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Mobile Haptic Interface Progress From the Last Year and Future Plan. Presenter: In Lee HVR Lab Summer Workshop 2010.08.04. Mobile Haptic Interface. Issues on MHI. - Precise and smooth MP tracking. - Reduction of force induced by MP motion. Haptic Interface (HI). Local pose of HI.

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mobile haptic interface progress from the last year and future plan

Mobile Haptic InterfaceProgress From the Last Year and Future Plan

Presenter: In Lee

HVR Lab Summer Workshop

2010.08.04

issues on mhi
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

mp tracking error
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)

filter selection
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.
adaptive moving average
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.
adaptive moving average1
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

ama result
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)

closed loop control
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.
relay feedback auto tuning
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.

problems of current mp
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.
new mp design under work
New MP Design (under work)
  • High rigidity: no moving parts
  • Smooth motion: advanced wheel
  • Light weight: simple structure
  • Low center-of-mass: thin base
new mp design under work1
New MP Design (under work)
  • Omni-Ball, designed by K. Tadakuma and R. Tadakuma, is adopted for more smooth motion.
other changes
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
conclusion
Conclusion
  • Adaptive moving average
  • Auto PID gain tuning
  • Design of new MP & end effector
  • Visual enhancements

Thank you!