The design of automatic air collision avoidance system based on geometric

Xiong, Chao; Wujie, Xie; Dong, Wenhan

doi:10.1109/ccdc.2018.8407138

Cited by 1 publication

(1 citation statement)

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“…However, it is essential to note that many of these approaches primarily cater to rotary-wing UAVs capable of stopping and hovering, rendering them less suitable for fixed-wing UAVs. For fixed-wing aircraft, a range of geometric approaches, such as forming circular arcs or Three-Dimensional (3D) trajectories [11][12][13], velocity modulation in the horizontal plane [14], velocity obstacle concepts [15][16][17][18], differential geometry concepts [19], and the use of collision cones [20,21], have been proposed. While these methods provide some applicability to fixed-wing aircraft, they may suffer from limitations such as high computational demands and a lack of intuitive understanding.…”

Section: Introductionmentioning

confidence: 99%

Genetic Fuzzy Inference System-Based Three-Dimensional Resolution Algorithm for Collision Avoidance of Fixed-Wing UAVs

Rauniyar,

Kim

2023

Electronics

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Fixed-wing Unmanned Aerial Vehicles (UAVs) cannot fly at speeds lower than critical stall speeds. As a result, hovering during a potential collision scenario, like with rotary-wing UAVs, is impossible. Moreover, hovering is not an optimal solution for Collision Avoidance (CA), as it increases mission time and is innately fuel-inefficient. This work proposes a decentralized Fuzzy Inference System (FIS)-based resolution algorithm that modulates the point-to-point mission path while ensuring the continuous motion of UAVs during CA. A simplified kinematic guidance model with coordinated turn conditions is considered to control the UAVs. The model employs a proportional-derivative control of commanded airspeed, bank angle, and flight path angle. The commands are derived from the desired path, characterized by airspeed, heading, and altitude. The desired path is, in turn, obtained using look-ahead points generated for the target point. The FIS aims to mimic human behavior during collision scenarios, generating modulation parameters for the desired path to achieve CA. Notably, it is also scalable, which makes it easy to adjust the algorithm parameters, as per the required missions, and factors specific to a given UAV. A genetic algorithm was used to optimize FIS parameters so that the distance traveled during the mission was minimized despite path modulation. The proposed algorithm was optimized using a pairwise conflict scenario. The effectiveness of the algorithm was evaluated through a Monte Carlo simulation of random conflict scenarios involving multiple UAVs operating in a confined space.

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