Drones, Journal Year: 2024, Volume and Issue: 8(11), P. 644 - 644

Published: Nov. 5, 2024

Path planning is a fundamental research issue for enabling autonomous flight in unmanned aerial vehicles (UAVs). An effective path algorithm can greatly improve the operational efficiency of UAVs complex environments like urban and mountainous areas, thus offering more extensive coverage various tasks. However, existing algorithms often encounter problems such as high computational costs tendency to become trapped local optima 3D with multiple constraints. To tackle these problems, this paper introduces hybrid multi-strategy artificial rabbits optimization (HARO) efficient stable UAV environments. realistically simulate scenarios, we introduce spherical cylindrical obstacle models. The HARO balances exploration exploitation phases using dual switching strategy population migration memory mechanism, enhancing search performance avoiding optima. Additionally, key point retention trajectory proposed reduce redundant points, lowering costs. Experimental results confirm algorithm’s superior performance, paths effectively reduces during optimization, thereby adaptability.

Language: Английский