• 산업공학
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• 제24권4호
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• pp.304-313
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• 2011

An Unmanned Aerial Vehicle(UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are currently employed in many military missions(surveillance, reconnaissance, communication relay, targeting, strike etc.) and a number of civilian applications(communication service, broadcast service, traffic control support, monitoring, measurement etc.). For accomplishing the UAV's missions, guarantee of survivability should be preceded. The main objective of this study is the path planning to maximize survivability for UAV based on 3-dimensional environment. A mathematical programming model is suggested by using MRPP(Most Reliable Path Problem) and solved by transforming MRPP into SPP(Shortest Path Problem). This study also suggests a $A^*PS$ algorithm based on 3-dimensional environment to UAV's path planning. According to comparison result of the suggested algorithm and SPP algorithms (Dijkstra, $A^*$ algorithm), the suggested algorithm gives better solution than SPP algorithms.

• 대한안전경영과학회지
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• 제13권2호
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• pp.103-113
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• 2011

This research is to select a path planning algorithm to maximize survivability for Unmanned Aerial Vehicle(UAV). An UAV is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are currently employed in many military missions(surveillance, reconnaissance, communication relay, targeting, strike etc.) and a number of civilian applications(communication service, broadcast service, traffic control support, monitoring, measurement etc.). In this research, a mathematical programming model is suggested by using MRPP(Most Reliable Path Problem) and verified by using ILOG CPLEX. A path planning algorithm for UAV is selected by comparing of SPP(Shortest Path Problem) algorithms which transfer MRPP into SPP.

• International Journal of Naval Architecture and Ocean Engineering
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• 제7권1호
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• pp.87-99
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• 2015

In recent years, the development of autonomous surface vehicles has been a field of increasing research interest. There are two major areas in this field: control theory and path planning. This study focuses on path planning, and two objectives are discussed: path planning for Unmanned Surface Vehicles (USVs) and implementation of path planning in a real map. In this paper, satellite thermal images are converted into binary images which are used as the maps for the Finite Angle $A^*$ algorithm ($FAA^*$), an advanced $A^*$ algorithm that is used to determine safer and suboptimal paths for USVs. To plan a collision-free path, the algorithm proposed in this article considers the dimensions of surface vehicles. Furthermore, the turning ability of a surface vehicle is also considered, and a constraint condition is introduced to improve the quality of the path planning algorithm, which makes the traveled path smoother. This study also shows a path planning experiment performed on a real satellite thermal image, and the path planning results can be used by an USV.

• 로봇학회논문지
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• 제13권2호
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• pp.142-149
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• 2018

$RRT^*$ (Rapidly exploring Random $Tree^*$) based algorithms are widely used for path planning. Informed $RRT^*$ uses $RRT^*$ for generating an initial path and optimizes the path by limiting sampling regions to the area around the initial path. $RRT^*$ algorithms have several limitations such as slow convergence speed, large memory requirements, and difficulties in finding paths when narrow aisles or doors exist. In this paper, we propose an algorithm to deal with these problems. The proposed algorithm applies the image skeletonization to the gridmap image for generating an initial path. Because this initial path is close to the optimal cost path even in the complex environments, the cost can converge to the optimum more quickly in the proposed algorithm than in the conventional Informed $RRT^*$. Also, we can reduce the number of nodes and memory requirement. The performance of the proposed algorithm is verified by comparison with the conventional Informed $RRT^*$ and Informed $RRT^*$ using initial path generated by $A^*$.

• 경영과학
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• 제25권2호
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• pp.81-88
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• 2008

This paper presents a new algorithm for the K shortest paths problem in a network. After a shortest path is produced with Dijkstra algorithm. detouring paths through inward arcs to every vertex of the shortest path are generated. A length of a detouring path is the sum of both the length of the inward arc and the difference between the shortest distance from the origin to the head vertex and that to the tail vertex. K-1 shorter paths are selected among the detouring paths and put into the set of K paths. Then detouring paths through inward arcs to every vertex of the second shortest path are generated. If there is a shorter path than the current Kth path in the set. this path is placed in the set and the Kth path is removed from the set, and the paths in the set is rearranged in the ascending order of lengths. This procedure of generating the detouring paths and rearranging the set is repeated until the $K^{th}-1$ path of the set is obtained. The computational results for networks with about 1,000,000 nodes and 2,700,000 arcs show that this algorithm can be applied to a problem of generating the detouring paths in the metropolitan traffic networks.

• 한국경영과학회지
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• 제18권2호
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• pp.23-43
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• 1993

This paper employs fuzzy variables instead of deterministic variables for job times in a project network. A fuzzy variable has its value restricted by a possibility distribution. This paper utilizes the triangular possibility distribution which has three estimated times. That is normal, resonable, and crash job times. This paper develops a fuzzy k-longest path algorithm, by utilizing the k-longest path algorithm. This algorithm will be useful to control the project the project network by considering the project completion possibility.

• 제어로봇시스템학회논문지
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• 제20권2호
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• pp.211-217
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• 2014

This paper addresses an algorithm of path planning for autonomous driving of a ground vehicle in waypoint navigation. The proposed algorithm is flexible in utilization under a large GPS positioning error and generates collision-free multiple paths while pursuing minimum traveling time. An optimal path reduces inefficient steering by minimizing lateral changes in generated waypoints along a path. Simulation results compare the proposed algorithm with the A* algorithm by manipulation of the steering wheel and traveling time, and show that the proposed algorithm realizes real-time obstacle avoidance by quick processing of path generation, and minimum time traveling by producing paths with small lateral changes while overcoming the very irregular positioning error from the GPS.

• 대한임베디드공학회논문지
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• 제12권5호
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• pp.295-301
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• 2017

Recently, autonomous navigation technology, obstacle recognition, and obstacle collision avoidance technology are actively being developed for an unmanned surface vehicle (USV). The path to move from the current location to the destination should be planned, in order for an USV to autonomously operate safely to its destination. The dynamic window approach (DWA) is a well-known navigation scheme as a local path planning. The DWA algorithm derives the linear velocity and angular velocity by evaluating the destination direction, velocity, and distance from the obstacle. However, because DWA algorithm does not consider tracking the path, when using only the DWA algorithm, the ship may navigate away from the path line after avoiding obstacles. In this paper, we propose an improved DWA algorithm that can follow path line. And we implemented the simulation and compared the existing DWA algorithm with the improved DWA algorithm proposed in this paper. As a result, it is confirmed that the proposed DWA algorithm follows the path line better.

• 한국경영과학회지
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• 제16권1호
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• pp.1-12
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• 1991

This paper studies an optimal algorithm for the Maximum Origin-Destination Flor Path (MODFP) in an acyclic transportation network. We define a Pseudo-Flow each are so that it can give an upper bound to the total flow of a given path. And using the K-th Shortest Path algorithm we obtain upper bound of MODF which is decreasing as the number of searched path grows. Computational Complexity of optimal algorithm is O(K + m) $n_{2}$), K being the total number of searched path. We proved that the problem complexity of finding MODFP in an acyclic network is NP-hard, showing that the-satisfiability problem can be polynomialy reduced to this problem. And we estimated the average of the number K as being (m/n)$^{1,08}$ Exp (0.00689gm) from the computational experiments.

• 전기학회논문지
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• 제63권1호
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• pp.92-98
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• 2014

This paper proposes a hermite spline based D* algorithm for effective path planning of mobile robot to improve the detecting speed. In conventional path planning research, a robot is supposed to pass through predetermined centers of grid partitions of area. However it doesn't guarantee the optimal path during its navigation. In addition, a robot is hard to avoid obstacles effectively. The proposed algorithm in this paper makes use of stochastic characteristics of nonholonomic mobile robot and estimation of shortest path to curvature movement of the robot. The performance evaluation of the improved spline D* algorithm performed through simulation shows its effectiveness. Moreover, the experiment verifies that a robot can find the shortest path by building the curve paths while it is moving on the path in spline.