Abstract
Environment recognition is an effective way for a mobile robot to move across rough terrain. In particular, this makes it possible to prevent a tiny mobile robot from getting stuck or turning over. Several studies have been conducted on environment recognition using a laser range finder or camera. However, almost all of these studies focused on obstacle detection or shape recognition, which cannot be used to recognize the surface condition such as slipperiness. The purpose of this work is to design a model for estimating the surface condition using a tiny mobile robot. We set slipperiness as one of the parameters for recognizing the surface condition, which is already used by terramechanics, along with two additional parameters, the hardness and unevenness. We find that a robot can roughly estimate the ground hardness by measuring the current peak of a motor and the unevenness from measuring the robot posture. By recognizing the surface condition, the robot can change the parameters of the controlling motor based on the ground characteristics. This new method for recognizing the surface condition is significant, not only because it fills gaps in the previous research, but also because it does not require any special sensors such as a laser range finder and does not consume a large quantity of energy. Therefore, it achieves a core objective of our environmental monitoring system using multiple mobile robot.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Intelligent Robots and Systems |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 6515-6520 |
| Number of pages | 6 |
| Volume | 2015-December |
| ISBN (Print) | 9781479999941 |
| DOIs | |
| Publication status | Published - 2015 Dec 11 |
| Event | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany Duration: 2015 Sep 28 → 2015 Oct 2 |
Other
| Other | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 |
|---|---|
| Country | Germany |
| City | Hamburg |
| Period | 15/9/28 → 15/10/2 |
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Keywords
- Force
- Legged locomotion
- Robot kinematics
- Robot sensing systems
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Computer Vision and Pattern Recognition
- Computer Science Applications
Cite this
Novel method of estimating surface condition for tiny mobile robot to improve locomotion performance. / Tanaka, Katsuaki; Ishii, Hiroyuki; Okamoto, Yuya; Kuroiwa, Daisuke; Miura, Yusaku; Endo, Daiki; Mitsuzuka, Junko; Shi, Qing; Okabayashi, Satoshi; Sugahara, Yusuke; Takanishi, Atsuo.
IEEE International Conference on Intelligent Robots and Systems. Vol. 2015-December Institute of Electrical and Electronics Engineers Inc., 2015. p. 6515-6520 7354308.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Novel method of estimating surface condition for tiny mobile robot to improve locomotion performance
AU - Tanaka, Katsuaki
AU - Ishii, Hiroyuki
AU - Okamoto, Yuya
AU - Kuroiwa, Daisuke
AU - Miura, Yusaku
AU - Endo, Daiki
AU - Mitsuzuka, Junko
AU - Shi, Qing
AU - Okabayashi, Satoshi
AU - Sugahara, Yusuke
AU - Takanishi, Atsuo
PY - 2015/12/11
Y1 - 2015/12/11
N2 - Environment recognition is an effective way for a mobile robot to move across rough terrain. In particular, this makes it possible to prevent a tiny mobile robot from getting stuck or turning over. Several studies have been conducted on environment recognition using a laser range finder or camera. However, almost all of these studies focused on obstacle detection or shape recognition, which cannot be used to recognize the surface condition such as slipperiness. The purpose of this work is to design a model for estimating the surface condition using a tiny mobile robot. We set slipperiness as one of the parameters for recognizing the surface condition, which is already used by terramechanics, along with two additional parameters, the hardness and unevenness. We find that a robot can roughly estimate the ground hardness by measuring the current peak of a motor and the unevenness from measuring the robot posture. By recognizing the surface condition, the robot can change the parameters of the controlling motor based on the ground characteristics. This new method for recognizing the surface condition is significant, not only because it fills gaps in the previous research, but also because it does not require any special sensors such as a laser range finder and does not consume a large quantity of energy. Therefore, it achieves a core objective of our environmental monitoring system using multiple mobile robot.
AB - Environment recognition is an effective way for a mobile robot to move across rough terrain. In particular, this makes it possible to prevent a tiny mobile robot from getting stuck or turning over. Several studies have been conducted on environment recognition using a laser range finder or camera. However, almost all of these studies focused on obstacle detection or shape recognition, which cannot be used to recognize the surface condition such as slipperiness. The purpose of this work is to design a model for estimating the surface condition using a tiny mobile robot. We set slipperiness as one of the parameters for recognizing the surface condition, which is already used by terramechanics, along with two additional parameters, the hardness and unevenness. We find that a robot can roughly estimate the ground hardness by measuring the current peak of a motor and the unevenness from measuring the robot posture. By recognizing the surface condition, the robot can change the parameters of the controlling motor based on the ground characteristics. This new method for recognizing the surface condition is significant, not only because it fills gaps in the previous research, but also because it does not require any special sensors such as a laser range finder and does not consume a large quantity of energy. Therefore, it achieves a core objective of our environmental monitoring system using multiple mobile robot.
KW - Force
KW - Legged locomotion
KW - Robot kinematics
KW - Robot sensing systems
UR - http://www.scopus.com/inward/record.url?scp=84958174413&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958174413&partnerID=8YFLogxK
U2 - 10.1109/IROS.2015.7354308
DO - 10.1109/IROS.2015.7354308
M3 - Conference contribution
SN - 9781479999941
VL - 2015-December
SP - 6515
EP - 6520
BT - IEEE International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
ER -