Dynamic Motion Modelling for Legged Robots

Mark Edgington, Yohannes Kassahun, Frank Kirchner

In: Nikos Papanikolopoulo , Shigeki Sugano , Stefano Chiaverini , Max Meng (Hrsg.). In Proceedings of the IEEE International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-09) October 11-15 St. Louis Missouri United States Seiten 4688-4694 10/2009.


An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effort. In this paper we present a motion model representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the need to manually design the form of a motion model, and provides a direct means of incorporating auxiliary sensory data into the model. This representation and its accompanying algorithms are validated experimentally using an 8-legged kinematically complex robot, as well as a standard benchmark dataset. The presented method not only learns the robot' s motion model, but also improves the model' s accuracy by incorporating information about the terrain surrounding the robot.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence