High Speed RCS for Robot Task Sequencing Optimization

Abstract:
Task sequencing optimization requires running robot controller simulations (RCS) a large number of times. However, available RCS solutions are designed to be used in real time applications. In this work, we present a high speed RCS that is up to 5 orders of magnitude faster compared to the available solution, while retaining a high level of accuracy. We demonstrate the effectiveness of our solution in a comparative study on a KUKA robot. Furthermore, we provide first promising results for an RCS parameterized by machine learning algorithms. In conclusion, the presented RCS creates the foundation for the practical application of modern task sequencing optimization and could be easily adapted to a broad range of robot types from various manufacturers.