• Current Industrial and Technological Trends in Aerospace
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• v.6 no.2
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• pp.143-153
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• 2008

This paper describes the current research trend and future development direction of autonomous flight of the aircraft. The autonomous flight means that aircraft control system recognize and cope with the emergency situation confronted during the flight by itself. Current research for autonomous flight technology is mainly performed for the application to unmanned air vehicle. Considering advent of future air traffic management system and increasing demand of the unmanned air vehicle application, however, autonomous flight technology required to be combined with future air traffic management system. In this paper, the current air traffic management system and anticipating change in future air traffic management system was investigated and research activities of autonomous flight technology was described as well as future prospect.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.122-130
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• 2012

Airborne separation assurance is a key requirement for Free Flight. This paper is to propose autonomous flight algorithm, such as extended authority of delegation, efficiency of airspace issue to deal with the empirical solution for free flight, and to measure flight efficiency and conflict detection and resolution (CD&R) by utilizing flight performance data under the two circumstances of scenario with the modeling of proposed algorithm and potential field algorithm. The results show that the autonomous flight algorithm is superior to the potential field algorithm under the circumstances of free flight airspace in terms of algorithm performance, CD&R, and flight efficiency.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.245-249
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• 2021

In this paper, we present novel simulation contents for drone racing and autonomous flight of drone. With Depth camera and SLAM, we conducted mapping 3 dimensional environment through RTAB-map. The 3 dimensional map is represented by point cloud data. After that we recovered this data in Unreal Engine. This recovered raw data reflects real data that includes noise and outlier. Also we built drone racing contents like gate and obstacles for evaluating drone flight in Unreal Engine. Then we implemented both HITL and SITL by using AirSim which offers flight controller and ROS api. Finally we show autonomous flight of drone with ROS and AirSim. Drone can fly in real place and sensor property so drone experiences real flight even in the simulation world. Our simulation framework increases practicality than other common simulation that ignore real environment and sensor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.595-609
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• 2020

In this study, we propose a decentralized mathematical model for predictive control of a system of multi-autonomous unmanned aerial vehicles (UAVs), also known as drones. Being decentralized and autonomous implies that all members make their own decisions and fly depending on the dynamic information received from other unmanned aircraft in the area. We consider a variety of realistic characteristics, including time delay and communication locality. For this flocking flight, we do not possess control for central data processing or control over each UAV, as each UAV runs its collision avoidance algorithm by itself. The main contribution of this work is a mathematical model for stable group flight even in adverse weather conditions (e.g., heavy wind, rain, etc.) by adding Gaussian noise. Two of our proposed variance control algorithms are presented in this work. One is based on a simple biological imitation from statistical physical modeling, which mimics animal group behavior; the other is an algorithm for cooperatively tracking an object, which aligns the velocities of neighboring agents corresponding to each other. We demonstrate the stability of the control algorithm and its applicability in autonomous multi-drone systems using numerical simulations.

• The Journal of Korea Robotics Society
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• v.6 no.2
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• pp.97-107
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• 2011

This paper describes efficient flight control algorithms for building a reconfigurable ad-hoc wireless sensor networks between nodes on the ground and airborne nodes mounted on autonomous vehicles to increase the operational range of an aerial robot or the communication connectivity. Two autonomous flight control algorithms based on adaptive gradient climbing approach are developed to steer the aerial vehicles to reach optimal locations for the maximum communication throughputs in the airborne sensor networks. The first autonomous vehicle control algorithm is presented for seeking the source of a scalar signal by directly using the extremum-seeking based forward surge control approach with no position information of the aerial vehicle. The second flight control algorithm is developed with the angular rate command by integrating an adaptive gradient climbing technique which uses an on-line gradient estimator to identify the derivative of a performance cost function. They incorporate the network performance into the feedback path to mitigate interference and noise. A communication propagation model is used to predict the link quality of the communication connectivity between distributed nodes. Simulation study is conducted to evaluate the effectiveness of the proposed reconfigurable airborne wireless networking control algorithms.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.193-200
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• 2016

This paper introduces an algorithm to middle-low price drone's autonomous navigation flight system using computer vision and GPS. Existing drone operative system mainly contains using methods such as, by inputting course of the path to the installed software of the particular drone in advance of the flight or following the signal that is transmitted from the controller. However, this paper introduces new algorithm that allows autonomous navigation flight system to locate specific place, specific shape of the place and specific space in an area that the user wishes to discover. Technology developed for military industry purpose was implemented on a lower-quality hobby drones without changing its hardware, and used this paper's algorithm to maximize the performance. Camera mounted on middle-low price drone will process the image which meets user's needs will look through and search for specific area of interest when the user inputs certain image of places it wishes to find. By using this algorithm, middle-low price drone's autonomous navigation flight system expect to be apply to a variety of industries.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.2
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• pp.109-117
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• 2018

The experimental results are presented of an autonomous flight algorithm of a foldable quadcopter with airdrop launching functions. A foldable wing structure enabled the quadcopter to be inserted into a rocket container with limited space. The foldable quadcopter was then separated from the rocket in the air. The flight pattern was tracked using a global positioning system (GPS) with various sensors, including an inertial measurement unit (IMU) module until a designated target position was reached. Extensive field tests were conducted through an international rocket competition, ARLISS 2017, which was held in Black Rock Desert, Nevada, USA. The flight trajectory record of the experiments is stored in electrically erasable programmable read-only memory (EEPROM) embedded in the main control unit. The flight record confirmed that the quadcopter successfully separated from the rocket, executed flight toward the target for a certain length of time, and stably landed on the ground.

• Electronics and Telecommunications Trends
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• v.38 no.3
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• pp.11-19
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• 2023

Drones, which were early operated by remote control, have evolved to enable autonomous flight by combining various sensors and software tools. In particular, autonomous flight of drones was possible since the application of GNSS-RTK (global navigation satellite system with real-time kinematic positioning), a precision satellite navigation technology. For instance, unmanned drone delivery based on GNSS-RTK data was demonstrated for pizza delivery in Korea for the first time in 2021. However, the vulnerabilities of GNSS-RTK should be overcome for delivery drones to be commercialized. In particular, jamming in the navigation system and low positioning accuracy in urban areas should be addressed. Solving these two problems can lead to stable flight, takeoff, and landing of drones in urban areas, and the corresponding solutions are expected to establish a hybrid positioning technology. We discuss current trends in hybrid positioning technology that can either replace or complement GNSS-RTK for stable drone autonomous flight.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.4
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• pp.266-273
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• 2019

The efficiency and effectiveness of mission performance can be greatly changed according to the operating conditions such as the number of manned aircraft, flight altitude, and so on, in performing search and reconnaissance missions using a large number of small reconnaissance unmanned aerial vehicles. However, it is not easy to determine which operating conditions are most reasonable. Therefore, in this study, we developed an unmanned airplane flight simulation that can detect and identify the target while avoiding collision according to autonomous flight, suggesting a way to derive operating conditions when operating a large number of unmanned aerial vehicles.

• Journal of Aerospace System Engineering
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• v.13 no.3
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• pp.15-22
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• 2019

In this paper, a global and local flight path system for autonomous flight of the UAV is proposed. The overall system is based on the ROS robot operating system. The UAV in-built computer detects obstacles through 2-D Lidar and generates real-time local path and global path based on VFH and Modified $RRT^*$-Smart, respectively. Additionally, a movement command is issued based on the generated path on the UAV flight controller. The ground station computer receives the obstacle information and generates a 2-D SLAM map, transmits the destination point to the embedded computer, and manages the state of the UAV. The autonomous UAV flight system of the is verified through a simulator and actual flight.