• Journal of the Korean Society for Precision Engineering
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• v.26 no.4
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• pp.72-81
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• 2009

The purpose of this paper is to accomplish the stable humanoid robot walking on the soft terrains. The goal of the humanoid robot development is to make the robotic system perform some tasks in human living environment. However, human dwelling environments are very different from those of laboratories, where varied experiments are performed by the robot. In many cases, the ground is soft or elastic unlike the floor of a laboratory. When a robot walks on the soft ground, the sole of robot contacts the uneven ground. This results in unstable walking or walking may be impossible according to the degree of softness. Therefore, the algorithm that facilitates stable walking on the soft ground surface is required. In this paper, we suggest an algorithm that controls the ankle to help the robot walk stably on the soft ground using the humanoid robot (ISHURO-II) as a real model. A humanoid robot walking on the soft ground was simulated to verify that the proposed algorithm results in stable walking.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.10-17
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• 2012

In general, image-based 3D scenes can now be found in many popular vision systems, computer games and virtual reality tours. In this paper, we propose a method for creating 3D virtual scenes based on 2D image that is completely automatic and requires only a single scene as input data. The proposed method is similar to the creation of a pop-up illustration in a children's book. In particular, to estimate geometric structure information for 3D scene from a single outdoor image, we apply the tensor voting to an image segmentation. The tensor voting is used based on the fact that homogeneous region in an image is usually close together on a smooth region and therefore the tokens corresponding to centers of these regions have high saliency values. And then, our algorithm labels regions of the input image into coarse categories: "ground", "sky", and "vertical". These labels are then used to "cut and fold" the image into a pop-up model using a set of simple assumptions. The experimental results show that our method successfully segments coarse regions in many complex natural scene images and can create a 3D pop-up model to infer the structure information based on the segmented region information.