• Journal of the Korean Society for Precision Engineering
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• v.19 no.9
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• pp.142-150
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• 2002

This study describes a method of finding an optimal path of a soccer robot by using a concentric circle method with different radii of rotation. Comparing with conventional algorithms which try to find the shortest path length, the variable concentric circle method find the shortest moving time. The radius fur the shortest moving time for a given ball location depends on the relative location between a shooting robot and a ball. Practically it is difficult to find an analytical solution due to many unknowns. Assuming a radius of rotation within a possible range, total path moving time can be calculated by adding the times needed for straight path and circular path. Among these times the shortest time is obtained. In this paper, a graphical solution is presented such that the game ground is divided into 3 regions with a minimum, medium, and maximum radius of rotation.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2732-2734
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• 2000

This paper proposes a path planning algorithm for mobile robots. To generate a minimum-distance path for mobile robots, we use the Genetic Algorithm(GA) and Visibility Graph. After finding a minimum-distance path between a start and a goal point, the path is revised to find the smooth subminimum-distance path by a path-smoothing algorithm. Simulation results show that the proposed algorithms are effective.

• Journal of Distribution Science
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• v.14 no.2
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• pp.41-45
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• 2016

Purpose - Finding an optimal path is an essential component for the design and operation of smart transportation or logistics network. Many applications in navigation system assume that travel time of each link is fixed and same. However, in practice, the travel time of each link changes over time. In this paper, we introduce a new transportation problem to find a latest departing time and delivery path between the two nodes, while not violating the appointed time at the destination node. Research design, data, and methodology - To solve the problem, we suggest a mathematical model based on network optimization theory and a backward search method to find an optimal solution. Results - First, we introduce a dynamic transportation problem which is different with traditional shortest path or minimum cost path. Second, we propose an algorithm solution based on backward search to solve the problem in a large-sized network. Conclusions - We proposed a new transportation problem which is different with traditional shortest path or minimum cost path. We analyzed the problem under the conditions that travel time is changing, and proposed an algorithm to solve them. Extending our models for visiting two or more destinations is one of the further research topics.

• Journal of the Korean Operations Research and Management Science Society
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• v.8 no.1
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• pp.11-18
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• 1983

This paper shows a new efficient solution method of finding an optimal path for a cruise missile or aircraft to a target which has the maximal survivability and penetration effectiveness against sophisticated defenses and over varied terrain. We first generate a grid structure over the terrain, to construct a network. Since our network usually has about 10,000 nodes, the conventional Dijkstra algorithm takes too much computational time in its searching process for a new permanent node. Our method utilizes the Hashing technique to reduce the computational time of the searching process. Extensive computational results are presented.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.85-90
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• 2003

In this paper we develop a method for finding optimal ordering of K statistical models. This is based on a dependent paired comparison experimental arrangement whose results can naturally be represented by a completely oriented graph (also so called tournament graph). Introducing preference probabilities, strong transitivity conditions, and an optimal criterion to the graph, we show that a Hamiltonian path obtained from row sum ranking is the optimal ordering. Necessary theories involved in the method and computation are provided. As an application of the method, generalized variances of K multivariate normal populations are compared by a Bayesian approach.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.1-12
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• 2003

In this paper, we Introduce the Unreal Tournament (UT) game and the Gamebots system. The former it a well-known 3D first-person action game and the latter is an intelligent agent research testbed based on UT And then we explain the design and implementation of KGBot, which is an intelligent non-player character deploying effectively within the 3D virtual environment provided by UT and the Gamebots system. KGBot is a bot client within the Gamebots System. KGBot accomplishes its own task to find out and dominate several domination points pro-located on the complex surface map of 3D virtual environment KGBot adopts UM-PRS as its control engine, which is a general BDI agent architecture. KGBot contains a hierarchical knowledge base representing its complex behaviors in multiple layers. In this paper, we explain details of KGBot's Intelligent behaviors, tuck af locating the hidden domination points by exploring the unknown world effectively. constructing a path map by collecting the waypoints and paths distributed over the world, and finding an optimal path to certain destination based on this path graph. Finally we analyze the performance of KGBot exploring strategy and control engine through some experiments on different 3D maps.

• 전기의세계
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• v.30 no.5
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• pp.297-305
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• 1981

The problem of finding shortest paths between every pair of points in a network is solved employing and optimal network decomposition in which the network is decomposed into a number of subnetworks minimizing the number of cut-set between them while each subnetwork is constrained by a size limit. Shortest path computations are performed on individual subnetworks, and the solutions are recomposed to obtain the solution of the original network. The method when applied to large scale networks significantly reduces core requirement and computation time. This is demonstrated by developing a computer program based on the method and applying it to 30-vertex, 160-vertex, and 273-vertex networks.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1736-1737
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• 2007

Significant increase of container flows in marine terminals requires more efficient automatic port systems. This paper presents a novel routing and collision avoidance algorithm of linear motor based shuttle cars using random access sequence dynamic programming (RAS DP). The proposed RAS DP is accomplished online for determining optimal paths for each shuttle car.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.3
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• pp.63-72
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• 2015

This paper presents the issue that is predicting the movement of an agent in an enclosed space by using the MDP (Markov Decision Process). Recent researches on the optimal path finding are confined to derive the shortest path with the use of deterministic algorithm such as $A^*$ or Dijkstra. On the other hand, this study focuses in predicting the path that the agent chooses to escape the limited space as time passes, with the stochastic method. The MDP reward structure from GIS (Geographic Information System) data contributed this model to a feasible model. This model has been approved to have the high predictability after applied to the route of previous armed red guerilla.

• The Journal of Korea Robotics Society
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• v.13 no.2
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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 $A^*$.