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Terrain prediction for an eight-legged robot

Research output: Contribution to journal › Article

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Terrain prediction for an eight-legged robot. / Urwin-Wright, Stephen; Sanders, David; Chen, Sheng.

In: Journal of Robotic Systems, Vol. 19, No. 2, 02.2002, p. 91-98.

Research output: Contribution to journal › Article

Harvard

Urwin-Wright, S, Sanders, D & Chen, S 2002, 'Terrain prediction for an eight-legged robot', Journal of Robotic Systems, vol. 19, no. 2, pp. 91-98. https://doi.org/10.1002/rob.1072

APA

Urwin-Wright, S., Sanders, D., & Chen, S. (2002). Terrain prediction for an eight-legged robot. Journal of Robotic Systems, 19(2), 91-98. https://doi.org/10.1002/rob.1072

Vancouver

Urwin-Wright S, Sanders D, Chen S. Terrain prediction for an eight-legged robot. Journal of Robotic Systems. 2002 Feb;19(2):91-98. https://doi.org/10.1002/rob.1072

Author

Urwin-Wright, Stephen ; Sanders, David ; Chen, Sheng. / Terrain prediction for an eight-legged robot. In: Journal of Robotic Systems. 2002 ; Vol. 19, No. 2. pp. 91-98.

Bibtex

@article{8471e62dbe5942ab8c86f981b688145d,
title = "Terrain prediction for an eight-legged robot",
abstract = "Most legged robots must negotiate unknown environments with little or no terrain knowledge, as autonomous terrain mapping for robots is limited. A predictive terrain contour mapping strategy is proposed, which employs the use of a feed-forward neural network to predict the contours in environments, based on the positions of the neighboring legs. The predicted performance is better than previous implementations. ",
keywords = "Terrain prediction, mobile, robot, legged, ",
author = "Stephen Urwin-Wright and David Sanders and Sheng Chen",
year = "2002",
month = feb
doi = "10.1002/rob.1072",
language = "English",
volume = "19",
pages = "91--98",
journal = "Journal of Robotic Systems",
number = "2",

}

RIS

TY - JOUR

T1 - Terrain prediction for an eight-legged robot

AU - Urwin-Wright, Stephen

AU - Sanders, David

AU - Chen, Sheng

PY - 2002/2

Y1 - 2002/2

N2 - Most legged robots must negotiate unknown environments with little or no terrain knowledge, as autonomous terrain mapping for robots is limited. A predictive terrain contour mapping strategy is proposed, which employs the use of a feed-forward neural network to predict the contours in environments, based on the positions of the neighboring legs. The predicted performance is better than previous implementations.

AB - Most legged robots must negotiate unknown environments with little or no terrain knowledge, as autonomous terrain mapping for robots is limited. A predictive terrain contour mapping strategy is proposed, which employs the use of a feed-forward neural network to predict the contours in environments, based on the positions of the neighboring legs. The predicted performance is better than previous implementations.

KW - Terrain prediction

KW - mobile

KW - robot

KW - legged,

U2 - 10.1002/rob.1072

DO - 10.1002/rob.1072

M3 - Article

VL - 19

SP - 91

EP - 98

JO - Journal of Robotic Systems

JF - Journal of Robotic Systems

IS - 2

ER -

ID: 1765670