Influencing Robot-Control Performance
Through Data Tuning
Department of Mechanical Engineering
University of British Columbia,
Vancouver, B.C. Canada
Typical industrial robot controllers are closed and therefore the development and technology transfer of control algorithms is inhibited without access to proprietory systems. Commercially available industrial controllers suitable for controlling systems at the frequencies necessary for robots are typically limited in the types of algorithms that can be implemented, and industrial control engineers prefer algorithms that that are easily defined and have well defined real time computation limits. Therefore, in practice, alternative strategies for improving robot performance are desirable. This talk focuses on improving the incoming data to the robot controller. That is, tuning the data to the controller-plant system rather than tuning the controller to the data.
In this talk two examples where improvement of the data signal to the controller are used to improve the control performance of the system. In the first case, an online, near-optimal, trajectory-planning algorithm which limits jerk is presented. Experimental results demonstrate that using the same industrial controller a much improved level of tracking can be achieved.
In the second example, an approach for improving the data stream through a heterogeneous sensor-fusion approach is discussed. The approach demonstrates how control can be maintained even in the presence of degraded or dazzled sensor data.
3:30 – 4:30 p.m.