Conference paper
A distributed strategy for gait adaptation in modular robots
In this paper we study online gait optimization for modular robots. The learning strategy we apply is distributed, independent on robot morphology, and easy to implement. First we demonstrate how the strategy allows an ATRON robot to adapt to faults and changes in its morphology and we study the strategy's scalability.
Second we extend the strategy to learn the parameters of gait-tables for ATRON and M-TRAN robots.We conclude that the presented strategy is effective for online learning of gaits for most types of modular robots and that learning can effectively be distributed by having independent processes learning in parallel.
| Language: | English |
|---|---|
| Year: | 2010 |
| Pages: | 2765-2770 |
| Proceedings: | 2010 IEEE International Conference on Robotics and Automation (ICRA) |
| ISBN: | 1424450381 , 1424450403 , 9781424450381 and 9781424450404 |
| ISSN: | 10504729 and 2577087x |
| Types: | Conference paper |
| DOI: | 10.1109/ROBOT.2010.5509942 |
ATRON robot M-TRAN robot Morphology Orbital robotics Robot kinematics Robot sensing systems Robotics and automation Robust control Robustness Scalability USA Councils Uninterruptible power systems gait adaptation learning (artificial intelligence) learning strategy modular robots motion control online gait optimization robot dynamics robot morphology
