Dynamic Motion Modelling for Legged Robots

Details Dynamic Motion Modelling for Legged Robots Dynamic Motion Modelling for Legged Robots

2010-05-27

arxiv.org/abs/1005.5035v1

Computer Science

Robotics

Scientific paper

10.1109/IROS.2009.5354026

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.

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