The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.
翻译:不断将无人驾驶航空器和ML技术合并,正在产生重要的协同作用,赋予无人驾驶航空器以前所未有的情报和自主性,赋予无人驾驶航空器以前所未有的情报和自主性;这项调查旨在及时、全面地概述无人驾驶航空器操作和通信中使用的ML技术,并查明潜在的增长领域和研究差距;我们强调无人驾驶航空器操作和通信的四个关键组成部分,无人驾驶航空器操作和通信可作出重大贡献,即感知和特征提取、特征解释和再生、轨迹和任务规划以及空气动力控制和操作;我们根据应用情况,将最新的流行ML工具分类为四个组成部分,并进行差距分析;这项调查还向前迈出了一步,指出在即将到来的由ML辅助自动无人驾驶航空器操作和通信领域的重大挑战;我们发现,不同的ML技术主导着无人驾驶航空器操作和通信四个关键模块的应用;虽然交叉模块设计的趋势日益增强,但很少致力于从感知和特征提取到空气动力控制和操作的终端框架;我们还披露,在无人驾驶航空器操作和应用程序的可靠性和信任需要大力合作,然后才能使无人驾驶航空器充分自动化和潜力实现自动化。