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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


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.


Pose Info. (at 200 Hz)

MHI Control PC

Tracker Server PC




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.



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.




Position (mm)


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
  • Adaptive moving average
  • Auto PID gain tuning
  • Design of new MP & end effector
  • Visual enhancements

Thank you!