• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.246-252
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• 2005

Intelligent Space is a space where many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents, which provide human with services. To realize this, human and mobile robots have to approach each other as much as possible. Moreover, it is necessary for them to perform interactions naturally. It is desirable for a mobile robot to carry out human affinitive movement. In this research, a mobile robot is controlled by the Intelligent Space through its resources. The mobile robot is controlled to follow walking human as stably and precisely as possible. In order to follow a human, control law is derived from the assumption that a human and a mobile robot are connected with a virtual spring model. Input velocity to a mobile robot is generated on the basis of the elastic force from the virtual spring in this model. And its performance is verified by the computer simulation and the experiment.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.14-19
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• 2019

In this paper, we propose a system for controlling the brightness of street lights by predicting pedestrian paths, identifying the position of pedestrians with motion sensing sensors and obtaining motion vectors based on past walking directions, then predicting pedestrian paths through the route prediction smart street lighting system. In addition, by using motion vector data, the pre-treatment process using linear interpolation method and the fuzzy system and neural network system were designed in parallel structure to increase efficiency and the rough set was used to correct errors. It is expected that the system proposed in this paper will be effective in securing the safety of pedestrians and reducing light pollution and energy by predicting the path of pedestrians in the detection of movement of pedestrians and in conjunction with smart street lightings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2148-2161
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• 2019

In this paper, we present a real-time cattle action recognition algorithm to detect the estrus phase of cattle from a live video stream. In order to classify cattle movement, specifically, to detect the mounting action, the most observable sign of the estrus phase, a simple yet effective feature description exploiting motion history images (MHI) is designed. By learning the proposed features using the support vector machine framework, various representative cattle actions, such as mounting, walking, tail wagging, and foot stamping, can be recognized robustly in complex scenes. Thanks to low complexity of the proposed action recognition algorithm, multiple cattle in three enclosures can be monitored simultaneously using a single fisheye camera. Through extensive experiments with real video streams, we confirmed that the proposed algorithm outperforms a conventional human action recognition algorithm by 18% in terms of recognition accuracy even with much smaller dimensional feature description.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.25-28
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• 2021

도보 위 위험 알림이란 사람이 지나다닐 수 있는 길을 파악하고 길 위에서 사용자에게 접근하는 위협적인 장애물들을 탐지하고 알려주는 것이다. 본 연구에서는 Computer Vision의 Semantic Segmentation을 이용하여 사람이 다닐 수 있는 길을 구분하고 YOLO 사물 인식 알고리즘을 이용하여 시각장애인에게 접근하는 위협적인 장애물들을 탐지하여 알려줄 수 있는 시스템을 제시한다. 해당 시스템은 실용성을 고려하여 모바일 환경에서 이용할 수 있도록 구현하였으며, 서버와의 연동을 통해 실시간으로 사용자에게 사물 인식의 결과를 알려준다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.468-470
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• 2022

본 논문은 강화학습 POMDP(Partially Observable Markov Decision Process) 알고리즘을 사용하여 자갈밭과 같은 비평탄 지형을 극복하는 4족 보행 지능로봇을 설계하고 딥러닝 기법을 사용하여 사람을 검출한다. 로봇의 임베디드 환경에서 1단계 검출 알고리즘인 YOLO-v7과 SSD의 기본 모델, 경량 또는 네트워크 교체 모델의 성능을 비교하고 선정된 SSD MobileNet-v2의 검출 속도를 개선하기 위해 TensorRT를 사용하여 최적화를 진행하였다

• 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%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.599-607
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• 2021

Street-view images, which are omnidirectional scenes centered on a specific location on the road, can provide various obstacle information for the pedestrians. Pedestrian network data for the navigation services should reflect the up-to-date obstacle information to ensure the mobility of pedestrians, including people with disabilities. In this study, the object detection model was trained for the bollard as a major obstacle in Seoul using street-view images and a deep learning algorithm. Also, a process for updating information about the presence and number of bollards as obstacle properties for the crosswalk node through spatial matching between the detected bollards and the pedestrian nodes was proposed. The missing crosswalk information can also be updated concurrently by the proposed process. The proposed approach is appropriate for crowdsourcing data as the model trained using the street-view images can be applied to photos taken with a smartphone while walking. Through additional training with various obstacles captured in the street-view images, it is expected to enable efficient information update about obstacles on the road.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.2
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• pp.169-176
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• 2013

To generate the walking path of a humanoid robot in an unknown environment, the shapes of obstacles around the robot should be detected accurately. However, doing so incurs a very large computational cost. Therefore this study proposes a method to classify the obstacle shape into three types: a shape small enough for the robot to go over, a shape planar enough for the robot foot to make contact with, and an uncertain shape that must be avoided by the robot. To classify the obstacle shape, first, the range and the number of the obstacles is detected. If an obstacle can make contact with the robot foot, the shape of an obstacle is accurately derived. If an obstacle has uncertain shape or small size, the shape of an obstacle is not detected to minimize the computational load. Experimental results show that the proposed algorithm efficiently classifies the shapes of obstacles around the robot in real time with low computational load.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.45-50
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• 2016

This paper describes the development of smart sticks as walking assistance for elderly people that incorporate motion sensing, hear-beat sensing, data processing, and communication functions. Our sticks communicate with users' smart phones and upon a detection of falling-off, an alarm is generated and propagated to multiple guardians registered in advance. In addition, the sticks provide smart healthcare functionalities for elderly people thus suggest an health platform that is empowered by various health-related informations. The heartbeat sensors and motion sensors mounted on the sticks enable various additional functions. Our smart sticks are designed to function as stable walking assistance as well as to support elderly people by providing useful services in the convergence with information technologies.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.4
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• pp.533-538
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• 2017

In this thesis, the ambulatory aid mechanism was implemented so that blind people could be safer at risk of walking outdoors. Using ultrasonic sensors, the obstacles can be detected when the distance between the obstacle is within 50 cm of the obstacle. If the light sensor becomes less than 25 lux, the LED will automatically turn on and help the safety of the visually impaired and the security of sight of the peripheral walkers. Color recognition sensors increase the rate of recognition of yellow color by the detection distance is 1cm, it vibrated when yellow light was detected. Using GPS with 7.3 m of error range, the guardian was able to check the location of the visually impaired.