A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning

Given the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Drones (Basel) Jg. 7; H. 10; S. 633
Hauptverfasser: Xiong, Tao, Li, Hao, Ding, Kai, Liu, Haoting, Li, Qing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2023
Schlagworte:
3D path planning
Algorithms
B spline functions
Control systems
Cost function
Disaster relief
Disasters
drone
Drone aircraft
Drones
Energy consumption
Flight
Flight paths
Genetic algorithms
Heuristic
improved symbiotic organisms search
Mathematical optimization
Mutation
Optimization techniques
Particle swarm optimization
Path planning
Performance enhancement
Planning
sine–cosine particle swarm optimization
Strategy
Trigonometric functions
Urban areas
China
ISSN:2504-446X, 2504-446X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Given the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial challenge. Nonetheless, prevalent path-planning algorithms exhibit issues encompassing diminished accuracy and inadequate stability. To solve this problem, a hybrid improved symbiotic organisms search (ISOS) and sine–cosine particle swarm optimization (SCPSO) method for drone 3D path planning named HISOS-SCPSO is proposed. In the proposed method, chaotic logistic mapping is first used to improve the diversity of the initial population. Then, the difference strategy, the novel attenuation functions, and the population regeneration strategy are introduced to improve the performance of the algorithm. Finally, in order to ensure that the planned path is available for drone flight, a novel cost function is designed, and a cubic B-spline curve is employed to effectively refine and smoothen the flight path. To assess performance, the simulation is carried out in the mountainous and urban areas. An extensive body of research attests to the exceptional performance of our proposed HISOS-SCPSO.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2504-446X
2504-446X
DOI:10.3390/drones7100633