• 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.

• 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.

• 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.

• ETRI Journal
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• v.43 no.4
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• pp.580-593
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• 2021

When operating in confined spaces or near obstacles, collision-free path planning is an essential requirement for autonomous exploration in unknown environments. This study presents an autonomous exploration technique using a carefully designed collision-free local planner. Using LiDAR range measurements, a local end-point selection method is designed, and the path is generated from the current position to the selected end-point. The generated path showed the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The results consistently demonstrated the safety and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flights in environment with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial robot systems. In addition, our drone performed an autonomous mission in the tunnel circuit competition (Phase 1) of the DARPA Subterranean Challenge.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.229-235
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• 2023

Recently, for drones to be used for agricultural applications, it is necessary to increase the payload and extending flight time. Currently, the payload and extending flight time are limited by the battery technology for solar paper drone. In addition, charging or replacing the batteries may be a practical solution at the field that requires near continuous operation. In this paper, the procedure to optimize the main power system of an electric hybrid drone that consists of a battery and electric motor is presented. As a result, the solar paper drone flied successfully for 2-3%. The developed solar paper drone consumes and average of 55W when cruising and can receive up to 25W of energy during the day, and its extending flight time was verified through flight tests.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.448-453
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• 2023

As the production of small quadcopter drones has diversified and multi-sensors have been installed in FC due to the spread of MCU capable of high-speed processing, small drones that can perform special-purpose operations rather than simple operations have been realized. Hovering, attitude control, and position movement control were possible through the IMU in the FC mounted on the drone, but control is not easy when GPS connection and video communication are not possible in a closed building with a complex structure. In this study, when encountering an obstacle with a change in altitude in such a space, we proposed a method to overcome the obstacle and perform autonomous flight using optical flow and IR sensors using the Lucas-Kanade method. Through experiments, the drone's altitude flight on stairs that replace the complex structure of a closed space with stable hovering motion has a success rate of 98% within the tolerance of 10 [cm] due to external influences, and reliable autonomous flight up and down is achieved.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.472-477
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• 2023

In this paper, we proposes the use of Model Predictive Control (MPC) techniques to enable quadcopter drones to effectively follow paths and maintain flight safety even under dynamic external environments and disturbances. Through simulations conducted in MATLAB/Simulink, the performance of two controllers, PID and MPC, is compared in flight scenarios with disturbances. The proposed design method shows that the MPC controller, when compared to the PID controller, exhibits a difference in the Mean Squared Error between the intended flight path and the actual path of the quadcopter drone. This difference is 0.2 in performance under no disturbance, and it increases to 0.8 under disturbance, demonstrating the improved path following accuracy of the MPC controller.

• 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.

• Electronics and Telecommunications Trends
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• v.36 no.2
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• pp.1-11
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• 2021

As sensors such as Inertial Measurement Unit, cameras, and Light Detection and Rangings have become cheaper and smaller, research has been actively conducted to implement functions automating micro aerial vehicles such as multirotor type drones. This would fully enable the autonomous flight of drones in the real world without human intervention. In this article, we present a survey of state-of-the-art development on autonomous drones. To build an autonomous drone, the essential components can be classified into pose estimation, environmental perception, and obstacle-free trajectory generation. To describe the trend, we selected three leading research groups-University of Pennsylvania, ETH Zurich, and Carnegie Mellon University-which have demonstrated impressive experiment results on automating drones using their estimation, perception, and trajectory generation techniques. For each group, we summarize the core of their algorithm and describe how they implemented those in such small-sized drones. Finally, we present our up to date research status on developing an autonomous drone.

• Electronics and Telecommunications Trends
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• v.35 no.4
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• pp.81-90
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• 2020

The use of machine learning technologies such as deep and reinforcement learning has proliferated in various domains with the advancement of deep neural network studies. To make the learning successful, both big data acquisition and fast processing are required. However, for some physical world applications such as autonomous drone flight, it is difficult to achieve efficient learning because learning with a premature A.I. is dangerous, cost-ineffective, and time-consuming. To solve these problems, simulation-based approaches can be considered. In this study, we analyze recent trends in drone simulation technologies and compare their features. Subsequently, we introduce Octopus, which is a highly precise and scalable drone simulator being developed by ETRI.