• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1718-1726
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• 2006

In this paper, we proposed a fuzzy controller and algorithm for efficiently obstacle avoidance in unknown space. The ultrasonic sensor is used for position and distance recognition of obstacle, and fuzzy controller is used for left and right wheels angular velocity control. The fuzzification is used singleton method and the control rule is each wheel forty-nine. The fuzzy inference is used simplified Mamdani's reasoning and defuzzification is used SCOG(Simplified Center Of Gravity). The computer simulation based on mobile robot modelling was performed for the capacity of fuzzy controller and the really applicable possibility revaluation of the proposed avoidance algorithm and fuzzy controller. As a result, mobile robot was exactly reached in target and it avoided obstacle efficiently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.782-791
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• 2005

In this paper, path planning and tracking problems are mentioned to guarantee efficient and safe navigation of autonomous mobile robots. We focus on the path planning and also deal with the path tracking and obstacle avoidance. We improved the conventional distance transform (DT) algorithm for the path planning. Using the improved DT algorithm, we obtain paths with shorter distances compared to the conventional DT algorithm. In the stage of the Path tracking, we employ the fuzzy logic controller to conduct the path tracking behavior and obstacle avoidance behavior. Through computer simulation studies, we show the effectiveness of the Nosed navigational algorithm for autonomous mobile robots.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.295-299
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• 2005

In this paper, path planning and tracking problems are mentioned to guarantee efficient and safe navigation of autonomous mobile robots. We focus on the path planning and also deal with the path tracking and obstacle avoidance. We improved the conventional distance transform (DT) algorithm for the path planning. Using the improved DT algorithm, we obtain paths with shorter distances compared to the conventional DT algorithm. In the stage of the path tracking, we employ the fuzzy logic controller to conduct the path tracking behavior and obstacle avoidance behavior. Through computer simulation studies, we show the effectiveness of the proposed navigational algorithm for autonomous mobile robots.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.1-4
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• 2003

인간의 작업을 보조 혹은 대신하기 위해서 인간과 흡사한 이족보행로봇에 대한 연구가 많이 진행되고 있다. 이족보행로봇이 인간을 대신해서 어떤 작업을 하기 위해서는 작업공간에서의 자유로운 이동과 장애물 대처능력은 반드시 필요한 기능이다. 본 논문에서는 안정된 정적보행 및 장애물을 지능적으로 대처하는 이족보행로봇의 보행알고리즘을 제안하였다. 먼저 장애물 대처가능한 이족보행로봇의 기구 설계 및 제어기 구현에 대하여 설명하고, 인간의 보행 분석 결과를 바탕으로 안정된 정적보행 알고리즘을 제안하였다. 또한 좌우 회전 및 옆걸음을 통한 장애물 회피 알고리즘, 발에 부착된 적외선센서로 장애물을 인식하여 장애물을 넘어가는 보행 알고리즘을 제안하였다. 제안한 보행알고리즘은 이족보행로봇을 제작하여 다양한 작업환경에서 실험으로 성능을 검증하였다.

• 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.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1886-1887
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• 2011

본 논문은 군집 로봇 포메이션 이동 제어를 위한 방법을 제안한다. Potential field method 알고리즘을 이용하여 Leader-Bot의 장애물 회피와 이동 경로를 계획한다. Leader-bot을 기준으로 하는 Follewer-bot의 포메이션 형성을 위해 Formation generated function을 사용한다. Leader-bot과 Follower-bot들 간에 충돌회피와 Follower-bot들의 장애물 회피를 위해 Potential function을 적용한다. 제안한 방법은 시뮬레이션을 통하여 실제 운용 가능성을 검증한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.377-383
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• 2021

Recently, in accordance with the 4th industrial revolution, The use of autonomous mobile robots for flexible logistics transfer is increasing in factories, the warehouses and the service areas, etc. In large factories, many manual work is required to use Simultaneous Localization and Mapping(SLAM), so the need for the improved mobile robot autonomous driving is emerging. Accordingly, in this paper, an algorithm for mapless navigation that travels in an optimal path avoiding fixed or moving obstacles is proposed. For mapless navigation, the robot is trained to avoid fixed or moving obstacles through Deep Q Network (DQN) and accuracy 90% and 93% are obtained for two types of obstacle avoidance, respectively. In addition, DQN requires a lot of learning time to meet the required performance before use. To shorten this, the target size change algorithm is proposed and confirmed the reduced learning time and performance of obstacle avoidance through simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1153-1156
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• 1996

본 논문은 새로운 지역 경로 계획 알고리즘으로 DPH(Distance Profile Histogram)방법과 뉴럴네트워크를 사용한 주행 방법을 제안한다. DPH방법은 격자형 환경 모델을 기반으로 장애물의 존재 유무와 거리정보와 같은 장애물의 기하학적 배치정보를 사용하게 된다. 또한 긴 장애물이나 막힘상황(Dead end)과 같이 지역 경로 계획만으로는 회피하기 어려운 상황에서는 뉴럴네트워크에 의해 학습된 정보에 의해 주행하는 방법을 사용했다.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.366-368
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• 2009

본 논문에서는 모바일 로봇이 작업하는 공간상에서 빠르고 안전한 최적 경로계획을 수행할 수 있게 하는 가변적 리드 맵을 이용한 장애물 밀집 정보 기반 경로계획을 제안한다. 모바일 로봇이 작업 공간에 대해서 빠르고 안전한 경로계획을 해 클러스터링 기법을 이용하여 정적 및 동적 장애물의 분포에 대한 맵 정보를 재구성하여 정보화 시킨다. 최적의 경로계획을 위해서는 재구성된 장애물 밀집 클러스터 데이터를 이용하여 전통적 기법의 GA 방법을 변형한 최적 경로계획을 수행한다. 제안한 기술의 효율성을 검증하기 위해 그리드 기반 경로계획 중의 하나인 A*알고리즘과 다양한 맵을 이용하여 성능 비교를 수행하였다. 실험결과 제안한 경로계획 기술은 기존 알고리즘 보다 빠른 처리 성능과 동적 장애물이 밀집한 지역을 회피하는 최적 경로계획을 수행함을 확인하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.15-20
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• 2024

Path planning for unmanned aerial vehicles (UAV) is crucial in avoiding collisions with obstacles in complex environments that include both static and dynamic obstacles. Path planning algorithms like RRT and A^* are effectively handle static obstacle avoidance but have limitations with increasing computational complexity in high-dimensional environments. Reinforcement learning-based algorithms can accommodate complex environments, but like traditional path planning algorithms, they struggle with training complexity and convergence in higher-dimensional environment. In this paper, we proposed a reinforcement learning model utilizing a cell decomposition algorithm. The proposed model reduces the complexity of the environment by decomposing the learning environment in detail, and improves the obstacle avoidance performance by establishing the valid action of the agent. This solves the exploration problem of reinforcement learning and improves the convergence of learning. Simulation results show that the proposed model improves learning speed and efficient path planning compared to reinforcement learning models in general environments.