Unmanned Aerial Vehicles (UAVs) can detect and communicate with cooperative obstacles through established protocols. However, non-cooperative obstacles pose a significant threat to UAVs during low-flight operations. These obstacles include static obstacles like buildings, trees, or communication towers and dynamic objects like other UAVs. The application of autonomous UAVs in low-altitude surveillance has motivated research into non-cooperative local obstacle avoidance. This paper provides an overview of such solutions that have been proposed within the last decade. Unlike most literature that limits obstacle avoidance to algorithms, this work provides an in-depth review of obstacle avoidance components, namely the perception sensor, techniques, and hardware architecture of the obstacle avoidance system. This review categorizes the non-cooperative obstacle avoidance techniques into four groups: gap-based methods, geometric methods, repulsive force-based methods, and Artificial Intelligence (AI) based methods. This paper provides a comprehensive resource for researchers working on collision-free surveillance by autonomous UAVs at low altitudes.

无人机（UAV）可以通过既定协议检测合作障碍物并与之通信。然而，非合作障碍对无人机在低空飞行期间构成重大威胁。这些障碍物包括建筑物、树木或通信塔等静态障碍物以及其他无人机等动态物体。自主无人机在低空监视中的应用激发了对非合作局部避障的研究。本文概述了过去十年内提出的此类解决方案。与大多数将避障限制于算法的文献不同，这项工作对避障组件进行了深入的回顾，即避障系统的感知传感器、技术和硬件架构。本综述将非合作避障技术分为四类：基于间隙的方法、几何方法、基于排斥力的方法和基于人工智能（AI）的方法。本文为从事低空自主无人机无碰撞监视研究的研究人员提供了全面的资源。