• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.896-898
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• 2022

최근 인건비보다 저렴하게 사용할 수 있는 자율주행 로봇에 대한 수요가 증가하고 있다. 팬데믹의 영향으로 마스크 착용과 체온 측정이 의무화되어 키오스크, 체온 측정기와 같은 비대면 서비스의 수요 또한 증가하였다. 하지만 이러한 기능들은 각기 다른 기계에서 독립적으로 사용되며, 현재 보급된 자율주행 로봇을 병원에서 사용하기에는 적합하지 않다고 판단하였다. 본 연구에서 개발한 마스크 착용 여부 확인, 체온 확인, 자율주행을 활용한 안내 기능을 탑재한 인공지능 병원 안내 로봇을 통해 의료진의 업무 효율화 및 잠재적 비용 감소 효과를 기대한다. 본 연구에서는 마스크 착용 여부 확인을 위해 사용한 YOLOv5 알고리즘 훈련 결과를 통하여 높은 성능을 확인하였고 열화상 카메라를 사용한 체온 측정 알고리즘을 개발하였다. 또한, 실내 자율주행 실험을 통하여 Cartographer, Navigation 기능이 정상적으로 작동함을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.775-778
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• 2007

로봇의 응용과 활용분야는 현 산업의 주요 이슈가 되고 있다. 현재 싱글로봇의 효율적인 운영을 넘어 넓은 공간에서 중복적인 공간 탐색을 최소화하기 위한 멀티 로봇 운영 기법은 중요한 연구 주제 중에 하나로 부각되고 있다. 멀티 로봇을 효율적으로 운영하기 위해서는 멀티 로봇 시스템의 각 싱글 로봇의 움직임을 파악하여 효율적으로 업무를 할당 할 수 있는 관리체계가 필요하다. 멀티 로봇의 업무 할당과 중복 탐색 최소화를 위해 본 논문에서는 중앙 서버와 RFID 시스템을 이용한 청소 멀티 로봇 운영 기법을 제안한다. 제안한 시스템은 로봇의 localization, navigation 및 mapping을 효율적으로 수행하기 위해 RFID를 활용하고 최적의 청소 공간 할당을 위하여 중앙 서버가 멀티 로봇을 효율적으로 관리한다. 청소 멀티 로봇 시스템에서는 싱글 로봇과 비교하여 효율적인 로봇의 운영을 보장할 뿐만 아니라 각 싱글 로봇의 상태와 주변 상태를 고려한 fault-tolerance를 제공함으로써 로봇 운영의 신뢰성을 보장할 수 있다.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.537-543
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• 2017

Underwater glider is the long-term operating underwater robot that was developed with a purpose of continuous oceanographic observations and explorations. Torpedo-type underwater glider is not efficient from an aspect of maneuverability, because it uses a single buoyancy engine and motion controller for obtaining propulsive forces and moments. This paper introduces a ray-type underwater glider(RUG) with dual buoyancy engine, which improves the control performance of buoyancy and motion compared with torpedo-type underwater glider. Carrying out Computational Fluid Dynamics (CFD) analysis as static pitch drift test, the performance of fluid resistance for gliding motion was identified. Based on the calculated hydrodynamic coefficients, the dynamic simulation compared and analyzed the motion performance of torpedo-type and ray-type while controlling same volume of buoyancy engine. Small-sized model of RUG was developed to perform fundamental performance tests.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.115-123
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• 2021

Pine nuts are Korea's representative nut forest products and profitable crops. However, pine nuts are harvested by climbing the trees themselves, thus the risk is high. In order to solve this problem, it is necessary to harvest pine nuts using a robot or an unmanned aerial vehicle(UAV). In this paper, we propose a deep learning based detection method for harvesting pine nut in UAV aerial images. For this, a video was recorded in a real pine forest using UAV, and a data augmentation technique was used to supplement a small number of data. As the data for 3D detection, Unity3D was used to model the virtual pine nut and the virtual environment, and the labeling was acquired using the 3D transformation method of the coordinate system. Deep learning algorithms for detection of pine nuts distribution area and 2D and 3D detection of pine nuts objects were used DeepLabV3+, YOLOv4, and CenterNet, respectively. As a result of the experiment, the detection rate of pine nuts distribution area was 82.15%, the 2D detection rate was 86.93%, and the 3D detection rate was 59.45%.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.700-710
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• 2009

A computer vision technique of estimating the location of an unmanned ground vehicle is proposed. Identifying the location of the unmaned vehicle is very important task for automatic navigation of the vehicle. Conventional positioning sensors may fail to work properly in some real situations due to internal and external interferences. Given a DSM(Digital Surface Map), location of the vehicle can be estimated by the registration of the DSM and multiview range images obtained at the vehicle. Registration of the DSM and range images yields the 3D transformation from the coordinates of the range sensor to the reference coordinates of the DSM. To estimate the vehicle position, we first register a range image to the DSM coarsely and then refine the result. For coarse registration, we employ a fast random sample matching method. After the initial position is estimated and refined, all subsequent range images are registered by applying a pair-wise registration technique between range images. To reduce the accumulation error of pair-wise registration, we periodically refine the registration between range images and the DSM. Virtual environment is established to perform several experiments using a virtual vehicle. Range images are created based on the DSM by modeling a real 3D sensor. The vehicle moves along three different path while acquiring range images. Experimental results show that registration error is about under 1.3m in average.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.10-19
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• 1999

Cellular Automata Neural Systems(CANS) are neural networks based on biological development and evolution. Each neuron of CANS has local connection and acts as a form of pulse according to the dynamics of the chaotic neuron. CANS are generated from initial cells according to the CA rule. In the previous study, to obtain the useful ability of CANS, we make the pattern of initial cells evolve. However, it is impossible to represent all solution space, so we propose an evolving method of CA rule to overcome this defect in this paper. DNA coding has the redundancy and overlapping of gene and is apt for the representation of the rule. In this paper, we show the general expression of CA rule and propose translation method from DNA code to CA rule. The effectiveness of the proposed scheme was verified by applying it to the navigation problem of autonomous mobile robot.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.734-741
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• 2020

This article proposes a new pedestrian avoidance algorithm for social robots that coexist and communicate with humans and do not induce stress caused by invasion of psychological safety distance(Social Distance). To redefine the pedestrian model, pedestrians are clustered according to the pedestrian's gait characteristics(straightness, speed) and a social distance is defined for each pedestrian cluster. After modeling pedestrians(obstacles) with the social distances, integrated navigation algorithm is completed by applying the newly defined pedestrian model to commercial obstacle avoidance and path planning algorithms. To show the effectiveness of the proposed algorithm, two commercial obstacle avoidance & path planning algorithms(the Dynamic Window Approach (DWA) algorithm and the Timed Elastic Bands (TEB) algorithm) are used. Four cases were experimented in applying and non-applying the new pedestrian model, respectively. Simulation results show that the proposed algorithm can significantly reduce the stress index of pedestrians without loss of traveling time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1677-1684
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• 2010

Recently, due to the development of mobile phone and supply of smart phone, many contents have been developed. Especially, since the small-sized cameras are equiped in mobile devices, people are interested in the image based contents development, and it also becomes important part in their practical use. Among them, the character recognition system can be widely used in the applications such as blind people guidance systems, automatic robot navigation systems, automatic video retrieval and indexing systems, automatic text translation systems. Therefore, this paper proposes a system that is able to extract text area from the natural images captured by smart phone camera. The individual characters are recognized and result is output in voice. Text areas are extracted using Adaboost algorithm and individual characters are recognized using error back propagated neural network.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.375-381
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• 2005

Usually genetic algorithms are used to design optimal system. However the performance of the algorithm is determined by the fitness function and the system environment. It is expected that a co-evolutionary algorithm, two populations are constantly interact and co-evolve, is one of the solution to overcome these problems. In this paper we propose three types of co-evolutionary algorithm to solve GA-Hard problem. The first model is a competitive co-evolutionary algorithm that solution and environment are competitively co-evolve. This model can prevent the solution from falling in local optima because the environment are also evolve according to the evolution of the solution. The second algorithm is schema co-evolutionary algorithm that has host population and parasite (schema) population. Schema population supply good schema to host population in this algorithm. The third is game model-based co-evolutionary algorithm that two populations are co-evolve through game. Each algorithm is applied to visual servoing, robot navigation, and multi-objective optimization problem to verify the effectiveness of the proposed algorithms.

• Journal of the Korea Society of Computer and Information
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• v.13 no.4
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• pp.51-58
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• 2008

In this paper, we propose a place modeling based on the distribution of scale-invariant features, and a place recognition method that recognizes places by comparing the place model in a database with the extracted features from input data. The proposed method is based on the assumption that every place can be represented by unique feature distributions that are distinguishable from others. The proposed method uses global information of each place where one place is represented by one distribution model. Therefore, the main contribution of the proposed method is that the time cost corresponding to the increase of the number of places grows linearly without increasing exponentially. For the performance evaluation of the proposed method, the different number of frames and the different number of features are used, respectively. Empirical results illustrate that our approach achieves better performance in space and time cost comparing to other approaches. We expect that the Proposed method is applicable to many ubiquitous systems such as robot navigation, vision system for blind people, wearable computing, and so on.