• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.39-46
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• 2012

An unmanned aerial vehicle (UAV) is a powered aerial vehicle that does not carry a human operator, uses aerodynamic forces to provide vehicle lift, can fly autonomously or be piloted remotely, can be expendable or recoverable, and can carry a lethal or non-lethal payload. An UAV is very important weapon system and is currently being employed in many military missions (surveillance, reconnaissance, communication relay, targeting, strike, etc.) in the war. To accomplish UAV's missions, guarantee of survivability should be preceded. The main objective of this study is a local path planning to maximize survivability for UAV on the battlefield of dynamic threats (obstacles, surface-to-air missiles, radar etc.). A local path planning is capable of producing a new path in response to environmental changes. This study suggests a $Smart$ $A^*$ (Smart A-star) algorithm for local path planning. The local path planned by $Smart$ $A^*$ algorithm is compared with the results of existing algorithms ($A^*$ $Replanner$, $D^*$) and evaluated performance of $Smart$ $A^*$ algorithm. The result of suggested algorithm gives the better solutions when compared with existing algorithms.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.241-249
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• 2014

This paper presents a path planning algorithm of an autonomous vehicle (ADAM III) for collision avoidance in the presence of multiple obstacles. Under the requirements that a low-cost GPS is used and its computation should be completed with a sampling time of sub-second, heading angle estimation is proposed to improve performance degradation of its measurement and a hierarchical structure for path planning is used. Once it is decided that obstacle avoidance is necessary, the path planning consists in three steps: waypoint generation, trajectory candidate generation, and trajectory selection. While the waypoints and the corresponding trajectory candidates are generated based on position of obstacles, the final desired trajectory is determined with considerations of kinematic constraints as well as an optimal condition in a term of lateral deviation. Finally the proposed algorithm was validated experimentally through field tests and its demonstration was performed in Autonomous Vehicle Competition (AVC) 2013.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.493-498
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• 2004

A novel approach for path planning of a polygonal robot is presented. Traditional path planners perform extensive geometric searching to find the optimal path or to prove that there is no solution. The computation required to prove that there is no solution is equivalent to exhaustive search of the motion space, which is typically very expensive. Humans seems to use a set of several different path planning strategies to analyse the situation of the obstacles in the environment, and quickly recognize whether the path-planning problem is easy to solve, hard to solve or has no solution. This human path-planning strategies have motivated the development of the presented algorithm that combines qualitative shape reasoning and exhaustive geometric searching to speed up the path planning process. It has three planning stages consisting of identification of no-solution cases based on an enclosure test, a qualitative reasoning stage, and finally a complete search algorithm in case the previous two stages cannot determine of the existence of a solution path.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.137-143
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• 2005

Autonomous mobile robot has an ability to navigate using both map in known environment and sensors for detecting obstacles in unknown environment. In general, autonomous mobile robot navigates by global path planning on the basis of already made map and local path planning on the basis of various kinds of sensors to avoid abrupt obstacles. This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.122-127
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• 1996

The achievement of the optimal condition for the task of an industrial articulated robot used in many fields is an important problem to improve productivity. In this paper, a minimum-time trajectory for an articulated robot along the specified path is studied and simulated with a proper example. A general dynamic model of manipulator is represented as a function of path distance. Using this model, the velocity is produced as fast as possible at each point along the path. This minimum-time trajectory planning module together with the existing collision-free path planning modules is utilized to design the optimal path planning of robot in cases where obstacles present.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.53-60
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• 2010

We propose a motion planning method for autonomous mobile robots. In order to minimize traveling time, a smooth path and a time optimal velocity profile should be generated under kinematic and dynamic constraints. In this paper, we develop an effective and practical method to generate a good solution with lower computation time. The initial path is obtained from voronoi diagram by Dijkstra's algorithm. Then the path is improved by changing the graph and path simultaneously. We apply the dynamic programming algorithm into the stage of improvement. Simulation results are presented to verify the performance of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.10
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• pp.1479-1485
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• 2015

Robot's technology was very simple and repetitive in the past. Nowadays, robots are required to perform intelligent operation. So, path planning has been studied extensively to create a path from start position to the goal position. In this paper, potential field algorithm was used for path planning in dynamic environments. It is used for a path plan of mobile robot because it is elegant mathematical analysis and simplicity. However, there are some problems. The problems are collision risk, avoidance path, time attrition. In order to resolve path problems, we amalgamated potential field algorithm with the artificial neural network system. The input of the neural network system is set using relative velocity and location between the robot and the obstacle. The output of the neural network system is used for the weighting factor of the repulsive potential function. The potential field algorithm problem of mobile robot's path planning can be improved by using artificial neural network system. The suggested algorithm was verified by simulations in various dynamic environments.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.539-547
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• 2011

In this paper, a method of generating path planning map is developed using digitalized geographic information such as FDB(Feature DataBase). FDB is widely used by the Army and needs to be applied to all weapon systems of newly developed. For the autonomous navigation of a robot, it is necessary to generate a path planning map by which a global path can be optimized. First, data included in FDB is analyzed in order to identify meaningful layers and attributes of which information can be used to generate the path planning map. Then for each of meaningful layers identified, a set of values of attributes in the layer is converted into the traverse cost using a matching table in which any combination of attribute values are matched into the corresponding traverse cost. For a certain region that is gridded, i.e., represented by a grid map, the traverse cost is extracted in a automatic manner for each gird of the region to generate the path planning map. Since multiple layers may be included in a single grid, an algorithm is developed to fusion several traverse costs. The proposed method is tested using a experimental program. Test results show that it can be a viable tool for generating the path planning map in real-time. The method can be used to generate other kinds of path planning maps using the digitalized geographic information as well.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.866-870
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• 1995

To make a mobile robot to get to a goal point, path which connects the mobile robot and the goal point is needed and a path planning is necessary. There are various kinds of a path planning. Well known methods are skeleton method, cell decomposition method and potential field method. But each method has both fortes and defects. In this paper, we propose a new method of path planning to find a path for mobile robot. It is obtained by modifying a Voronoi diagram. An original Voronoi diagram can make a safe path but its result is not satisfied. First defect of path, finded by the original Voronoi diagram, is sulplus of safty which make a path longer. Second defect is that the original Voronoi diagram method has a problem of connecting the Voronoi daigram with start/goal point of mobile robot. These defects are removed in proposed algorithm in this paper. We define a function to show the quality of paths. And by computer simulation, paths are compared and its result are shown.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.252-260
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• 2016

Unlike terrestrial transportation, marine transportation should consider environment factors in order to optimize path planning. This is because, ship's path planning is greatly influenced by real-time ocean environment-sea currents, wave and wind. Therefore, in this study, we suggest a ship path planning algorithm based on real-time ocean environment using not only $A^*$ algorithm but also path smoothing method. Moreover, in order to improve objective function value, we also consider ship's moving distance based on ship's location and real-time ocean environment data on grid map. The efficiency of the suggested algorithm is proved by comparing with $A^*$ algorithm only. This algorithm can be used as a reasonable automatics control system algorithm for unmaned ship.