Forward Kinematics Kernel for Improved Proxy Collision Checking
This work addresses the need for faster and more efficient collision checking in robotics, which is crucial for real-time robot control and motion planning, though it is incremental as it builds on an existing proxy collision checker.
The authors tackled the problem of improving computational performance in robot collision checking by introducing a new forward kinematics kernel, achieving a two-fold speedup, 8 times less memory usage, and boosting classification accuracy from 75% to over 95% for a 7-DOF robot arm.
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.