• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.507-509
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• 2015

본 논문에서는 다수의 공중무인기로 구성된 이동센서네트워크를 이용하여 유해 지역 또는 인력 접근이 제한된 지역 감시를 고려한다. 공중무인기는 기존 무인기와 달리 이동 제약이 적으며 기동성이 뛰어나 다양한 분야에서 활용이 가능하다. 그러나 다수의 공중무인기를 이용하여 센서네트워크 구축할 경우 지상 관제센터의 한정된 자원으로 인해 개별 공중무인기 제어에 한계가 있다. 따라서 최소한의 중앙 제어를 통한 신속한 네트워크 구축, 관심 지역으로 위치 이동 그리고 자율적 협업을 통한 센싱커버리지 최대화를 위한 알고리즘이 요구된다. 본 논문에서는 공중무인기의 제한된 센싱 거리와 통신 거리를 바탕으로 다수의 공중무인기를 이용한 가상력 기반 협력 감시 네트워크 구성을 고려한다. 또한 시뮬레이션을 통해 관리자의 별도의 제어 없이 다수 공중무인기의 자율 감시 네트워크를 구축과 지역 감시가 가능함을 보인다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.2
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• pp.159-166
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• 2020

In this paper, we propose a heterogenous mission allocation technique for multi-UAV system based on discrete event modeling. We model a series of heterogenous mission creation, mission allocation, UAV departure, mission completion, and UAV maintenance and repair process as a mathematical discrete event model. Based on the proposed model, we then optimize the number of UAVs required to operate in a given scenario. To validate the optimized number of UAVs, the simulations are executed repeatedly, and their results are analyzed. The proposed mission allocation technique can be used to efficiently utilize limited UAV resources, and allow the human operator to establish an optimal mission plan.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.3
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• pp.282-287
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• 2015

In modern warfare, the importance of SEAD(Suppression of Enemy Air Defense) mission is being emphasized. However, this mission runs the risk of hull damage or casualties of our friendly air forces. Because of these risks, research on the way of minimizing damages and enhancing mission capability is under active discussion. As a part of this research, SEAD mission planning with multiple UAVs has been covered using vector field guidance. This guidance method not only applies to various forms of flight path but also requires less computational power than other guidance methods. Therefore, in this paper, planning methods of SEAD mission for multiple UAVs using vector field guidance and experimental data from flight experiments regarding designed mission has been covered.

• Journal of Advanced Navigation Technology
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• v.25 no.3
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• pp.211-216
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• 2021

Multiple UAV System has been used for various purposes such as reconnaissance, networking and aerial photography. In such systems, it is essential to form and maintain the formation of multiple UAVs. This paper proposes the algorithm that produces an autonomous distributed control for each vehicle for a flexible formation. This command is a repulsive force in the form of the second-order system by the nearest UAV or mission area. The algorithm uses the relative position/speed through sensing and communication for calculating the command without external intervention. The command allows each UAV to follow the reference distance and fill the mission area as densely as possible without overlapping. We determine the reference distance via optimization technique solving the packing problem. The mission area comprises the desired formation outline and can be set flexibly depending on the mission. Numerical simulation is carried out to verify the performance of the proposed algorithm under a complex and flexible environment. The formation is formed in 26.94 seconds and has a packing density of 71.91%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.6
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• pp.565-573
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• 2008

The behavior-based decentralized approach is considered for multi-UAV formation flight. It is assumed that each UAV has its own mission of flying to a specified region, while the distances between UAVs should be maintained. These two requirements may conflict with each other. To design the controller, coupled dynamics approach is applied to multi-UAVs with an assumption that each UAV can communicate with each other to share the state-information. Control gain matrices are optimized to acquire better performances of formation flying. To validate the proposed control approach, numerical simulation is performed for the waypoint-passing mission of multi-UAVs.

• Smart Media Journal
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• v.6 no.4
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• pp.65-71
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• 2017

In this paper, we propose a design method for the construction of LTE-based small unmanned aerial vehicle control system to quickly and reliably collect multiple small unmanned aerial vehicle position information simultaneously flying all over the country. In particular, the main requirements are the network (N/W), hardware (H/ W), software(SW), Database(DB), development architecture, and business needs. To satisfy these requirements, N/W, H/W, SW, DB design, and architectural design plan were suggested regarding the design requirements of a small UAV system. To effectively control the small unmanned multi-party system in the system design, the architecture is divided into the front-end service area and the back-end service area according to the function and role of the unit system. In the front-end service area that grasps and controls the position and state of small unmanned aerial vehicles (UAVs), we have studied the design part that can be expanded to N through TCP/IP network by applying Client PC method.

• Electronics and Telecommunications Trends
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• v.32 no.1
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• pp.82-92
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• 2017

최근 들어 무인기와 무선통신기술의 비약적인 발전은 무인기들을 국가 공역에 진입시켜 운행할 필요성과 택배, 긴급 통신영역 확장, 재해 감시 및 대처, 위험지역 정찰, 항공촬영과 같은 다양한 분야로의 활용에 대한 요구를 크게 증가시켰다. 다수의 무인기를 안전하게 운행 및 조종하기 위해서는 고신뢰성 및 고보안성을 제공하는 무인기 제어 전용 네트워크가 요구되며, 이러한 네트워크를 무인기 제어용 통합 네트워크라고 한다. 본고는 무인기 제어용 통합 네트워크의 보안 적용 구간을 분류하고, 구간별로 보안 요구사항과 요구사항을 달성할 수 있는 보안기술들을 차례로 제시한다. 또한, 향후에 무인기 제어용 통합 네트워크의 보안성을 강화하기 위해 개선되어야 하는 부분들을 제시한다.

• Journal of Aerospace System Engineering
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• v.17 no.2
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• pp.78-85
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• 2023

For Multi-UAV systems, a task allocation could be a key factor to determine the capability to perform a task. In this paper, we proposed a task allocation method based on genetic algorithm for minimizing makespan and satisfying various constraints. To obtain the optimal solution of the task allocation problem, a huge calculation effort is necessary. Therefore, a genetic algorithm-based method could be an alternative to get the answer. Many types of UAVs, tasks, and constraints in real worlds are introduced and considered when tasks are assigned. The proposed method can build the task sequence of each UAV and calculate waiting time before beginning tasks related to constraints. After initial task allocation with a genetic algorithm, waiting time is added to satisfy constraints. Multiple numerical simulation results validated the performance of this mission planning method with minimized makespan.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.10
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• pp.988-995
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• 2008

A consensus-based feedback linearization method is proposed to maintain a specified time-varying geometric configuration for formation flying of multiple autonomous vehicles. In this approach, there exists no explicit leader in the team, and the proposed control strategy requires only the local neighbor-to-neighbor information between vehicles. The information flow topology between the vehicles is defined by Graph Laplacian matrix, and the formation flying can be achieved by the proposed feedback linearization with consensus algorithm. The stability analysis of the proposed controller is also performed via eigenvalue analysis for the closed-looop system. Numerical simulation is performed for rotary-wing type micro aerial vehicles to validate the performance of the proposed controller.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.197-202
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• 2014

In order to operate multiple UAVs for multiple tasks efficiently, we need a task allocation algorithm with minimum cost, i.e.,total moving distance required to accomplish the whole mission. In this paper, we have proposed the MCBAA (Modified Consensus Based Auction Algorithm) which can be suitably applied to the operation of multiple UAVs. The key idea of proposed algorithm is to minimize sum of distance from current location of agents to location of tasks based on the conventional CBAA. Several simulation test performed on three UAV agents with multiple tasks demonstrates the overall efficiency both in time and total distance.