• Journal of Advanced Navigation Technology
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• v.10 no.2
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• pp.145-151
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• 2006

In this paper, we propose a novel navigation method combined Nearness Diagram, Limit-Cycle method and the Vector Field Method for avoidance of unexpected obstacles in the dynamic environment. The Limit-Cycle method is used to obstacle avoidance in front of the robot and the Vector Field Method is used to obstacle avoidance in the side of robot. And the Nearness Diagram Navigation is used to obstacle avoidance in the nearness area of the robot. The performance of the proposed method is demonstrate by simulations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.6 s.312
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• pp.1-8
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• 2006

Many researches on path planning and obstacle avoidance for the fundamentals of mobile robot have been done recently. Informations from various sensors can be used to find obstacles and plan feasible path. In spite of many solutions of finding optimal path, each can be applied in only a constrained condition. This means that it is difficult to find university good algorithm. An optimal path with a complicated computation generates a time delay which cannot avoid moving obstacles. In this paper, we propose an algorithm of path planning and obstacle avoidance for mobile robot. We call the proposed method Random Access Sequence(RAS) method. In the proposed method, a small region is set first and numbers are assigned to its neighbors, then the path is selected using these numbers and cumulative numbers. It has an advantage of fast planning time and completeness of path if one exists. This means that new path selection may be possible within short time and that helps a robot to avoid obstacle in dynamic environments. Using the information of the start and destination position, the RAS can be performed for collision-free navigation by reforming feasible paths repeatedly in dynamic environments.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.72-79
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• 2019

Autonomous locomotion has two essential functionalities: mapping builds and updates maps by uncertain position information and measured sensor inputs, and localization is to find the positional information with the inaccurate map and the sensor information. In addition, obstacle detection, avoidance, and path designs are necessarily required for autonomous locomotion by combining the probabilistic methods based on uncertain locations. The sensory inputs, which are measured by a metric-based scanner, have difficulties of distinguishing moving obstacles like humans from static objects like walls in given environments. This paper proposes the low resolution grid map combined with reinforcement learning, which is compared with the conventional recognition method for detecting static and moving objects to generate obstacle avoiding path. Finally, the proposed method is verified with experimental results.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.56-63
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• 2024

As the safety of unmanned vehicles continues to improve, their usage in urban environments, which are full of obstacles such as buildings, is expected to increase. When numerous unmanned vehicles are operated in such environments, an algorithm that takes into account mutual collision avoidance, as well as static and dynamic obstacle avoidance, is necessary. In this paper, we propose an algorithm that handles task assignment and path planning. To efficiently plan paths, we construct a grid-based map and derive the paths from it. To enable quick re-planning in dynamic environments, we focus on reducing computational time. Through simulation, we explain obstacle avoidance and mutual collision avoidance in small-scale problems and confirm their performance by observing the entire mission completion time (Makespan) in large-scale problems.

• Journal of Korea Game Society
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• v.6 no.3
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• pp.3-12
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• 2006

Path-finding, one of the traditional Game A.I. problems, becomes an important issue to make games more realistic. Due to the limited resources in the computer system, path-finding systems sometimes produce a simplified and unrealistic path. The most relent researches have been focused on the path-finding avoiding only static obstacles. Various moving obstacles are however deployed in real games, a method avoiding those obstacles and producing a smooth path is necessary. In this paper, navigation mesh is used to represent 3D space and its topological characteristics are used for path-finding. Intellectual repulser and attractor are also used to avoid moving obstacles and to find an optimal path. We have evaluated the path produced by the method proposed in this paper and verified its usability in real game.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.363-365
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• 2009

대부분의 이동 로봇은 효율적인 경로계획을 위하여 최단거리 및 최소비용을 갖는 경로를 선택한다. 그러나 다수의 로봇이 존재하는 환경에서는 이웃하는 로봇 상호간에 동적 장애물로 인식되어 주행성능을 떨어뜨리게 된다. 또한 트래픽량이 거의 없는 환경에서는 무선 통신의 전송거리 제한으로 이동 로봇간 네트워킹이 원활하게 수행될 수 없는 문제도 있다. 따라서 적당한 거리의 이웃 로봇들과 협업을 위한 네트워킹을 하면서 동적인 경로계획 및 주행을 하는 것이 필수적이다. 본 논문에서는 기존의 A* 알고리즘을 수정하여 로봇의 동적인 트래픽을 고려한 경로계획 알고리즘을 제안한다. 제안된 기법을 이용하여 경로설정과정에서의 로봇 상호간 병목현상을 완화시키며, 일관된 협업 통신도 유지할 수 있다. 모의 실험을 통하여 제안된 알고리즘이 동적인 트래픽을 고려하여 경로를 선택함을 보인다.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.381-382
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• 2013

본 연구의 목적은 젊은 성인과 노인의 장애물 보행 시 신체질량중심의 변위와 최대속도를 3차원적으로 비교 분석하여 노인의 낙상위험 및 균형능력을 평가할 수 있는 중요한 요소가 될 수 있는지를 검증하는데 있다. 본 연구의 대상은 건강한 젊은 성인 10명(남성 6명/여성 4명, $24.6{\pm}1.9$세)과 65세 이상 건강한 노인 10명(남성 1명/여성 9명)으로 선정하였다. 각 피험자 신장의 10% 높이의 장애물 보행을 실시하였으며 동작 분석 장비를 통해 신체질량중심의 변위와 최대속도를 3차원적으로 분석하였다. 전후방향에서는 젊은 성인 집단의 변위가 더 크고(p=.019) 최대속도가 더 빠르게 나타났으며(p<.001), 좌우방향에서는 노인 집단의 변위가 더 크고(p=.004), 더 빠르게 나타났다(p<.001). 수직방향에서의 변위는 유의한 차이가 없었고(p=.135), 최대 속도는 젊은 성인 집단이 더 빠르게 나타났다(p<.001). 신체질량중심의 좌우방향에서의 크고 빠른 움직임은 노인의 동적 균형능력 저하로 인해 넘어가는 발(swing limb)의 안정적인 지지면 딛기를 위한 보상적 조절로 여겨지며, 따라서 노인의 낙상위험 및 균형능력을 평가할 수 있는 중요한 요소가 될 수 있을 것으로 사료된다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.186-186
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• 2022

과거에는 선박을 운용하기 위해서 많은 인원이 필요하였으나 최근 들어 선박 운용에 필요한 인원이 줄어들고 있으며, 더 나아가 자율적으로 운항하는 선박을 만들기 위한 연구가 활발히 수행되고 있다. 자율 운항 선박을 구성하는 여러 요소 중 인간의 시각을 대체하기 위한 자율 인지 시스템은 가장 선행되어야 하는 연구 분야 중 하나이다. RADAR (RAdio Detection And Ranging) 및 AIS (Automatic Identification System) 등의 전통적인 인지 센서를 활용한 연구가 진행 중이지만 사각지대나 탐지 주기 등의 한계가 있다. 따라서 본 연구에서는 다중 카메라 (광학, 열상, 파노라마)를 이용하여 전통적인 인지 센서의 한계를 보완하는 새로운 인지 시스템을 고안하였으며, 이를 기반으로 해상 장애물을 추적하여 동적 운동 정보를 얻었다. 먼저 실해역에서 수집한 이미지를 바탕으로 해상 장애물 탐지를 위한 데이터를 구성하고, 딥 러닝 기반의 탐지 모델을 학습시켰다. 탐지 모델을 이용하여 탐지한 결과는 직접 설계한 칼만 필터 기반의 적응형 추적 필터를 통과시켜 해상 장애물의운동 정보 (궤적, 속력, 방향)를 계산하는데 활용되었다. 또한 본 연구는 카메라를 센서로 활용했을 때의 한계를 보완하기 위하여 동 시간대에 다중 카메라에서 추적한 각각의 정보를 융합하였다. 그 결과 단일 카메라를 활용하는 경우, RADAR의 오차 범위 이내에 추적 결과가 수렴하는 양상을 보였으며, 다중 카메라를 활용하는 경우에는 단일 카메라보다 정확한 추적이 가능함을 확인하였다.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.2
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• pp.47-53
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• 2018

In this paper, we investigate a collision avoidance algorithm for unmanned aerial vehicles using potential field based on the relative velocity of obstacles. The potential field consists of the attraction force and the repulsive force that are generated for the target and the obstacles. And the field can be classified into the attractive potential field generated by the target and the repulsive potential field generated by the obstacle, respectively. In this study, we construct an attractive potential field as a function of the distance between the UAV and the target position. On the other hand, a repulsive potential field is created by a function of distance and the relative velocity of the obstacle with respect to the UAV. The proposed potential field based collision avoidance algorithm is evaluate through simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.635-641
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• 2008

In this paper, we used a laser range finder (LRF) to detect both the static and dynamic obstacles for the safe navigation of a mobile robot. LRF sensor measurements containing the information of obstacle's geometry are first processed to extract the characteristic points of the obstacle in the sensor field of view. Then the dynamic states of the characteristic points are approximated using kinematic model, which are tracked by associating the measurements with Probability Data Association Filter. Finally, the collision avoidance algorithm is developed by using fuzzy decision making algorithm depending on the states of the obstacles tracked by the proposed obstacle tracking algorithm. The performance of the proposed algorithm is evaluated through experiments with the experimental mobile robot.