Forward Kinematics Kernel for Improved Proxy Collision Checking

TL;DR

This paper introduces a novel forward kinematics kernel for robot pose comparison, significantly enhancing collision checking speed, accuracy, and memory efficiency in motion planning tasks.

Contribution

A new forward kinematics-based kernel function is proposed, integrated into the Fastron collision checker, improving performance over traditional kernels and geometric methods.

Findings

Two-fold increase in collision check speed

Eight times less memory usage

Collision checks are nine times faster than geometric methods

Abstract

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