TY - JOUR
T1 - Development of a guide-dog robot
T2 - Human-robot interface considering walking conditions for a visually handicapped person
AU - Saegusa, Shozo
AU - Yasuda, Yuya
AU - Uratani, Yoshitaka
AU - Tanaka, Eiichirou
AU - Makino, Toshiaki
AU - Chang, Jen Yuan
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011/6
Y1 - 2011/6
N2 - The repletion rate of guide dogs for visually handicapped persons is roughly 10% nationwide. The reasons for this low rate are the long training period and the expense for obtaining a guide dog in Japan. Motivated by these two reasons, we are developing guide-dog robots. The major objective is to develop an intelligent human-robot interface. This paper describes two novel interface algorithms and strategy to guide visually handicapped person. We developed new leading edge searching method, which uses a single laser range finder (LRF) developed to find the center of the corridor in an indoor environment. We also developed a new twin cluster trace method that can recognize the led-person's walking conditions measured by the LRF. The algorithm allows the guide-dog robot to accurately estimate and anticipate the led-person's next move. We experimentally verified these algorithms. The results show that the algorithms are reliable enough to enable the guide-dog robot and the led-person to maneuver in a complex corridor environment.
AB - The repletion rate of guide dogs for visually handicapped persons is roughly 10% nationwide. The reasons for this low rate are the long training period and the expense for obtaining a guide dog in Japan. Motivated by these two reasons, we are developing guide-dog robots. The major objective is to develop an intelligent human-robot interface. This paper describes two novel interface algorithms and strategy to guide visually handicapped person. We developed new leading edge searching method, which uses a single laser range finder (LRF) developed to find the center of the corridor in an indoor environment. We also developed a new twin cluster trace method that can recognize the led-person's walking conditions measured by the LRF. The algorithm allows the guide-dog robot to accurately estimate and anticipate the led-person's next move. We experimentally verified these algorithms. The results show that the algorithms are reliable enough to enable the guide-dog robot and the led-person to maneuver in a complex corridor environment.
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U2 - 10.1007/s00542-010-1219-1
DO - 10.1007/s00542-010-1219-1
M3 - Article
AN - SCOPUS:79958846275
VL - 17
SP - 1169
EP - 1174
JO - Microsystem Technologies
JF - Microsystem Technologies
SN - 0946-7076
IS - 5-7
ER -