  Real-time robot motion planning in complex high-dimensional environments
remains an open problem. Motion planning algorithms, and their underlying
collision checkers, are crucial to any robot control stack. Collision checking
takes up a large portion of the computational time in robot motion planning.
Existing collision checkers make trade-offs between speed and accuracy and
scale poorly to high-dimensional, complex environments. We present a novel
space decomposition method using K-Means clustering in the Forward Kinematics
space to accelerate proxy collision checking. We train individual configuration
space models using Fastron, a kernel perceptron algorithm, on these decomposed
subspaces, yielding compact yet highly accurate models that can be queried
rapidly and scale better to more complex environments. We demonstrate this new
method, called Decomposed Fast Perceptron (D-Fastron), on the 7-DOF Baxter
robot producing on average 29x faster collision checks and up to 9.8x faster
motion planning compared to state-of-the-art geometric collision checkers.
