• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.211-217
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• 2019

In an complicated battlefield environment, information from enemy's camp is an important factor in carrying out military operations. For obtaining this information, the number of UAVs that can be deployed to the mission without our forces' loss and at low cost is increasing. Because the mission environment has anti-aircraft weapons, mission space is needed for UAV to guarantee survivability without being killed. The concept of Configuration Space is used to define the mission space considering with range of weapons and detect range of UAV. UAV must visit whole given area to obtain the information and perform Coverage Path Planning for this. Based on threats to UAV and importance of information that will be obtained, area that UAV should visit first is defined. Grid Map is generated and mapping threat information to each grid for UAV path planning. On this study, coverage conditions and path planning procedures are presented based on the threat information on Grid Map, and mission space is expanded to improve detection efficiency. Finally, simulations are performed, and results are presented using the suggested UAV path planning method in this study.

• IE interfaces
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• v.24 no.4
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• pp.304-313
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• 2011

An Unmanned Aerial Vehicle(UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are currently employed in many military missions(surveillance, reconnaissance, communication relay, targeting, strike etc.) and a number of civilian applications(communication service, broadcast service, traffic control support, monitoring, measurement etc.). For accomplishing the UAV's missions, guarantee of survivability should be preceded. The main objective of this study is the path planning to maximize survivability for UAV based on 3-dimensional environment. A mathematical programming model is suggested by using MRPP(Most Reliable Path Problem) and solved by transforming MRPP into SPP(Shortest Path Problem). This study also suggests a $A^*PS$ algorithm based on 3-dimensional environment to UAV's path planning. According to comparison result of the suggested algorithm and SPP algorithms (Dijkstra, $A^*$ algorithm), the suggested algorithm gives better solution than SPP algorithms.

• Journal of the Korea Safety Management & Science
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• v.13 no.2
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• pp.103-113
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• 2011

This research is to select a path planning algorithm to maximize survivability for Unmanned Aerial Vehicle(UAV). An UAV is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are currently employed in many military missions(surveillance, reconnaissance, communication relay, targeting, strike etc.) and a number of civilian applications(communication service, broadcast service, traffic control support, monitoring, measurement etc.). In this research, a mathematical programming model is suggested by using MRPP(Most Reliable Path Problem) and verified by using ILOG CPLEX. A path planning algorithm for UAV is selected by comparing of SPP(Shortest Path Problem) algorithms which transfer MRPP into SPP.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.8-19
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• 2023

This paper presents advancement in multi- unmanned aerial vehicle (UAV) cooperative area surveillance, focusing on optimizing UAV route planning through the application of genetic algorithms. Addressing the complexities of comprehensive coverage, two real-time dynamic path planning methods are introduced, leveraging genetic algorithms to enhance surveillance efficiency while accounting for flight constraints. These methodologies adapt multi-UAV routes by encoding turning angles and employing coverage-driven fitness functions, facilitating real-time monitoring optimization. The paper introduces a novel path planning model for scenarios where UAVs navigate collaboratively without predetermined destinations during regional surveillance. Empirical evaluations confirm the effectiveness of the proposed methods, showcasing improved coverage and heightened efficiency in multi-UAV path planning. Furthermore, we introduce innovative optimization strategies, (Foresightedness and Multi-step) offering distinct trade-offs between solution quality and computational time. This research contributes innovative solutions to the intricate challenges of cooperative area surveillance, showcasing the transformative potential of genetic algorithms in multi-UAV technology. By enabling smarter route planning, these methods underscore the feasibility of more efficient, adaptable, and intelligent cooperative surveillance missions.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.3
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• pp.24-34
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• 2011

An Unmanned Aerial Vehicle (UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are an attractive alternative for many scientific and military organizations. UAVs can perform operations that are considered to be risky or uninhabitable for human. UA V s are currently employed in many military missions such as reconnaissance, surveillance, enemy radar jamming, decoying, suppression of enemy air defense (SEAD), fixed and moving target attack, and air-to-air combat. UAVs also are employed in a number of civilian applications such as monitoring ozone depletion, inclement weather, traffic congestion, and taking images of dangerous territory. For accomplishing the UAV's missions, guarantee of survivability should be preceded. The main objective of this study is to suggest a mathematical programming model and a $A^*PS$-PGA (A-star with Post Smoothing-Parallel Genetic Algorithm) for an UAV's path planning to maximize survivability. A mathematical programming model is composed by using MRPP (Most Reliable Path Problem) and TSP (Traveling Salesman Problem). A path planning algorithm for UAV is applied by transforming MRPP into SPP (Shortest Path Problem).

• Journal of the Society of Naval Architects of Korea
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• v.61 no.5
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• pp.324-333
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• 2024

This paper focuses on the cooperation between Unmanned Aerial Vehicle (UAV) and Unmanned Surface Vessel (USV). It aims to develop efficient guidance and control algorithms for USV based on obstacle identification and path planning from aerial images captured by UAV. Various obstacle scenarios were implemented using the Robot Operating System (ROS) and the Gazebo simulation environment. The aerial images transmitted in real-time from UAV to USV are processed using the computer vision-based deep learning model, You Only Look Once (YOLO), to classify and recognize elements such as the water surface, obstacles, and ships. The recognized data is used to create a two-dimensional grid map. Algorithms such as A^* and Rapidly-exploring Random Tree star (RRT^*) were used for path planning. This process enhances the guidance and control strategies within the UAV-USV collaborative system, especially improving the navigational capabilities of the USV in complex and dynamic environments. This research offers significant insights into obstacle avoidance and path planning in maritime environments and proposes new directions for the integrated operation of UAV and USV.

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.10-20
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• 2024

Recent advancements in autonomous flight technologies for Unmanned Aerial Vehicles (UAVs) have greatly expanded their applicability for various tasks, including delivery, agriculture, and rescue. This article presents a comprehensive survey of path planning techniques in autonomous navigation and exploration that are tailored for UAVs. The robotics literature has studied path and motion planning, from basic obstacle avoidance to sophisticated algorithms capable of dynamic decision-making in challenging environments. In this article, we introduce popular path and motion planning approaches such as grid-based, sampling-based, and optimization-based planners. We further describe the contributions from the state-of-the-art in exploration planning for UAVs, which have been derived from these well-studied planners. Recent research, including the method we are developing, has improved performance in terms of efficiency and scalability for exploration tasks in challenging environments without human intervention. On the basis of these research and development trends, this article discusses future directions in UAV path planning technologies, illustrating the potential for UAVs to perform complex tasks with increased autonomy and efficiency.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.337-345
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• 2023

In this paper, we propose a 3D LiDAR sensor-based costmap generation and path planning algorithm using it for reliable autonomous flight in complex indoor environments. 3D path planning is essential for reliable operation of UAVs. However, existing grid search-based or random sampling-based path planning algorithms in 3D space require a large amount of computation, and UAVs with weight constraints require reliable path planning results in real time. To solve this problem, we propose a method that divides a 3D space into several 2D spaces and a path planning algorithm that considers the distance to obstacles within each space. Among the paths generated in each space, the final path (Best path) that the UAV will follow is determined through the proposed objective function, and for this purpose, we consider the rotation angle of the 2D space, the path length, and the previous best path information. The proposed methods have been verified through autonomous flight of UAVs in real environments, and shows reliable obstacle avoidance performance in various complex environments.

• Journal of Aerospace System Engineering
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• v.14 no.5
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• pp.9-17
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• 2020

Mobile robots, including UAVs perform missions with limited fuel. Therefore, the energy-aware path planning is required to maximize efficiency when the robot is operated for a long time. In this study, we estimated the power consumption for each maneuver of a quadcopter UAV in the 3D environment and applied to the cost functions of D^⁎ Lite. The simulations were performed in a 3D environment that is similar to the industrial sites. The efficiency of path generation was high when the energy-aware path planning with simplified heuristic was applied. In addition, the energy-aware path was generated 19.3 times faster than the shortest path with a difference within 3.2%.