• Journal of Mechanical Science and Technology
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• v.14 no.2
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• pp.131-140
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• 2000

For a walking robot with high constant body speed, the dynamic effects of the legs on the transfer phase are dominant compared with other factors. This paper presents a new force distribution algorithm to maximize walkable terrain without slipping considering the dynamic effects of the legs on the transfer phase. Maximizing the walkable terrain means having the capability of walking on more slippery ground under the same constraint, namely constant body speed. A simple force distribution algorithm applied to the proposed walking model with a pantograph leg shows an improvement in the capability of preventing foot-slippage compared with one using a pseudo-inverse method.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.5
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• pp.331-341
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• 2017

This paper presents the sea-trial results of Crabster CR6000 which is a deep-sea walking robot developed by KRISO in 2016. Crabster CR6000 is designed to inspect deep-sea environment rejecting the disturbance on the silent and calm abyssal area. The sea-trial was conducted at the East Sea and the Philippine Sea on December 2016. The Crabster CR6000 undocked successfully from the Shuttle after touchdown on the sea-bed and walked out on the soft sediment soil of the 4,743m seafloor at the fourth diving in the Philippine Sea. The advanced technologies and capabilities of CR6000 were verified from the operational and functional test conducted in the sea-trial. The experimental data acquired from the sea-trial were summarized and the first experience of the deep-sea walking robot was presented in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1925-1936
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• 2010

In this paper, multi-legged robot was designed and produced using stable walking pattern algorithm. The robot had embedded camera and wireless communication function and it is possible to recognize both hand posture and obstacles. The algorithm decided moving paths, and recognized and avoided obstacles through Hough Transform using Edge Detection of inputed image from image sensor. The robot can be controlled by hand posture using Mahalanobis Distance and average value of skin's color pixel, which is previously learned in order to decide the destination. The developed system has shown obstacle detection rate of 96% and hand posture recognition rate of 94%.