Dynamic Motion Modelling for Legged Robots
Mark Edgington, Yohannes Kassahun, Frank Kirchner
Editors: Nikos Papanikolopoulo, Shigeki Sugano, Stefano Chiaverini, Max Meng
In In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, (IROS-09), 11.10.-15.10.2009, St. Louis, Missouri, o.A., pages 4688-4694, Oct/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.