• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.981-988
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• 2023

As the usage of unmanned aerial vehicles expands, the development and the demand of related technologies are increasing. As the frequency of operation increases and the convenience of operation is emphasized, the importance of related autonomous flight technology is also highlighted. Establishing a path plan to reach the destination in autonomous flight of an unmanned aerial vehicle is important in guidance and control, and a technology for automatically generating path plan is required in order to maximize the effect of unmanned aerial vehicle. In this study, the optimization research of path planning using rapid-exploring random tree method was performed for increasing the effectiveness of autonomous operation. The path planning optimization method considering the characteristics of the unmanned aerial vehicle is proposed. In order to achieve indexes such as optimal distance, shortest time, and passage of mission points, the path planning was optimized in consideration of the mission goals and dynamic characteristics of the unmanned aerial vehicle. The proposed methods confirmed their applicability to the generation of path planning for unmanned aerial vehicles through performance verification for obstacle situations.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.401-407
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• 2016

This paper introduces 3-dimensional path planning and guidance including power model for high altitude long endurance (HALE) UAV using solar energy. Dubins curve used in this paper has advantage of being directly available to apply path planning. However, most of the path planning problems using Dubins curve are defined in a two-dimensional plan. So, we used 3-dimensional Dubins path generation algorithm which was studied by Randal W. Beard. The aircraft model which used in this paper does not have an aileron. So we designed lateral controller by using a rudder. And then, we were conducted path tracking simulations by using a nonlinear path tracking algorithm. We generate examples according to altitude conditions. From the path tracking simulation results, we confirm that the path tracking is well on the flight path. Finally, we were modeling the power system of HALE UAVs and conducting path tracking simulation during 48hours. Modeling the amount of power generated by the solar cell through the calculation of the solar energy yield. And, we show the 48hours path tracking simulation results.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.316-322
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• 2023

The increasing popularity of autonomous unmanned aerial vehicles (UAVs) can be attributed to their wide range of applications. 3D path planning is one of the crucial components enabling autonomous flight. In this paper, we present a novel 3D path planning algorithm that generates and utilizes curvature-based trajectories. Our approach leverages circular properties, offering notable advantages. First, circular trajectories make collision detection easier. Second, the planning procedure is streamlined by eliminating the need for the spline process to generate dynamically feasible trajectories. To validate our proposed algorithm, we conducted simulations in Gazebo Simulator. Within the simulation, we placed various obstacles such as pillars, nets, trees, and walls. The results demonstrate the efficacy and potential of our proposed algorithm in facilitating efficient and reliable 3D path planning for UAVs.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.5
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• pp.379-387
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• 2019

The complexity of path planning for visiting multiple mission points is even larger than that of single pair path planning. Deciding a path for visiting n mission points requires conducting $n^2+n$ times of single pair path planning. We propose Multiple Mission $D^*$ Lite($MMD^*L$) which is an optimal path planning algorithm for visiting multiple mission points in dynamic environments. $MMD^*L$ reduces the complexity by reusing the computational data of preceding single pair path planning. Simulation results show that the complexity reduction is significant while its path optimality is not compromised.

• Journal of Satellite, Information and Communications
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• v.12 no.1
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• pp.76-80
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• 2017

With the fast growing popularity of small UAVs (Unmanned Aerial Vehicles), recent UAV systems have been designed and utilized for the various field with their own specific purposes. UAVs are opening up many new opportunities in the fields of electronics, sensors, camera, and software for pilots. Increase in awareness and mission capabilities of UAVs are driving innovations and new applications driven with the help of low cost and its capability in undertaking high threat task. In particular, small unmanned aerial vehicles should fly in environments with high probability of unexpected sudden change or obstacle appearance in low altitude situations. In this paper, current researches regarding techniques of autonomous flight of smal UAV systems are introduced and we propose a draft idea for planning paths for small unmanned aerial vehicles in adversarial environments to arrive at the given target safely with low cost sensors.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.98-109
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• 2010

This paper deals with a new method of unmanned aerial vehicle (UAV) recovery when a UAV fails to get a global positioning system (GPS) signal at an unprepared site. The proposed method is based on the simultaneous localization and mapping (SLAM) algorithm. It is a process by which a vehicle can build a map of an unknown environment and simultaneously use this map to determine its position. Extensive research on SLAM algorithms proves that the error in the map reaches a lower limit, which is a function of the error that existed when the first observation was made. For this reason, the proposed method can help an inertial navigation system to prevent its error of divergence with regard to the vehicle position. In other words, it is possible that a UAV can navigate with reasonable positional accuracy in an unknown environment without the aid of GPS. This is the main idea of the present paper. Especially, this paper focuses on path planning that maximizes the discussed ability of a SLAM algorithm. In this work, a SLAM algorithm based on extended Kalman filter is used. For simplicity's sake, a blimp-type of UAV model is discussed and three-dimensional pointed-shape landmarks are considered. Finally, the proposed method is evaluated by a number of simulations.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.88-92
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• 2018

Currently drone industry has become one of the fast growing markets and the technology for unmanned aerial vehicles are expected to continue to develop at a rapid rate. Especially small unmanned aerial vehicle systems have been designed and utilized for the various field with their own specific purposes. In these fields the path planning problem to find the shortest path between two oriented points is important. In this paper we introduce a path planning strategy for an autonomous flight of unmanned aerial vehicles through reinforcement learning with self-positioning technique. We perform Q-learning algorithm, a kind of reinforcement learning algorithm. At the same time, multi sensors of acceleraion sensor, gyro sensor, and magnetic are used to estimate the position. For the functional evaluation, the proposed method was simulated with virtual UAV environment and visualized the results. The flight history was based on a PX4 based drones system equipped with a smartphone.

• Journal of Aerospace System Engineering
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• v.8 no.4
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• pp.32-38
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• 2014

Sufficient energy charging during a day is essential for a solar-powered long-endurance aircraft. Variations of flight path that is superior to a basic circle path are sought in this study for more efficient charging. Flight path associated with roll and pitch attitudes are investigated. It seems that the pitch angle can play more important role than the roll angle for the solar charging efficiency. Thus, more energy charging is observed when the entire flight path is tilted toward the direction of the sun.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1007-1021
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• 2022

Autonomous driving of unmanned aerial vehicles may have to pay expensive cost to create and switch new routes if unexpected obstacles exist or local map updates occured by the control system due to incorrect route information. Integrating digital twins into the path-following process requires more computing resources to quickly switch the wrong path to an alternative path, but it can quickly update the path during flight. In this study, we design a DEVS-based simulation environment which can modify optimized paths through short-term simulation of multi-virtual UAVs for applying digital twin concepts to path follow. Through simulation, we confirmed the possibility of increasing the mission stability of UAV.