An effective memetic algorithm for UAV routing and orientation under uncertain navigation environments

Navigation correction is usually frequently required by unmanned aerial vehicles (UAVs), especially under uncertain navigation environments. Although the UAV’s straight flights that connect navigation correction points can constitute a plan of navigation corrections, the underlying attitude orientat...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Memetic computing Jg. 13; H. 2; S. 169 - 183
Hauptverfasser: Xiang, Shang, Wang, Ling, Xing, Lining, Du, Yonghao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021
Springer Nature B.V
Schlagworte:
Algorithms
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Complex Systems
Control
Engineering
Heuristic methods
Knowledge bases (artificial intelligence)
Mathematical and Computational Engineering
Mechatronics
Navigation
Optimization
Regular Research Paper
Robotics
Steering
Unmanned aerial vehicles
Navigation correction
Memetic algorithm
Routing
Uncertainty
Unmanned aerial vehicle
Orientation
ISSN:1865-9284, 1865-9292
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Navigation correction is usually frequently required by unmanned aerial vehicles (UAVs), especially under uncertain navigation environments. Although the UAV’s straight flights that connect navigation correction points can constitute a plan of navigation corrections, the underlying attitude orientations of the UAV when flying through the visited points are also required by appropriate steering motions. In this regard, a UAV routing and orientation problem (UAV-ROP) that minimizes the 3D flight distances of the UAV under navigational, steering and uncertain constraints, is introduced and proven NP-hard in this paper. To optimize the layered routing and orientations in the UAV-ROP simultaneously, an effective memetic algorithm is proposed in this paper. In the algorithm, the GA performs the outer loop for optimizing the route and the local search metaheuristic does the inner loop for optimizing the orientations. Also, a globally maintained knowledge base that records high-quality sub-routes is used to accelerate the inner optimization of the memetic algorithm. The highlight in addressing the UAV-ROP in this paper is a layered optimization idea in a memetic algorithm to fit the layered optimization requirements of the problem. Experiments on open-access datasets indicate that, the proposed memetic algorithm shows an excellent overall performance compared with other competitors, which is qualified to give an authenticated reliable route with orientations of the UAV despite uncertain navigation environments.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1865-9284
1865-9292
DOI:10.1007/s12293-021-00334-9