Paper Info
Title
Dynamic motion modelling for legged robots
Abstract
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.
Year
DOI
Venue
2009
10.1109/IROS.2009.5354026
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Keywords
DocType
Volume
accurate motion model,direct mean,motion model,complex system,motion model representation,appreciable amount,dynamic motion,accompanying algorithm,legged robot,auxiliary sensory data,dynamic gaussian mixture model,8-legged kinematically complex robot,robot kinematics,gaussian mixture model,gaussian processes,probability density function,mobile robots,simultaneous localization and mapping,data models,mathematical model,motion control
Conference
abs/1005.5035
ISBN
Citations 
PageRank 
978-1-4244-3804-4
2
0.43
References 
Authors
12
3
Name
Order
Citations
PageRank
Mark Edgington1495.93
Yohannes Kassahun210911.23
Frank Kirchner314324.53