• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.4
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• pp.284-291
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• 2017

There is no doubt that advances in UAV technology have improved military performance. However, these advances require humans to adapt to new and complex operational systems. UAV has been rapidly expanding to a variety of fields such as reconnaissance, transportation, communication and aerial photographing recently. Also, with the development of UAV automation technology, one operator is able to supervisory-control multiple-UAVs. However, as the number of assigned UAV increases, the amount of information increases and this results in the workload of the operator increasing and deterioration in controlling performance. Accordingly, there is a need for a model to determine the level of overload an operator may encounter with regard to multiple-UAV but nationally this kind of research is currently lacking. Therefore, this paper provides an experimental platform for evaluating workload index effectiveness integrating multiple-UAV operational environments, GCS, and eye-tracking system followed by a limited survey of domestic and international studies of multi-UAV overload studies.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.529-536
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• 2017

The general ground control system has control and information display functions for the operation of a single unmanned aerial vehicle. Recently, the function of the single ground control system extends to the operation of multiple UAVs. As a result, operators have been exposed to more diverse tasks and are subject to task overload due to various factors during their mission. This study proposes an adaptive ground control system that reflects the operator's condition through the task overload measurement of multiple UAV operators. For this, the ground control software is developed to control multiple UAVs at the same time, and the simulator with six degree-of-freedom aircraft dynamics is constructed for realistic human-machine-interface experiments by the operators.

• Current Industrial and Technological Trends in Aerospace
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• v.7 no.1
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• pp.141-152
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• 2009

The simultaneous operation of multiple UAV's makes it possible for us to raise the mission accomplishment and cost efficiency. For this we need an easily scalable control algorithm, and swarm intelligence having the characteristics such as flexibility, robustness, decentralized control and self-organization comes into the spotlight as a practical substitute. In this paper the features of swarm intelligence are described, and various research results are introduced which show that the application of swarm intelligence to the control of multiple UAV's enables the missions of surveillance, path planning, target tracking and attack to be accomplished efficiently by simulations and tests.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.156-161
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• 2020

In an environment that operates multiple UAVs, the use of a decentralized task allocation algorithm has more robustness from a single failure of UAV on the mission because there is no central command center. In addition, UAVs have situational awareness and redistribute tasks among themselves, which can expand the mission range. The use of multiple UAVs in a mission has increased as the agent hardware has decreased in size and cost. The decentralized mission-planning algorithm has the advantages of a larger mission range and robustness to a single failure during the mission. This paper extended the type of mission the uses CBBA, which is the most well-known decentralized task allocation algorithm, to the point mission and en-route mission. This will describe the real mission situation that has the purpose of surveillance. A Monte-Carlo simulation was conducted in the case of multiple agents in the task-rich environment, and the global rewards of each case were compared.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.352-362
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• 2010

The simultaneous operation of multiple UAV's makes it possible to enhance the mission accomplishment efficiency. In order to achieve this, easily scalable control algorithms are required, and swarm intelligence having such characteristics as flexibility, robustness, decentralized control, and self-organization based on behavioral model comes into the spotlight as a practical substitute. Recently, evolutionary robotics is applied to the control of UAV's to overcome the weakness of difficulties in the logical design of behavioral rules. In this paper, a neural network controller evolved by evolutionary robotics is applied to the control of multiple UAV's which have the mission of searching limited area. Several numerical demonstrations show the proposed algorithm has superior results to those of behavior based neural network controller which is designed by intuition.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.11
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• pp.1042-1051
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• 2011

In this paper, a total operating environment equipped with onboard wireless communication systems and ground-based mission control systems is proposed for simultaneous operation of multiple UAVs. A variety of operating structures are studied and classified systematically based on types and usages of the components. For each operating system, the strength, weakness and reliability aspects are investigated. Based on these results, a proper operating system configuration is determined and components are developed for mission formation flight. Proposed system can make a formation flight of various UAVs, execute complex missions decentralizing mission to several UAVs and cooperate several missions.

• Aerospace Engineering and Technology
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• v.12 no.2
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• pp.118-122
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• 2013

In this study, We show that multiple UAVs (Unmaned Air Vehicle) have datalink system which includes the IEEE 802.11b technology. we are predicting and calculating to the number of the UAV and data rate between UAV and ground control system, using the IEEE 802.11 standards which include the transmit/receive the delay time and theoretical maximum throughput (TMT).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.10
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• pp.859-867
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• 2015

This study proposes a robust formation flight control technique of multiple unmanned aerial vehicles(UAVs) using behavior-based decentralized approach. The behavior-based decentralized method has various advantages because it utilizes information of neighboring UAVs only instead of information of whole UAVs in the formation maneuvering. The controllers in this paper are divided into two methods: first one is based on position and velocity of neighboring UAVs, and the other one is based on position of neighboring UAVs and passivity technique. The proposed controllers assure uniformly ultimate boundedness of closed-loops system under time varying bounded disturbances. Numerical simulations are performed to validate the effectiveness of the proposed method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.6
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• pp.488-496
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• 2015

This paper proposes a framework to determine the routes of multiple unmanned aerial vehicles (UAVs) to conduct multiple tasks in different locations considering the survivability of the vehicles. The routing problem can be formulated as the vehicle routing problem (VRP) with different cost matrices representing the trade-off between the safety of the UAVs and the mission completion time. The threat level for a UAV at a certain location was modeled considering the detection probability and the shoot-down probability. The minimal-cost path connecting two locations considering the threat level and the flight distance was obtained using the Dijkstra algorithm in hexagonal cells. A case study for determining the optimal routes for a persistent multi-UAVs surveillance and reconnaissance missions given multiple enemy bases was conducted and its results were discussed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.3
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• pp.180-190
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• 2017

This paper describes the application of Multi-frame based super-resolution method to enhance resolution of image information from the UAV, and the improvement of UAV's ground target recognition ability. To verify this algorithm, we designed a flight/ground control system, and the UAV, and then the algorithm was validated using the UAV system with ground target. As a result of the comparison between the pre-applied image and post-applied one shows that the RMSE is from 0.0677 to 0.0315, NRMSE is from 7.4030% to 3.5726%, PSNR is from 23.3885dB to 30.0036dB, and SSIM is from 0.6996 to 0.8948. Through these results, we validate this study can enhance the resolution of UAV's image using Multi-frame based super-resolution algorithm.