feedback + Force from MP Mobile Platform (MP) World pose of MP MP motion + Tracking error

4. MP Tracking Error • IS-900 has poor tracking ability for dynamic objects in contrast with its quite good performance for static ones. IS-900 Pose Info. (at 200 Hz) MHI Control PC Tracker Server PC X Y Z Tracking error along X and Z-axis(about ±20 mm in maximum) Position (mm) Trajectory of a tracker that israndomly moved along Y-axisby a linear lift. Time (ms)

5. Filter Selection • Low-pass filter: Noise freq. overlaps with that of signal. • Kalman filter: Difficult to choose the parameter values. • Moving Average: Filtering delay may affect the stability. • Moving average with variable window size: -> May reduce the noise effectively with small delay.

6. Adaptive Moving Average • Proposed by Perry Kaufman (1998). • Exponential Moving Average (EMA). • yn = αxn + (1-α)yn-1= yn-1 + α(xn-yn-1),where, x: original signal, y: filtered signal, α: smoothing factor. • α = 2 / (w+1),where, w is window size.

7. Adaptive Moving Average • AMA adjusts α within a certain range, αmax and αmin, using following equations: • ERn = |xn – xn-t| / Σ|xi – xi-1|,where, t: test window size. • αn = ERn(αmax - αmin) + αmin. • Typically, AMA uses α2 instead of α to obtain more robust result against the noise. n n-t+1

8. AMA Result • High frequency error is well removed. • Low frequency error is somewhat reduced but still exist. • Sensor fusion will be the key for further improvement. Y-Axis Original Filtered Position (mm) X-Axis Time (ms)

9. Closed-loop Control • In the previous research, we used the closed-loop force control to reduce the force from the MP dynamics. • In the closed-loop control, PID gain setting is crucial and search for the optimal values requires hard work. • If the starting point (i.e., near optimum) for the gains can be known, the labor may greatly reduced. • Ziegler and Nichols proposed an empirical method for the near-optimum gains values. • The method requires the maximum P-gain, ku, which drives the system stably oscillate, and the period of the oscillation, pu.

10. Relay Feedback Auto Tuning • Autonomous method to find kuand pu. • Induces the limit cycle oscillation using a relay feedback. • Relay feedback: • ku = r(e) = h, if e < 0, -h, otherwise. r(e) = h, if e < 0, -h, otherwise.

11. Problems of Current MP • Soft suspension: complex dynamics • Mecanum wheel: vibration, slip • Heavy weight: dull response • Complex dynamics can severely degrade the quality of force control.

12. New MP Design (under work) • High rigidity: no moving parts • Smooth motion: advanced wheel • Light weight: simple structure • Low center-of-mass: thin base

13. New MP Design (under work) • Omni-Ball, designed by K. Tadakuma and R. Tadakuma, is adopted for more smooth motion.

14. Other Changes • Side-by-side stereoscopic rendering • Additional distant cue using transparent sphere • (optional) Command vs. actual force graph for online evaluation. • stylus-type, 3-dof end effector with a button

15. Conclusion • Adaptive moving average • Auto PID gain tuning • Design of new MP & end effector • Visual enhancements Thank you!