• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.84-90
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• 2019

Applications of unmanned aerial vehicles (UAVs) in the military environment have become popular because they require minimum human contribution and can avoid accidents during missions. UAVs are employed in various missions such as reconnaissance, observation, aggression, and protection. Consequently, counter-measures, known as anti-drone technologies, have been developed as well. In order to protect against threats from anti-drone technologies and enhance the survivability of UAVs, this study proposes an evasive measure. The proposed bio-inspired evasive maneuver of a UAV mimics a dragonfly's irregular flight. The unpredictable UAV movement is able to confuse enemies and avoid threats, thereby enhancing the UAV's survivability. The proposed system has been implemented on a commercial UAV platform (AR Drone 2.0) and tested in a real environment. The experiment results demonstrate that the proposed flight pattern has larger displacement values compared to a regular flight maneuver, thus making the UAV's position is difficult to predict.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.3
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• pp.267-281
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• 2012

This paper reviews the flight mechanics and control of birds and bird-size aircraft. It is intended to fill a niche in the current survey literature which focuses primarily on the aerodynamics, flight dynamics and control of insect scale flight. We review the flight mechanics from first principles and summarize some recent results on the stability and control of birds and bird-scale aircraft. Birds spend a considerable portion of their flight in the gliding (i.e., non-flapping) phase. Therefore, we also review the stability and control of gliding flight, and particularly those aspects which are derived from the unique control features of birds.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.25-33
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• 2019

This paper presents a novel evolutionary framework for optimizing a bio-inspired fully dynamic neurocontroller for the maneuverable flapping flight of a simulated bird-sized ornithopter robot which takes advantage of the morphological computation and mechansensory feedback to improve flight stability. In order to cope with the difficulty of generating robust flapping flight and its maneuver, the wing of robot is modelled as a series of sub-plates joined by passive torsional springs, which implements the simplified version of feathers attached to the forearm skeleton. The neural controller is designed to have a bilaterally symmetric structure which consists of two fully connected neural network modules receiving mirrored sensory inputs from a series of flight navigation sensors as well as feather mechanosensors to let them participate in pattern generation. The synergy of wing compliance and its sensory reflexes gives a possibility that the robot can feel and exploit aerodynamic forces on its wings to potentially contribute to the agility and stability during flight. The evolved robot exhibited target-following flight maneuver using asymmetric wing movements as well as its tail, showing robustness to external aerodynamic disturbances.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.385-391
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• 2022

This paper presents to develop a path planning algorithm combining gradient descent-based path planning (GBPP) and particle swarm optimization (PSO) for considering prohibited flight areas, terrain information, and characteristics of fixed-wing unmmaned aerial vehicle (UAV) in 3D space. Path can be generated fast using GBPP, but it is often happened that an unsafe path can be generated by converging to a local minimum depending on the initial path. Bio-inspired swarm intelligence algorithms, such as Genetic algorithm (GA) and PSO, can avoid the local minima problem by sampling several paths. However, if the number of optimal variable increases due to an increase in the number of UAVs and waypoints, it requires heavy computation time and efforts due to increasing the number of particles accordingly. To solve the disadvantages of the two algorithms, hierarchical path planning algorithm associated with hierarchical particle swarm optimization (HPSO) is developed by defining the initial path, which is the input of GBPP, as two variables including particles variables. Feasibility of the proposed algorithm is verified by software-in-the-loop simulation (SILS) of flight control computer (FCC) for UAVs.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.9
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• pp.712-722
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• 2018

This paper conducts parametric studies on flapping wing design, one of the most important design parameters of insect-mimicking aerial vehicles. Experimental study on wing shape was done through comparison and analysis of thrust, pitching moment, power consumption, and thrust-to-power ratio. A two-axis balance and hall sensor measure force and moment, and flapping frequency, respectively. Wing configuration is biplane configuration which can develop clap and fling effect. A reference wing shape is a simplified dragonfly's wing and studies on aspect ratio and wing area were implemented. As a result, thrust, pitching moment, and power consumption tend to increase as aspect ratio and area increase. Also, it is found that the flapping mechanism was not normally operated when the main wing has an aspect ratio or area more than each certain value. Finally, the wing shape is determined by comparing thrust-to-power ratio of all wings satisfying the required minimum thrust. However, the stability is not secured due to moment generated by disaccord between thrust line and center of gravity. To cope with this, aerodynamic dampers are used at the top and bottom of the fuselage; then, indoor flight test was attempted for indirect performance verification of the parametric study of the main wing.