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http://dx.doi.org/10.5139/JKSAS.2022.50.6.385

Hierarchical Particle Swarm Optimization for Multi UAV Waypoints Planning Under Various Threats  

Chung, Wonmo (Ulsan National Institute of Science and Technology)
Kim, Myunggun (Ulsan National Institute of Science and Technology)
Lee, Sanha (Ulsan National Institute of Science and Technology)
Lee, Sang-Pill (LIG Nex1)
Park, Chun-Shin (LIG Nex1)
Son, Hungsun (Ulsan National Institute of Science and Technology)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.50, no.6, 2022 , pp. 385-391 More about this Journal
Abstract
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.
Keywords
Fixed-Wing UAV; Path Planning; Particle Swarm Optimization; Software-in-the-loop Simulation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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