• 한국산학기술학회논문지
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• 제20권7호
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• pp.211-217
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• 2019

고도화되는 전장 환경에서 적진의 정보는 군사 작전을 수행하는 데 있어 중요한 요소이다. 적진의 정보를 획득하기 위한 감시정찰을 목적으로 아군의 인력 손실이 없고 저 비용으로 임무에 투입이 가능한 무인항공기(UAV; Unmanned Aerial Vehicle, 이하 무인기) 운용이 늘어나고 있다. 무인기 임무 수행 환경은 대공위협이 존재하므로 무인기가 격추되지 않고 생존성을 보장할 수 있는 임무공간이 필요하다. 임무공간을 정의하기 위해서는 형상공간(Configuration Space) 개념을 활용하며, 적진의 대공방호영역 및 무인기가 탐지할 수 있는 범위를 고려한다. 무인기는 적진의 정보를 획득하기 위해 임무에 주어진 모든 영역을 방문해야 하며, 이를 위해 커버리지 경로 계획(Coverage Path Planning)을 수행한다. 무인기에 대한 위협 및 획득할 정보의 중요도를 바탕으로 무인기가 우선적으로 방문해야 할 영역을 정의하며, 격자지도를 생성하고 각 격자에 위협 정보를 매핑하여 경로 계획에 활용한다. 본 연구에서는 격자지도에 표시된 위협정보를 바탕으로 커버리지 조건 및 경로 계획 절차를 제시하며, 탐지 효율 향상을 위해 임무공간을 확장한다. 끝으로 본 연구에서 제시한 무인기 경로계획 방법에 대한 시뮬레이션을 수행하고, 관련된 결과를 제시한다.

• 산업공학
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• 제24권4호
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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.

• 대한안전경영과학회지
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• 제13권2호
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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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• 제12권4호
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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.

• 산업경영시스템학회지
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• 제35권1호
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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.

• 산업경영시스템학회지
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• 제34권3호
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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).

• 전자통신동향분석
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• 제39권4호
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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.

• 로봇학회논문지
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• 제18권3호
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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.

• 항공우주시스템공학회지
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• 제14권5호
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• pp.9-17
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• 2020

무인항공기를 비롯한 다수의 이동로봇은 제한된 연료로 임무를 수행하므로 장시간 운용시 효율을 극대화 하기위해 에너지를 고려한 경로계획이 요구된다. 본 연구에서는 3차원 환경에서 쿼드콥터 무인항공기 비행에 따른 소모 에너지를 근사화하여 기존 D^⁎ Lite 알고리즘의 비용함수에 적용하였다. 산업현장과 유사한 3차원 환경에서 시뮬레이션을 수행한 결과 에너지를 비용함수로 하고 휴리스틱 계산을 3 단순화 하였을 때 경로 생성 효율이 높았으며, 최단경로와 약 3.2% 이내의 차이를 갖는 경로를 최대 19.3배 빠르게 도출했다.

• 한국군사과학기술학회지
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• 제15권4호
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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.