Effective Sampling and Distance Metrics for
3D Rigid Body Path Planning
- James J. Kuffner, Jr.
The Robotics Institute
Carnegie Mellon University
5000 Forbes Ave., Pittsburgh, PA 15213, USA
Digital Human Research Center
National Institute of Advanced
Science and Technology (AIST)
2-41-6 Aomi, Koto-ku, Tokyo, Japan 135-0064
Important implementation issues in rigid body path planning
are often overlooked. In particular, sampling-based motion planning
algorithms typically require a distance metric defined on the configuration
space, a sampling function, and a method for interpolating sampled points.
The configuration space of a 3D rigid body is identified with the Lie group
SE(3). Defining proper metrics, sampling, and interpolation techniques
for SE(3) is not obvious, and can become a hidden source of failure for
many planning algorithm implementations. This paper examines some
of these issues and presents techniques which have been found to be
effective experimentally for Rigid Body path planning.