AI 中文总结
探讨发展中经济体无人机智能操作面临的机器学习挑战，涉及导航、感知等核心功能领域，因资源、环境等因素受限，凸显了实验性能与实际部署间的差距。
AI 中文摘要
无人机环境因平台资源有限、异构传感器数据、动态任务条件和安全关键要求等，给机器学习带来重大挑战。本文在发展中经济体背景下，审视无人机智能核心功能领域（包括导航、感知、通信感知操作和恢复力）的这些限制。在此类环境中，成本敏感、基础设施有限等因素常加剧挑战。讨论突出了受控实验背景下机器学习性能与发展中经济体实际无人机任务可靠部署之间的差距。
英文摘要
Unmanned aerial vehicle (UAV) environments present significant challenges for machine learning (ML) due to limited platform resources, heterogeneous sensor data, dynamic mission conditions, and safety-critical requirements. This paper examines these constraints across the core functional areas of UAV intelligence, including navigation, perception, communication-aware operation, and resilience specifically in the context of developing economies. In such settings, these challenges are often amplified by constraints such as cost sensitivity, limited infrastructure, intermittent connectivity, regulatory uncertainty, and harsh or variable operating environments. The discussion highlights the gap between ML performance in controlled experimental backgrounds and dependable deployment in real-world UAV missions within developing economies context.
