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.

In a dynamic robotic workcell environment where robots may be frequently relocated or may need to collaborate with other robots, it is simpler and more robust to program robots in an external or unifying reference frame. The process of robot registration involves finding the location of a robot with respect to another reference frame. For instance, if parts are in known locations on a table and a robot can locate itself with respect to the table (an external reference frame), then the robot will also know the location of the parts. Furthermore, if two or more robots can locate themselves with respect to the table, then each robot will not only know the location of the parts, but also the location of every other robot. This knowledge facilitates the coordination of robot motions and robot collaboration and eases the integration of additional robots into the workcell. Since robot registration is critically necessary and occurs frequently, its process needs to be inexpensive, fast, and accurate. This paper details the requirements for a relatively inexpensive and fast robot registration experience, along with detailing strategies that incur significant improvements to registered robot positioning accuracy with minimal overhead. A quantitative verification process is presented to evaluate the performance impacts of these strategies. Peg-in-hole experiments are conducted to validate the notion that more accurate robot registration translates to more reliable task-level performance.