GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems

Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves detecting landing targets, perceiving obstacles, planning collision-free paths, and controlling UAV movements for safe landing. Failures can lead to...

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Vydané v:	Proceedings / International Conference on Software Engineering s. 411 - 423
Hlavní autori:	Liang, Linfeng, Deng, Yao, Morton, Kye, Kallinen, Valtteri, James, Alice, Seth, Avishkar, Kuantama, Endrowednes, Mukhopadhyay, Subhas, Han, Richard, Zheng, Xi
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 26.04.2025
Predmet:	Autonomous aerial vehicles Autonomous systems Diversity reception Genetic Algorithm Genetic algorithms Reinforcement learning Safety Search-based testing Testing Training Trajectory UAV auto-landing system Vehicle dynamics
ISSN:	1558-1225
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