Strategies for Improving and Evaluating Robot Registration Performance

The ability to calculate rigid-body transformations between arbitrary coordinate systems (i.e., registration) is an invaluable tool in robotics. This effort builds upon previous work by investigating strategies for improving the registration accuracy between a robotic arm and an extrinsic coordinate system with relatively inexpensive parts and minimal labor. The framework previously presented is expanded with a new test methodology to characterize the effects of strategies that improve registration performance. In addition, statistical analyses of physical trials reveal that leveraging more data and applying machine learning are two major components for significantly reducing registration error. One-shot peg-in-hole tests are conducted to show the application-level performance gains obtained by improving registration accuracy. Trends suggest that the maximum translation positioning error (postregistration) is a good, albeit not perfect, indicator for peg insertion performance.