Part pose statistics: estimators and experiments
Goldberg, K.; Mirtich, B.V.; Yan Zhuang; Craig, J.; Carlisle, B.R.; Canny, J.
Robotics and Automation, IEEE Transactions on
Volume 15, Issue 5, Oct 1999 Page(s):849 - 857
Digital Object Identifier 10.1109/70.795790
Summary:Many of the most fundamental examples in probability involve the
pose statistics of coins and dice as they are dropped on a flat surface.
For these parts, the probability assigned to each stable face is
justified based on part symmetry, although most gamblers are familiar
with the possibility of loaded dice. In industrial part feeding, parts
also arrive in random orientations. We consider the following problem:
given part geometry and parameters such as center of mass, estimate the
probability of encountering each stable pose of the part. We describe
three estimators for solving this problem for polyhedral parts with
known center of mass. The first estimator uses a quasistatic motion
model that is computed in time O(n log n) for a part with n vertices.
The second estimator has the same time complexity but takes into account
a measure of dynamic stability based on perturbation. The third
estimator uses repeated Monte Carlo experiments with a mechanics
simulation package. To evaluate these estimators, we used a robot and
computer vision system to record the pose statistics based on 3595
physical drop experiments with four different parts. We compare this
data to the results from each estimator. We believe this is the first
paper to systematically compare alternative estimators and to correlate
their performance with statistically significant experiments on
industrial parts
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