• 대한전기학회논문지:시스템및제어부문D
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• 제52권4호
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• pp.223-232
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• 2003

This paper presents a new method driving multiple robots to their goal position without collision. Each robot adjusts its motion based on the information on the goal location, velocity, and position of the robot and the velocity and position of the .other robots. To consider the movement of the robots in a work area, we adopt the concept of avoidability measure. The avoidability measure figures the degree of how easily a robot can avoid other robots considering the following factors: the distance from the robot to the other robots, velocity of the robot and the other robots. To implement the concept in moving robot avoidance, relative distance between the robots is derived. Our method combines the relative distance with an artificial potential field method. The proposed method is simulated for several cases. The results show that the proposed method steers robots to open space anticipating the approach of other robots. In contrast, the usual potential field method sometimes fails preventing collision or causes hasty motion, because it initiates avoidance motion later than the proposed method. The proposed method can be used to move robots in a robot soccer team to their appropriate position without collision as fast as possible.

• 제어로봇시스템학회논문지
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• 제10권9호
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• pp.799-808
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• 2004

In this paper, a new collision avoidance algorithm based on the dynamic model of a mobile robot is proposed. In order to avoid obstacles on the path of a mobile robot, intelligent force control is used to regulate accurate distance between a robot and an obstacle. Since uncertainties from robot and environment dynamics degrade the performance of a collision avoidance task, neural network is used to compensate for uncertainties so that the collision avoidance can be performed intelligently. Simulation studies are conducted to confirm the proposed collision avoidance tracking control algorithm.

• 한국해양공학회지
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• 제33권6호
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• pp.510-517
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• 2019

Efficient path planning is essential for unmanned surface vehicle (USV) navigation. The A^* algorithm is an effective algorithm for identifying a safe path with optimal distance cost. In this study, a modified version of the A^* algorithm is applied for planning the path of a USV in a static and dynamic obstacle environment. The current study adopts the A^* approach while maintaining a safe distance between the USV and obstacles. Two important parameters-path length and computational time-are considered at various start times. The results demonstrate that the modified approach is effective for obstacle avoidance by a USV that is compliant with the International Regulations for Preventing Collision at Sea (COLREGs).

• 대한기계학회논문집
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• 제15권1호
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• pp.98-106
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• 1991

본 연구에서는 로봇이용의 자동화를 위하여 가장 필수적인 과정인 로봇의 운 동경로 계획에 관하여 연구하고 작업영역 내에 장애물이 있는 경우에 로봇과 장애물과 의 충돌 회피를 위한 정량적인 양으로 두물체간의 최소거리 및 충돌깊이를 효율적으로 계산할 수 있는 방법을 제안하며 이를 이용하여 로봇이 장애물과 충돌하지 않고 주어 진 작업을 수행할 수 있는 로봇의 경로를 자동으로 생성함으로써 로봇을 보다 상위레 벨에서 운용하기 위한 기초를 마련한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.255-257
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• 2005

This paper describes a method for obstacle avoidance and map building for mobile robots using one CCD camera. The captured image from one camera has the feature that some parts where focused look fine but the other parts look blear (this is the out-focusing effect). Using this feature a mobile robot can find obstacles in his way from the captured image. After Processing the image, a robot can not only determine whether an obstacle is in front of him or not, but also calculate the distance from obstacles based on image data and the focal distance of its camera lens. Finally, robots can avoid the obstacle and build the map using this calculated data.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.274-277
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• 2000

In this paper we propose new algorithms of path planning and obstacle avoidance for an autonomous mobile robot with vision system. Distance variation is included in path planning to approach the target point and avoid obstacles well. The fuzzy rules are also applied to both trajectory planning and obstacle avoidance to improve the autonomy of mobile robot. It is shown by computer simulation that the proposed algorithm is working well.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.266-268
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• 2005

Recently, the ability of sensing obstacles by oneself and creating suitable moving path in mobile robots are required to provide various kinds automation services. Therefore, in this paper, we studied the avoidance behavior of mobile robots from dynamic obstacles using potential function that minimizes distance and time. We examined the performance of the proposed algorithm by comparing the method of based on the geometrical experience in simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.434-439
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• 1993

This paper present a sensor based obstacle avoidance method which is based on a VFH(Vector Field Histogram) method. The basic idea of obstacle avoidance is to find a minimum obstacle direction and distance. From the minimum sonar index and the target direction high level system determine steering angle of mobile robot. The sonar sensor system consists of 12 ultra sonic sensor, and each sensor have its direction and safety value. This method has advantage on calculation speed and small memory. This method is implemented on indoor autonomous vehicle'ALiVE-2'.

• 한국전자통신학회논문지
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• 제8권3호
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• pp.433-438
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• 2013

저속 전기자동차에 사용되는 차량 간 충돌방지 센서의 간섭저감 방법에 대하여 연구하였다. 앞차에서 송신되는 기준신호를 수신하여 앞차와의 거리를 감지하는 차량 충돌방지용 거리센서는 뒤차를 향하여 발신되는 자차의 송신신호에 의해 간섭되는 것을 피하기 위하여 다양한 방법이 시도된 바 있다. 본 연구에서는 자차 송신기에서 발신되는 신호에 의한 영향을 위상소거법을 사용하여 감소시켜 -12dB 의 감쇄 비를 실현하였다. 본 연구에서 시도한 위상소거법은 종래의 시분할 송수신 방법에 비해 연속적인 감시가 가능한 장점이 있다.

• 센서학회지
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• 제29권5호
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• pp.348-353
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• 2020

This paper presents an adaptive method to avoid obstacles in various environmental settings, using a two-dimensional (2D) LiDAR sensor for mobile robots. While the conventional reaction based smooth nearness diagram (SND) algorithms use a fixed safety distance criterion, the proposed algorithm autonomously changes the safety criterion considering the obstacle density around a robot. The fixed safety criterion for the whole SND obstacle avoidance process can induce inefficient motion controls in terms of the travel distance and action smoothness. We applied a multinomial logistic regression algorithm, softmax regression, to classify 2D LiDAR point clouds into seven obstacle structure classes. The trained model was used to recognize a current obstacle density situation using newly obtained 2D LiDAR data. Through the classification, the robot adaptively modifies the safety distance criterion according to the change in its environment. We experimentally verified that the motion controls generated by the proposed adaptive algorithm were smoother and more efficient compared to those of the conventional SND algorithms.