• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.1-8
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• 2008

This paper presents a real-time algorithm for collision detection, collision avoidance and guidance. Three-dimensional point-mass aircraft models are used. For collision detection, conflict probability is calculated by using the Monte-Carlo Simulation. Time at the closest point of approach(CPA) and distance at CPA are needed to determine the collision probability, being compared to certain threshold values. For collision avoidance, one of possible maneuver options is chosen to minimize the collision probability. For guidance to a designated way-point, proportional navigation guidance law is used. Two scenarios on encounter situation are studied to demonstrate the performance of proposed algorithm.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.25 no.4
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• pp.83-90
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• 2017

Unmanned Aerial Vehicles(UAVs) require collision avoidance capabilities equivalent to the capabilities of manned aircraft to enter the airspace of manned aircraft. In the case of Visual Flight Rules of manned aircraft, collision avoidance is performed by 'See-and-Avoid' of pilots. To obtain those capabilities of UAVs named as 'Sense-and-Avoid', sensor-system-based intruder tracking and collision avoidance methods are required. In this study, a multi-sensor-based tracking, data fusion, and collision avoidance algorithm is designed by using a model-based design tool MATLAB/SIMULINK, and validations of the designed model and code using numerical simulations and processor-in-the-loop simulations are performed.

• Journal of Aerospace System Engineering
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• v.15 no.1
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• pp.102-110
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• 2021

Recently, multi-copters equipped with various collision avoidance sensors have been introduced to improve flight stability. LiDAR is used to recognize a three-dimensional position. Multiple cameras and real-time SLAM technology are also used to calculate the relative position to obstacles. A three-dimensional depth sensor with a small process and camera is also used. In this study, a small collision-avoidance multi-copter system capable of in-door flight was developed as a platform for the development of collision avoidance software technology. The multi-copter system was equipped with LiDAR, 3D depth sensor, and small image processing board. Object recognition and collision avoidance functions based on the YOLO algorithm were verified through flight tests. This paper deals with recent trends in drone collision avoidance technology, system design/manufacturing process, and flight test results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.9
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• pp.752-761
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• 2014

With increasing of interest in quad-rotor which has excellent maneuverability recently, a various types of multi-rotor aircraft was developed and commercialized, and there are many kinds of leisure products to be easily operated. In these products, the AR.Drone manufactured by Parrot has an advantage that it is easily operated by user due to the its internal stabilization loop in the on-board computer. Thus it is possible to design the unmanned UAV system easily by using this AR.Drone and its inner loop for the stabilization. For this advantage, KARI(Korea Aerospace Research Institute) has been developing the indoor swarming flight system by using multiple AR.Drones. For this indoor swarming flight, it is necessary that not only the position controller for each AR.Drone, but also the collision avoidance algorithm. Therefore, in this paper, the collision avoidance controller is provided for the swarm flight by using these AR.Drones.

• 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.