• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.4
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• pp.424-434
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• 2012

This paper presents a path planning algorithm of the unmanned aerial vehicle for the neutralization of the enemy firepower. The long range firepower of the ememy is usually located at the rear side of the mountain which is difficult to bomb. The path planner not only consider the differential constraints of the Unmanned Aerial Vehicle (UAV) but also consider the final approaching angle constraint. This problem is easily solved by incorporating the analytical upper bounded continuous curvature path smoothing algorithm into the Rapidly Exploring Random Tree (RRT) planner. The proposed algorithm can build a feasible path satisfying the kinematic constraints of the UAV on the fly. In addition, the curvatures of the path are continuous over the whole path. Simulation results show that the proposed algorithm can generate a feasible path of the UAV for the bombing mission regardless of the posture of the tunnel.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.365-370
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• 2012

Most path planning algorithms for a marine robot in the ocean environment have been developed without considering the robot's heading angle. As a result, the robot has a difficulty in following the path correctly. In this paper, we propose a limit-cycle circle set that applies to the $Theta^*$ algorithm. The minimum turning radius of a marine robot is calculated using a limit-cycle circle set, and circles of this radius is used to generate a configuration space of an occupancy grid map. After applying $Theta^*$ to this configuration space, the limit-cycle circle set is also applied to the start and end nodes to find the appropriate path with specified heading angles. The benefit of this algorithm is its fast computation time compared to other 3-D ($x,y,{\theta}$) path planning algorithms, along with the fact that it can be applied to the 3-D kinematic state of the robot. We simulate the proposed algorithm and compare it with 3-D $A^*$ and 3-D $A^*$ with post smoothing algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1175-1180
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• 2014

This paper proposes a new technique that produces improved local information using a low-cost GPS/INS system combined with Extended Kalman Filter and Path Planning when a Quad-rotor flies. In the research, a low-cost GPS is combined with INS by Extended Kalman Filter to improve local information. However, this system has disadvantages in that estimation accuracy is getting worsens when the Quad-rotor flies through the air in a curve and precision of location information is influenced by performance of the used GPS. An algorithm based on Path Planning is adopted to deal with these weaknesses. When the Quad-rotor flies outdoors, a short moving path can be predicted because all short moving paths of quad-rotor can be assumed to be straight. Path planning is used to make the short moving path and determine the closest local information of data of the GPS/INS system to location determined by path planning. Through the foregoing process, improved local data is obtained when the quad-rotor flies, and the performance of the proposed system is verified from various outdoor experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.471-478
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• 2011

This UGV (Unmanned Ground Vehicle) is not only widely used in various practical applications but is also currently being researched in many disciplines. In particular, obstacle avoidance is considered one of the most important technologies in the navigation of an unmanned vehicle. In this paper, we introduce a simple algorithm for path planning in order to reach a destination while avoiding a polygonal-shaped static obstacle. To effectively avoid such an obstacle, a path planned near the obstacle is much shorter than a path planned far from the obstacle, on the condition that both paths guarantee that the robot will not collide with the obstacle. So, to generate a path near the obstacle, we have developed an algorithm that combines an edge detection method and a limit-cycle navigation method. The edge detection method, based on Hough Transform and IR sensors, finds an obstacle's edge, and the limit-cycle navigation method generates a path that is smooth enough to reach a detected obstacle's edge. And we proposed novel algorithm to solve local minima using the virtual wall in the local vision. Finally, we verify performances of the proposed algorithm through simulations and experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.154-157
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• 2003

This paper presents an efficient real-time tool-path planning method for interpolation of NURBS surfaces in CNC machining. The proposed tool-path planning method is based on an improved iso-scallop strategy and can provide better precision than the existing methods. The proposed method is designed such that tool-path planning is easily managed in realtime. It proposed a new algorithm, for regulation of a scallop height, which can efficiently generate tool-paths and can save machining time compared with the existing method. Through computer simulations, the performance of the proposed method is analyzed and compared with the existing method in terms of feedrate. total machining time and a degree of constraint on the scallop height.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.256-256
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• 2000

In this parer, we present a real-time path planning algorithm which is integrated the artificial potential field(APF) and simulated annealing(SA) methods for mobile robot. The APF method in path planning has gained popularity since 1990's. It doesn't need the modeling of the complex configuration space of robot, and is easy to apply the path planning with simple computation. However, there is a major problem with APF method. It is the formation of local minima that can trap the robot before reaching its goal. So, to provide local minima recovery, we apply the SA method. The effectiveness of the proposed algorithm is verified through simulation.

• 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 Journal of Korea Robotics Society
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• v.5 no.2
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• pp.127-134
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• 2010

In this paper, a hybrid semi-3D path planning algorithm combining Virtual Tangential Vector(VTV) and fuzzy control is proposed. 3D dynamic environmental factors are reflected to the 2D path planning model, VTV. As a result, the robot can control direction from 2D path planning algorithm VTV and speed as well depending on the fuzzy inputs such as the distance between the robot and obstacle, roughness and slope. Performances and feasibilities of the suggested method are demonstrated by using Matlab simulations. Simulation results show that fuzzy rules and obstacle avoidance methods are working properly toward virtual 3D environments. The proposed hybrid semi-3D path planning is expected to be well applicable to a real life environment, considering its simplicity and realistic nature of the dynamic factors included.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.32-38
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• 1999

An autonomous mobile vehicle for transport system must plan optimal path in work envimnrent without human supervision and obstacle collision. This is to reach a destination without getting lost. In this paper, a genetic algorithm for globaI and local path planning and collision avoidance is proposed. The genetic algorithm searches for a path in the entire and continuous free space and unifies global path planning and local path planning. The sinmulation shows the proposed method is an efficient and effective method when compared with the traditional collision avoidance algorithms.rithms.