Over the last years, Unmanned Aerial Vehicles (UAVs) have seen significant advancements in sensor capabilities and computational abilities, allowing for efficient autonomous navigation and visual tracking applications. However, the demand for computationally complex tasks has increased faster than advances in battery technology. This opens up possibilities for improvements using edge computing. In edge computing, edge servers can achieve lower latency responses compared to traditional cloud servers through strategic geographic deployments. Furthermore, these servers can maintain superior computational performance compared to UAVs, as they are not limited by battery constraints. Combining these technologies by aiding UAVs with edge servers, research finds measurable improvements in task completion speed, energy efficiency, and reliability across multiple applications and industries. This systematic literature review aims to analyze the current state of research and collect, select, and extract the key areas where UAV activities can be supported and improved through edge computing.

翻译：近年来，无人机在传感器能力和计算能力方面取得了显著进步，实现了高效的自主导航和视觉跟踪应用。然而，对复杂计算任务的需求增长速度已超过电池技术的进步速度。这为利用边缘计算进行改进提供了可能性。在边缘计算中，边缘服务器通过战略性地理部署，能够比传统云服务器实现更低延迟的响应。此外，这些服务器不受电池限制，与无人机相比能保持更优越的计算性能。通过将边缘服务器与无人机技术相结合，研究发现，在任务完成速度、能源效率和可靠性方面，跨多个应用和行业均可实现可量化的改进。本系统性文献综述旨在分析当前研究现状，收集、筛选并提取无人机活动可通过边缘计算得到支持与改进的关键领域。