• 한국항공운항학회:학술대회논문집
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• 2015.11a
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• pp.247-251
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• 2015

본 논문에서는 항공기의 3자유도 질점 모델과 Dubins 곡선을 이용한 3차원 경로생성 알고리즘 및 비선형 경로추종 유도기법을 통합하여 항공기의 경로계획을 위해 적용한 경로생성 알고리즘을 검증하고, 경로추종 성능과 항공교통관리를 위한 시뮬레이션에 3자유도 질점 항공기 모델 사용의 타당성을 확인하였다. 최적경로 생성을 위해 사용된 Dubins 곡선의 경우 계산속도가 빠르고 경로계획에 바로 적용이 가능하다는 장점이 있다. 하지만 주로 2차원 상의 경로문제를 다루기 때문에 이를 3차원 비행경로로 확장시킨 알고리즘을 사용하였다. 경로추종을 위한 유도제어법칙은 Specific Acceleration 명령을 통해 자세를 제어하는 비선형 경로추종 유도법칙을 활용하였으며, 이에 대한 결과를 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.701-704
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• 2010

In studying an autonomous navigation technique of UUV(Unmaned Underwater Vehicle), one of the many fundamental techniques is to plan a 3D path to complete the mission via realtime information received by sonar showing landscapes and obstacles. The simulation system is necessary to verify the algorithm in researching and developing 3D path planning of UUV. It is because 3D path planning of UUV should consider guide control, the dynamics, ocean environment, and search sonar models on the basis of obstacle avoidance technique. The simulation system developed in this paper visualizes the UUV's movement of avoiding obstacles, arriving at the goal position via waypoints by using C++ and OpenGL. Plus, it enables the user to setup the various underwater environment and obstacles by a user interface. It also provides a generalization that can verify path planning algorithm of UUV studied in any developing environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1305-1308
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• 2015

모바일 로봇의 경로 계획의 경우 주로 위치로 표현되는 2차원 공간 상에서 현재 위치에서 목표 위치까지 모바일 로봇이 도달하도록 경로를 계획한다. 그러나 nonholonomic 구조를 가지는 모바일 로봇의 경우 기구학적 제약에 의해 추종 불가능한 경로가 존재하게 된다. 또한 nonholonomic 모바일 로봇은 진행 방향을 포함한 3차원 공간 상에서의 경로 계획이 이루어져야 한다. 모바일 로봇의 경로 계획 알고리즘으로는 A* 경로 계획 알고리즘이 주로 사용되는데, A* 경로 계획 알고리즘은 경로 계획 시 현재 위치에서부터 노드를 확장시켜 가며 경로를 탐색한다. 이 때 각 노드로부터 목표 위치까지의 비용을 계산하기 위해 heuristic 함수가 사용된다. 기존의 A* 경로 계획 알고리즘의 경우 Euclidean 거리에 기반한 heuristic 함수가 사용되었으나, 이 경우 모바일 로봇의 진행 방향은 고려하지 않아, 로봇의 목표 위치에 도말만 할 뿐 목표 방향으로의 도달은 보장 할 수 없다. 본 논문에서는, A* 경로 계획 알고리즘을 통해 nonholonomic 모바일 로봇이 목표 위치에 목표 방향에 맞추어 도달할 수 있도록 경로 생성이 이루어지는 heuristic 함수를 제안하고, 시뮬레이션을 통해 그 성능을 검증한다.

• Journal of Aerospace System Engineering
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• v.14 no.5
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• pp.9-17
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• 2020

Mobile robots, including UAVs perform missions with limited fuel. Therefore, the energy-aware path planning is required to maximize efficiency when the robot is operated for a long time. In this study, we estimated the power consumption for each maneuver of a quadcopter UAV in the 3D environment and applied to the cost functions of D^⁎ Lite. The simulations were performed in a 3D environment that is similar to the industrial sites. The efficiency of path generation was high when the energy-aware path planning with simplified heuristic was applied. In addition, the energy-aware path was generated 19.3 times faster than the shortest path with a difference within 3.2%.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.401-407
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• 2016

This paper introduces 3-dimensional path planning and guidance including power model for high altitude long endurance (HALE) UAV using solar energy. Dubins curve used in this paper has advantage of being directly available to apply path planning. However, most of the path planning problems using Dubins curve are defined in a two-dimensional plan. So, we used 3-dimensional Dubins path generation algorithm which was studied by Randal W. Beard. The aircraft model which used in this paper does not have an aileron. So we designed lateral controller by using a rudder. And then, we were conducted path tracking simulations by using a nonlinear path tracking algorithm. We generate examples according to altitude conditions. From the path tracking simulation results, we confirm that the path tracking is well on the flight path. Finally, we were modeling the power system of HALE UAVs and conducting path tracking simulation during 48hours. Modeling the amount of power generated by the solar cell through the calculation of the solar energy yield. And, we show the 48hours path tracking simulation results.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.487-491
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• 2010

In this paper we propose a new single-query path planning algorithm for working well in high-dimensional configuration space. With the notice of the similarity between single-query algorithms with exploration algorithms, we propose a new path planning algorithm, which applies the Remembering Exploration method, which is one of exploration algorithms, to a path-planning problem by selecting a node from a roadmap, finding out the neighbor nodes from the node, and then inserting the neighbor nodes into the roadmap, recursively. For the performance comparison, we had experiments in 2D and 3D environments and compared the time to find out the path. In the results our algorithm shows the superior performance than other path planning algorithms.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.157-164
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• 2022

In this paper, we describe how to implement the mountainous terrain, radar, and air defense network for UAV path planning in a 3-D environment, and perform path planning and re-planning using the PRM algorithm, a sampling-based path planning algorithm. In the case of the original PRM algorithm, the calculation to check whether there is an obstacle between the nodes is performed 1:1 between nodes and is performed continuously, so the amount of calculation is greatly affected by the number of nodes or the linked distance between nodes. To improve this part, the proposed LineGridMask method simplifies the method of checking whether obstacles exist, and reduces the calculation time of the path planning through parallelization. Finally, comparing performance with existing PRM algorithms confirmed that computational time was reduced by up to 88% in path planning and up to 94% in re-planning.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.579-580
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• 2016

본 연구에서는 군집 로봇 체계에서 next-best-view(NBV)의 결정과 경로 계획(path planning)을 동시에 수행하는 효율적인 3차원 탐사 알고리즘을 제안한다. NBV 결정 및 경로 계획을 동시에 수행하는 단일 로봇 탐사 알고리즘에는 최근에 제안된 RH-NBVP[1]가 있다. 우리는 각 로봇에게 효율적으로 탐사 영역을 분배하는 Voronoi-biased multi-RRTs(VB-MRRTs)를 제시하며, 이를 통해 군집 로봇 체계에 RH-NBVP를 적용한다. 제시한 VB-MRRTs가 공간을 얼마나 점유하는지 분석하였으며, 3차원 시뮬레이션 상의 군집 로봇 체계에서 VB-MRRTs가 적용된 RH-NBVP의 탐사율 향상을 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.612-614
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• 1998

뇌정위 수술(Stereotactic Surgery)은 컴퓨터 단층영상과 자기공명 영상 같은 3차원 영상을 이용하여 뇌병변의 위치를 입체적으로 정확히 파악하여 정상 뇌에 대한 손상을 최소화하며 병변을 수술하는 기법이다. 본 논문에서는 수술 받을 환자의 컴퓨터 단층영상과 자기공명 영상 등 다양한 종류의 3차원 볼륨 데이터를 전처리한 다음 동일한 3차원 공간 내에서 정렬시켜 선택적 또는 동시적으로 3차원 영상을 가시화 하는 기법을 제안한다. 또한 3차원 영상에서 뇌정위 수술의 삽입점과 목표점을 지정할 수 있는 기능을 지원하며 수술 경로에 따른 가상 수술의 시뮬레이션을 통하여 수술 경로의 안전성을 검증할 수 있게 하였다.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1483-1495
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• 2006

The navigation mesh represents a terrain as a set of triangles on which characters may move around. The navigation mesh cab be generated automatically, and it is more flexible in representing 3D surface. The number of triangles to represent a terrain may vary according to the structure of the terrain. As characters are moving around on a navigation mesh, the path planning can be performed more easily by projecting the 3D surfaces into 2D space. However, when the terrain is represented with an elaborated mesh of large number of triangles to achieve more realistic movements, the path finding can be very inefficient because there are too many states(triangles) to be searched. In this paper, we propose an efficient method of path finding in 3D games where the terrain is represented by navigation meshes. Our method uses the visibility tests. When the graph-based search is applied to elaborated polygonal meshes for detailed terrain representation, the path finding can be very inefficient because there are too many states(polygons) to be searched. In our method, we reduce the search space by using visibility tests so that the search can be fast even on the detailed terrain with large number of polygons. First we find the visible vertices of the obstacles, and define the heuristic function as the distance to the goal through those vertices. By doing that, the number of states that the graph-based search visits can be substantially reduced compared to the plane search with straight-line distance heuristic.