Paper Info
Title
Visual motion capturing for kinematic model estimation of a humanoid robot
Abstract
Controlling a tendon-driven robot like the humanoid Ecce is a difficult task, even more so when its kinematics and its pose are not known precisely. In this paper, we present a visual motion capture system to allow both real-time measurements of robot joint angles and model estimation of its kinematics. Unlike other humanoid robots, Ecce (see Fig. 1A) is completely molded by hand and its joints are not equipped with angle sensors. This anthropomimetic robot design [5] demands for both (i) real-time measurement of joint angles and (ii) model estimation of its kinematics. The underlying principle of this work is that all kinematic model parameters can be derived from visual motion data. Joint angle data finally lay the foundation for physics-based simulation and control of this novel musculoskeletal robot.
Year
DOI
Venue
2011
10.1007/978-3-642-23123-0_45
DAGM-Symposium
Keywords
Field
DocType
kinematic model estimation,kinematic model parameter,joint angle data,novel musculoskeletal robot,tendon-driven robot,joint angle,anthropomimetic robot design,model estimation,robot joint angle,humanoid robot,visual motion,real-time measurement
Motion capture,Computer vision,Kinematics,Inverse kinematics,Simulation,Computer science,Robot design,Robot kinematics,Visual motion,Artificial intelligence,Robot,Humanoid robot
Conference
Citations 
PageRank 
References 
1
0.40
11
Authors
1
Name
Order
Citations
PageRank
Andre Gaschler11359.32