Sampling Based Motion PLanning for Minimizing Position Uncertainty with Stewart Platforms

Abstract

The work described in this dissertation provides a unique approach to error based motion planning. Originally designed specifically for use on a parallel robot,these methods can be extended to a more general case of any well-defined robotic platforms. Requirements for application of these methods are a known method of kinematics for defining the system as well as a means of calculating noise based on the system. Two methods of error tracking and two motion planning algorithms are tested here as approaches to this problem. Shown within are the results of the motion planning methods used. One combination of motion planning algorithm and error tracking works best as a general solution to this problem and is designed to work on a parallel robot; specifically, a Stewart platform. The motivation for use of a Stewart platform comes from research done at NASA Langley Research Center in the field of In-Space Assembly.