Unmanned aerial vehicles (UAVs) have the potential to make a significant impact in a range of scenarios where it is too risky or too costly to rely on human labour. Fleets of autonomous UAVs, which complete tasks collaboratively while managing their basic flight and related tasks independently, present further opportunities along with research and regulatory challenges. Improvements in UAV construction and components, along with developments in embedded computing hardware, communication mechanisms and sensors which may be mounted on-board a UAV, are nearing the point where commercial deployment of fleets of autonomous UAVs will be technically possible. To fulfil this potential, UAVs will need to operate safely and reliably in complex and potentially dynamically changing environments with path-planning, obstacle sensing and collision avoidance paramount. This survey presents an original environment complexity classification and critically analyses the current state of the art in relation to UAV path-planning approaches. Moreover, it highlights the existing challenges in environment complexity modelling and representation, as well as path-planning approaches, and outlines open research questions together with future directions.

无人驾驶飞行器 (UAV) 有可能在依赖人力劳动风险太大或成本太高的一系列场景中产生重大影响。自主无人机机队在独立管理其基本飞行和相关任务的同时协同完成任务，带来了更多机遇以及研究和监管挑战。UAV 构造和组件的改进，以及嵌入式计算硬件、通信机制和可安装在 UAV 上的传感器的发展，正在接近在技术上可以实现自主 UAV 机队商业部署的程度。为了发挥这一潜力，无人机需要在复杂且可能动态变化的环境中安全可靠地运行，并进行路径规划，障碍物感应和防撞至关重要。本调查提出了一个原始的环境复杂性分类，并批判性地分析了与无人机路径规划方法相关的最新技术水平。此外，它强调了环境复杂性建模和表示以及路径规划方法方面的现有挑战，并概述了开放的研究问题和未来的方向。