• 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.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.196-202
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• 2024

Path-planning, a critical component of mobile robot navigation, comprises both local and global planning. Previous studies primarily focused on enhancing the individual performance of these planners, avoiding obstacles, and computing an optimal global path from a starting position to a target position. In this study, we introduce a practical path-planning framework that employs a target planner to bridge the local and global planners; this enables mobile robots to navigate seamlessly and efficiently toward a global target position. The proposed target planner assesses the visibility of waypoints along the global path, and it selects a reachable navigation target, which can then be used to generate efficient control commands for the local planners. A visibility-based target planner can handle situations, wherein the current, target waypoint is occupied by unknown obstacles. Real-world experiments demonstrated that the proposed pathplanning framework with the visibility-based target planner allowed the robot to navigate to the final target position along a more efficient path than the framework without a target planner.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.418-418
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• 2000

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

• Journal of the Korean Society of Industry Convergence
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• v.19 no.2
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• pp.75-80
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• 2016

This study proposes a efficient technology to control the optimal trajectory planning and real-time implementation method which can perform autonomous travelling for unmaned factory automation. Online path planning should plan and execute alternately in a short time, and hence it enables the robot avoid unknown dynamic obstacles which suddenly appear on robot's path. Based on Route planning and control algorithm, we suggested representation of edge cost, heuristic function, and priority queue management, to make a modified Route planning algorithm. Performance of the proposed algorithm is verified by simulation test.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2325-2330
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• 2005

A potential field method for solving the problem of path planning based on global and local information for a mobile robot moving among a set of stationary obstacles is described. The concept of various method used path planning is used design a planning strategy. A real human living area is constructed by many moving and imminence obstacles. Home service mobile robot must avoid many obstacles instantly. A path that safe and attraction towards the goal is chosen. The potential function depends on distance from the goal and heuristic function relies on surrounding environments. Three additional combined methods are proposed to apply to human living area, calibration robots position by measured surrounding environment and adapted home service robots. In this work, we proposed the application of various path planning theory to real area, human living. First, we consider potential field method. Potential field method is attractive method, but that method has great problem called local minimum. So we proposed intermediate point in real area. Intermediate point was set in doorframe and between walls there is connect other room or other area. Intermediate point is very efficiency in computing path. That point is able to smaller area, area divided by intermediate point line. The important idea is intermediate point is permanent point until destruction house or apartment house. Second step is move robot with sensing on front of mobile robot. With sensing, mobile robot recognize obstacle and judge moving obstacle. If mobile robot is reach the intermediate point, robot sensing the surround of point. Mobile robot has data about intermediate point, so mobile robot is able to calibration robots position and direction. Third, we gave uncertainty to robot and obstacles. Because, mobile robot was motion and sensing ability is not enough to control. Robot and obstacle have uncertainty. So, mobile robot planed safe path planning to collision free. Finally, escape local minimum, that has possibility occur robot do not work. Local minimum problem solved by virtual obstacle method. Next is some supposition in real living area.

• 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.

• 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.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.674-680
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• 2007

The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

• 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.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.1-8
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• 2021

The subject of navigation has drawn a large interest in the last few years. Navigation problem (or path planning) finds the path between two points, source location and destination location. In smart cities, solving navigation problem is essential to all residents and visitors of such cities to guide them to move easily between locations. Also, the navigation problem is very important in case of moving robots that move around the city or part of it to get some certain tasks done such as delivering packages, delivering food, etc. In either case, solution to the navigation is essential. The core to navigation systems is the navigation algorithms they employ. Navigation algorithms can be classified into navigation algorithms that depend on maps and navigation without the use of maps. The map contains all available routes and its directions. In this proposal, we consider the first class. In this paper, we are interested in getting path planning solutions very fast. In doing so, we employ a parallel platform, Reconfigurable mesh (R-Mesh), to compute the path from source location to destination location. R-Mesh is a parallel platform that has very fast solutions to many problems and can be deployed in moving vehicles and moving robots. This paper presents two algorithms for path planning. The first assumes maps with linear streets. The second considers maps with branching streets. In both algorithms, the quality of the path is evaluated in terms of the length of the path and the number of turns in the path.