Name
Abstract | ||
|---|---|---|
To plan complex motions of robots with many degrees of freedom, our novel, very flexible framework builds task-relevant roadmaps (TRMs), using a new sampling-based optimizer called Natural Gradient Inverse Kinematics (NGIK) based on natural evolution strategies (NES).To build TRMs, NGIK iteratively optimizes postures covering task-spaces expressed by arbitrary task-functions, subject to constraints expressed by arbitrary cost-functions, transparently dealing with both hard and soft constraints. TRMs are grown to maximally cover the task-space while minimizing costs. Unlike Jacobian-based methods, our algorithm does not rely on calculation of gradients, making application of the algorithm much simpler. We show how NGIK outperforms recent related sampling algorithms. A video demo (http://youtu.be/N6x2e1Zf_yg) successfully applies TRMs to an iCub humanoid robot with 41 DOF in its upper body, arms, hands, head, and eyes. To our knowledge, no similar methods exhibit such a degree of flexibility in defining movements. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IROS.2013.6697192 | 2013 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Keywords | Field | DocType |
humanoid robots,sampling methods,mobile robots,robot kinematics,robotics,artificial intelligence,path planning | Motion planning,Robot control,Computer vision,iCub,Inverse kinematics,Computer science,Robot kinematics,Artificial intelligence,Mobile robot,Robotics,Humanoid robot | Conference |
ISSN | Citations | PageRank |
2153-0858 | 7 | 0.47 |
References | Authors | |
16 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marijn Stollenga | 1 | 127 | 10.23 |
| Leo Pape | 2 | 38 | 3.61 |
| Mikhail Frank | 3 | 39 | 3.70 |
| Jürgen Leitner | 4 | 104 | 14.05 |
| Alexander Förster | 5 | 236 | 15.48 |
| Jürgen Schmidhuber | 6 | 17836 | 1238.63 |