The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial intelligence (AI) technologies, for instance, computer vision and path planning. These AI methods must process data and provide decisions while ensuring low latency and low energy consumption. However, the existing cloud-based AI paradigm finds it difficult to meet these strict UAV requirements. Edge AI, which runs AI on-device or on edge servers close to users, can be suitable for improving UAV-based IoT services. This article provides a comprehensive analysis of the impact of edge AI on key UAV technical aspects (i.e., autonomous navigation, formation control, power management, security and privacy, computer vision, and communication) and applications (i.e., delivery systems, civil infrastructure inspection, precision agriculture, search and rescue (SAR) operations, acting as aerial wireless base stations (BSs), and drone light shows). As guidance for researchers and practitioners, this article also explores UAV-based edge AI implementation challenges, lessons learned, and future research directions.

中文翻译：

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无人机边缘计算与人工智能融合调查：机遇与挑战


最新的 5G 移动网络已经实现了许多令人兴奋的使用无人机 (UAV/无人机) 的物联网 (IoT) 应用。大多数基于无人机的物联网应用的成功很大程度上依赖于人工智能（AI）技术，例如计算机视觉和路径规划。这些人工智能方法必须处理数据并提供决策，同时确保低延迟和低能耗。然而，现有的基于云的人工智能范式很难满足这些严格的无人机要求。边缘人工智能在设备上或靠近用户的边缘服务器上运行人工智能，适合改进基于无人机的物联网服务。本文全面分析了边缘人工智能对无人机关键技术方面（即自主导航、编队控制、电源管理、安全和隐私、计算机视觉和通信）和应用（即运载系统、民用基础设施检查）的影响、精准农业、搜索和救援 (SAR) 行动、充当空中无线基站 (BS) 和无人机灯光秀）。作为对研究人员和从业者的指导，本文还探讨了基于无人机的边缘人工智能实施挑战、经验教训和未来的研究方向。