Features of Flight Planning of an Intelligent Unmanned Aircraft in an Undefined Environment
Year: 2025
Type: article
Abstract: In addition, the integration of artificial intelligence (AI) techniques, such as machine learning and neural networks, enhances the UAV's ability to adapt to dynamic environments by continuously refining decision-making models based on real- time data. The implementation of reinforcement learning algorithms enables UAVs to autonomously adjust their flight paths and operational strategies, improving performance in complex missions without direct human intervention. Moreover, the proposed approach allows for seamless integration with multi-agent systems, enabling coordinated UAV operations in collaborative mission scenarios. By leveraging decentralized communication protocols and distributed processing architectures, UAVs can share situational data, optimize resource allocation, and enhance overall mission efficiency. The study also emphasizes the importance of incorporating advanced sensor fusion techniques, where data from multiple onboard sensors. This multimodal data processing capability significantly enhances the UAV's ability to operate effectively in GPS-denied or signal-jammed environments, ensuring mission continuity in contested or adverse conditions. Finally, the research highlights the potential for integrating UAV decision-making frameworks with cloud-based and edge computing technologies. This hybrid computational approach allows UAVs to process critical data locally for low-latency responses while offloading complex calculations to cloud servers when high computational power is required. Such an architecture not only enhances the UAV's real- time operational capabilities but also enables post-mission analysis and optimization based on historical flight data. By incorporating these advancements, the proposed model establishes a robust foundation for next-generation autonomous UAV systems, paving the way for enhanced mission effectiveness, reduced operational risks, and improved adaptability in evolving tactical and commercial applications.
Source: Випробування та сертифікація
Authors: N. Korolyuk, Ye. Drob, O. Pershyn, Yа. Chopenko
Institution: Ivan Kozhedub Kharkiv National Air Force University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409309851