| Publication type: |
Article in Proceedings |
| Author: |
Thomas Münder, Thomas Röfer |
| Editor: |
Hidehisa Akiyama, Oliver Obst, Claude Sammut, Flavio Tonidandel |
| Title: |
Model-based Fall Detection and Fall Prevention for the NAO Robot |
| Book / Collection title: |
RoboCup 2017: Robot World Cup XXI |
| Volume: |
11175 |
| Page(s): |
312 – 324 |
| Series: |
Lecture Notes in Artificial Intelligence |
| Year published: |
2018 |
| Publisher: |
Springer |
| Abstract: |
Fall detection and fall prevention are crucial for humanoid robots when operating in natural environments. Early fall detection is important to have sufficient time for making a stabilizing movement. Existing approaches mostly analyze the sensor data to detect an ongoing fall. In this paper, we use a physical model of the robot to detect whether the measured sensor data indicates a fall in the near future. A trajectory for the foot is calculated to compensate the rotational velocity and acceleration of the fall. In an evaluation with the humanoid robot NAO, we demonstrate that falls can be detected significantly earlier than with traditional sensor classification with little false-positive detections during staggering. Falls due to small to medium impacts can be prevented. |
| PDF Version: |
http://www.informatik.uni-bremen.de/kogrob/papers/RC-Muender-Roefer-18.pdf |
| Status: |
Reviewed |
| Last updated: |
11. 11. 2022 |
