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Kernel functions may be used in robotics for comparing different poses of arobot, such as in collision checking, inverse kinematics, and motion planning.These comparisons provide distance metrics often based on joint spacemeasurements and are performed hundreds or thousands of times a second,continuously for changing environments. Few examples exist in creating newkernels, despite their significant effect on computational performance androbustness in robot control and planning. We introduce a new kernel functionbased on forward kinematics (FK) to compare robot manipulator configurations.We integrate our new FK kernel into our proxy collision checker, Fastron, thatpreviously showed significant speed improvements to collision checking andmotion planning. With the new FK kernel, we realize a two-fold speedup in proxycollision check speed, 8 times less memory, and a boost in classificationaccuracy from 75% to over 95% for a 7 degrees-of-freedom robot arm compared tothe previously-used radial basis function kernel. Compared to state-of-the-artgeometric collision checkers, with the FK kernel, collision checks are now 9times faster. To show the broadness of the approach, we apply Fastron FK inOMPL across a wide variety of motion planners, showing unanimously faster robotplanning.

Forward Kinematics Kernel for Improved Proxy Collision Checking