• Journal of KIISE:Computer Systems and Theory
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• v.28 no.10
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• pp.499-506
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• 2001

Terrain Following means that a mobile object, such a user's avatar, must follow terrain, remaining in contact with the ground at all times in virtual environments. This makes a virtual environment have the effects of gravity. Terrain Following is often done using collision detection: however this is inefficient, because general collision detection solves a problem that is inherently more complex than merely determining terrain contact points. Many virtual environments avoid the expense by utilizing a flat terrain with a constant altitude everywhere. This makes a terrain following trivial, but lacks realism. This paper provides as algorithm and a data structure for a terrain following using a neighboring area search as a way to search neighboring polygons. Because this algorithm uses a pre-processing step that stores the terrain polygons for calculating, it results in reducing overheads to workstations that is used to construct and maintain a virtual environment. Consequently, workstation can be used to apply not only a terrain following but also other things.

• Proceedings of the Korea Society for Simulation Conference
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• 2002.11a
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• pp.163-170
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• 2002

Terrain surfaces have to be modeled in very detail and wheel-surface contacting geometry must be well defined in order to obtain proper ground-reaction and friction forces for realistic simulation of off-road vehicles. Delaunay triangulation is one of the most widely used methods in modeling 3-dimensional terrain surfaces, and T-search is a relevant algorithm for searching resulting triangular polygons. The T-search method searches polygons in successive order and may not allow real-time computation of off-road vehicle dynamics if the terrain is modeled with many polygons, depending on the computer performance used in the simulation. In order to accelerate the searching speed of T-search, a terrain database of triangular polygons is modeled in multi-levels by adopting the LOD (Level of Detail) method used in realtime computer graphics. Simulation results show that the new LOD search is effective in shortening the required computing time. The LOD search can be even further accelerated by introducing an NN (Neural Network) algorithm, in the cases where a appropriate range of moving paths can be predicted by cultual information of the simulated terrain, such as lakes, houses, etc.. Numerical tests show that LOD-NN search almost double the speed of the original T-search.

• Journal of IKEEE
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• v.25 no.3
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• pp.437-444
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• 2021

In this paper, we propose an autonomous driving algorithm that allows a robot to explore various terrains, and implement an application that can monitor the robot's movement path during terrain search. The implemented application consists of a status unit that indicates the position, direction, speed, and motion of the mobile robot, a map unit that displays terrain information obtained through terrain search, and a control unit that controls the movement of the mobile robot. In order to control the movement of the robot, only the start and stop of the search/return is commanded by the application, and all driving for the search is performed autonomously. The basic algorithm for terrain search uses an infrared sensor to check for obstacles in the order of left, front, right, and rear, and if there is no obstacle and the path traveled is a dead end, it returns to the previous position and moves in the other direction to continue the search. Repeat the process to explore the terrain.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.5
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• pp.681-687
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• 2003

Terrain surfaces have to be modeled in very detail and wheel-surface contacting geometry must be well defined in order to obtain proper ground-reaction and friction forces fur realistic simulation of off-road vehicles. Delaunay triangulation is one of the most widely used methods in modeling 3-dimensional terrain surfaces, and the T-search is a relevant algorithm for searching resulting triangular polygons. The T-search method searches polygons in a successive order and may not allow real-time computation of off-road vehicle dynamics if the terrain is modeled with many polygons, depending on the computer performance used in the simulation. The dynamic T-search, which is proposed in this paper, combines conventional T-search and the concept of the dynmaic-window search which uses reduced searching windows or sets of triangular surface polygons at each frame by taking advantage of the information regarding dynamic charactereistics of a simulated vehicle. Numerical tests show improvement of searching speeds by about 5% for randomly distributed triangles. For continuous search following a vehicle path, which occurs in actual vehicle simulation, the searching speed becomes 4 times faster.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.11-21
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• 2015

In order to achieve success in ground operations, searching for moving targets is one of critical factors. Usually, the system of searching for adversary ground moving targets has complex properties which includes target's moving characteristics, camouflage level, terrain, weather, available search time window, distance between target and searcher, moving speed, target's tactics, etc. The purpose of this paper is to present a practical quantitative method for effectively searching for infiltrated moving targets considering aforementioned complex properties. Based upon search theories, this paper consists of two parts. One is infiltration route analysis, through terrain and mobility analysis. The other is building dynamic probability maps through Monte Carlo simulation to determine the prioritized searching area for moving targets. This study primarily considers ground moving targets' moving pattern. These move by foot and because terrain has a great effect on the target's movement, they generally travel along a constrained path. With the ideas based on the terrain's effect, this study deliberately performed terrain and mobility analysis and built a constrained path. In addition, dynamic probability maps taking terrain condition and a target's moving speed into consideration is proposed. This analysis is considerably distinct from other existing studies using supposed transition probability for searching moving targets. A case study is performed to validate the effectiveness and usefulness of our methodology. Also, this study suggests that the proposed approach can be used for searching for infiltrated ground moving target within critical time window. The proposed method could be used not only to assist a searcher's mission planning, but also to support the tactical commander's timely decision making ability and ensure the operations' success.

• 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.

• Korean Journal of Remote Sensing
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• v.18 no.5
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• pp.291-297
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• 2002

Optimal allocation of viewers on a terrain in such a wav that the hidden area would be minimized has many practical applications. However, it is impossible in practical sense to evaluate all the possible allocations. In this paper, we propose an optimal allocation of viewers based on genetic algorithm that enables probabilistic search of huge solution space. An experiment for one and three viewers was performed. The algorithm converges to good solutions. Especially, in one viewer case, the algorithm found the best solution.

• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1538-1551
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• 2021

The current drone flight plan creation creates a flight path point of two-dimensional coordinates on the map and sets an arbitrary altitude value considering the altitude of the terrain and the possible flight altitude. If the created flight path is a simple terrain such as a mountain or field, or if the user is familiar with the terrain, setting the flight altitude will not be difficult. However, for drone flight in a city where buildings are dense, a safer and more precise flight path generation method is needed. In this study, using high-precision spatial information, we construct a drone safety flight map with a 3D grid map structure and propose a flight path search algorithm based on it. The safety of the flight path is checked through the virtual drone flight simulation extracted by searching for the flight path based on the 3D grid map created by setting weights on the properties of obstacles and terrain such as buildings.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.1 s.6
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• pp.93-102
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• 1996

For an atomatic generation of DEM(Digital Elevation Model) by computer, it is a time-consumed work to determine adquate matches from stereo images. Correlation and evenly distributed area-based method is generally used for matching operation. In this paper, we propose a new approach that computes matches efficiantly by changing the size of mask window and search area according to the given terrain information. For image segmentation, at first edge-preserving smoothing filter is used for preprocessing, and then region growing algorithm is applied for the filterd images. The segmented regions are classifed into mountain, plain and water area by using MRF(Markov Random Filed) model. Maching is composed of predicting parallex and fine matching. Predicted parallex determines the location of search area in fine matching stage. The size of search area and mask window is determined by terrain information for each pixel. The execution time of matching is reduced by lessening the size of search area in the case of plain and water. For the experiments, four images which are covered $10km{\times}10km(1024{\times}1024\;pixel)$ of Taejeon-Kumsan in each are studied. The result of this study shows that the computing time of the proposed method using terrain information for matching operation can be reduced from 25% to 35%.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.867-873
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• 2011

A navigation mesh (NavMesh) is a suitable tool for the representation of a three-dimensional game world. A NavMesh consists of convex polygons covering free space, so the path can be found reliably without detecting collision with obstacles. The main disadvantage of a NavMesh is the huge state space. When the $A^*$ algorithm is applied to polygonal meshes for detailed terrain representation, the pathfinding can be inefficient due to the many states to be searched. In this paper, we propose a method to reduce the number of states searched by using visibility tests to achieve fast searching even on a detailed terrain with a large number of polygons. Our algorithm finds the visible vertices of the obstacles from the critical states and uses the heuristic function of $A^*$, defined as the distance to the goal through such visible vertices. The results show that the number of searched states can be substantially reduced compared to the $A^*$ search with a straight-line distance heuristic.