Computer Science Department
School of Computer Science, Carnegie Mellon University
Shape Reconstruction Using Active Tactile Sensors
Keywords:Tactile sensing, shape reconstruction, nonprehensile
manipulation, contact kinematics
We present a new method to reconstruct the shape of an unknown object
using tactile sensors, without requiring object immobilization.
Instead, sensing and nonprehensile manipulation occur simultaneously.
The robot infers the shape, motion and center of mass of the object
based on the motion of the contact points as measured by the tactile
sensors. This allows for a natural, continuous interaction between
manipulation and sensing. We analyze the planar case first by assuming
quasistatic dynamics, and present simulation results and experimental
results obtained using this analysis. We extend this analysis to the
full dynamics and prove observability of the nonlinear system describing
the shape and motion of the object being manipulated. In our simulations,
a simple observer based on Newton's method for root finding can recover
unknown shapes with almost negligible errors. Using the same framework
we can also describe the shape and dynamics of three-dimensional objects.
However, there are some fundamental differences between the planar and
three-dimensional case, due to increased tangent dimensionality. Also,
perfect global shape reconstruction is impossible in the 3D case, but
it is almost trivial to obtain upper and lower bounds on the shape.
The 3D shape reconstruction method has also been implemented and we
present some simulation results.