• Journal of Aerospace System Engineering
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• v.13 no.3
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• pp.40-47
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• 2019

This paper proposes the environment construction and test method of system integration laboratory (SIL) and system integration test (SIT) for verification of interface between onboard equipment and ground control equipment of unmanned aerial systems (UAS). This research also describes the interface environment between subsystems built in SIL and verification methods for the systems' operation logic through simulated flights. Similarly, the paper handles the ground integration test process of UAS in the real testing environments.

• International Journal of Aeronautical and Space Sciences
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• v.5 no.1
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• pp.57-63
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• 2004

UAV(Unmanned Aerial Vehicle) has become one of the most popularmilitary/commercial aerial robots in the new millennium. In spite of all theadvantages that UAVs inherently have, it is not an easv job to develop a UAVbecause it requires very systematic and complete approaches in full developmentenvelop. The ground test and evaluation phase has the utmost importance in thesense that a well-developed system can be best verified on the ground. In addition,many of the aircraft crashes in the flight tests were resulted from the incompletedevelopment procedure. In this research, a verification procedure of the wholeairbome integrated system was conducted including the flight management system.An airbome flight control computer(FCC) senses the extemal environment from thepehpheral devices and sends the control signal to the actuating system using theassigned control logic and flight test strategy. A ground test station controls themission during the test while the downlink data are transferred from the flightmanagement computer using the serial communication interface. The pilot controlbox also applies additional manual actuating commands. The whole system wastested/verified on the wind-tunnel system, which gave a good pitch controlperformance with a preUspecified flight test procedure. The ground test systemguarantees the performance of fundamental functions of airbome electronic systemfor the future flight tests.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.101-106
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• 2010

Researches on air-ground robot cooperating system has been made recently. The cooperation among homogeneous robots focused on the architecture of the system, quality and influence of the communication. In contrast, the cooperation among heterogeneous robots such as aerial vehicle and ground vehicle robots has not been much handled. There are a couple of main points for those air-ground cooperating robots. One is using UAV (Unmanned Aerial Vehicle) as an extra sensor of UGV (Unmanned Ground Vehicle). This kind of application is usually used in situations such as guiding UGV to an appropriate path which could be better determined from the eye in the sky as UAV. The other main application of air-ground cooperating robot system is the localization. By combining sensor information from both UAV and UGV, the robot system as a whole can localize a target object or find features in the environment with better performance than UGV or UAV alone. Although these applications are recently studied in many different ways and devices, there are still a lot of possibilities in the field of air-ground cooperating robot systems. We introduce those research fields in this paper.

• Journal of Aerospace System Engineering
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• v.13 no.6
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• pp.36-42
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• 2019

This research illustrates how the simulation environment for operating the simultaneous man/unmanned aerial vehicle teaming is constructed. X-Plane program, HILS for the ducted fan aircraft (unmanned) and CTLS (manned aircraft) with communication devices are interfaced to simulate the basic co-operational flight. The X-plane and HILS can allow operators to experience the maned and unmanned aircraft operation in the airspace on the ground in turn they can perform various simulated missions in advance before the actual flight. For the test purpose, the data link between man/unmanned aircraft and ground control station is examined using C Band and UHF radio channels by the manned aircraft.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.4
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• pp.329-334
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• 2021

Korean Air has set the goal of the first stage of the development of unmanned helicopters to perform in hovering flight by remote control. In order to achieve the development goal, Korean Air carried out system integration, ground test, and safety wire test in sequence after carrying out programmed depot maintenance and aircraft modification of manned aircraft, and verified the controllability and flight safety of the unmanned helicopter system step by step. In particular, it was confirmed that the safety wire test technique used in the final stage of verification was an effective method to verify flight safety and controllability for a fully unmanned helicopter system.

• Convergence Security Journal
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• v.19 no.5
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• pp.91-98
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• 2019

The military is facing an era in which it must take advantage of technologies related with the 4th Industrial Revolution. The representative of the final application of the 4th Industrial Revolution technology is the unmanned system. Many unmanned systems have not yet been harnessed to the army, and it is assumed based on literature studies that improving familiarity with unmanned systems will have a positive effect on force strength in the future. To verify these assumptions, the structural equation model(SEM) was used for quantitative analysis. The results suggest that the increase in familiarity with unmanned systems induces the increase in availability of unmanned systems in each field, which eventually raises the expectation that overall ground force strength will increase. These results imply that the military should reinforce the curriculum that enhances the familiarity of the unmanned system in military education system.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.359-370
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• 2007

Lane tracking and obstacle avoidance are considered two of the key technologies on an unmanned ground vehicle system. In this paper, we propose a method of lane tracking and obstacle avoidance, which can be expressed as vehicle control, modeling, and sensor experiments. First, obstacle avoidance consists of two parts: a longitudinal control system for acceleration and deceleration and a lateral control system for steering control. Each system is used for unmanned ground vehicle control, which notes the vehicle's location, recognizes obstacles surrounding it, and makes a decision how fast to proceed according to circumstances. During the operation, the control strategy of the vehicle can detect obstacle and perform obstacle avoidance on the road, which involves vehicle velocity. Second, we explain a method of lane tracking by means of a vision system, which consists of two parts: First, vehicle control is included in the road model through lateral and longitudinal control. Second, the image processing method deals with the lane tracking method, the image processing algorithm, and the filtering method. Finally, in this paper, we propose a method for vehicle control, modeling, lane tracking, and obstacle avoidance, which are confirmed through vehicles tests.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.27-35
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• 2008

A navigation system is one of the important components of an unmanned ground vehicle (UGV). A GPS receiver collects data signals transmitted by (Earth orbiting) satellites. However, these data signals may contain many errors resulting misinformation and depending on one's position (environment), reception may be impossible. The proposed self-driven algorithm uses three low-cost GPS in order to minimize errors of existing inexpensive single GPS's driving algorithm. By using reliable final data, which is analyzed and combined from each of three GPS's received data signals, gathering a vehicle's steering performance information and its current pin-point position is improved even with error containing signals or from a place where signal gathering is impossible. The purpose of this thesis is to explain navigation system algorithm using multiple GPS and compass sensor and prove the algorithm through experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.436-443
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• 2011

Recognition of structures in urban environments is a fundamental ability for unmanned ground vehicles. In this paper we propose the geometrical featured voxel which has not only 3-D coordinates but also the type of geometrical properties of point cloud. Instead of dealing with a huge amount of point cloud collected by range sensors in urban, the proposed voxel can efficiently represent and save 3-D urban structures without loss of geometrical properties. We also provide an urban structure classification algorithm by using the proposed voxel and machine learning techniques. The proposed method enables to recognize urban environments around unmanned ground vehicles quickly. In order to evaluate an ability of the proposed map representation and the urban structure classification algorithm, our vehicle equipped with the sensor system collected range data and pose data in campus and experimental results have been shown in this paper.