Unmanned aircraft or drones as they are sometimes called are continuing to become part of more real-life applications. The integration of unmanned aerial vehicles in public airspace is becoming an important issue that should be addressed. As the number of unmanned aerial vehicles and their applications are largely increasing, air traffic with more unmanned aircraft has to be given more attention to prevent collisions and maintain safe skies. Unmanned aerial vehicle air traffic integration and regulation has become a priority to different regulatory agencies and has become of greater interest for many researchers all around the world. In this research, a sampling-based air traffic integration, path planning, and collision avoidance approach is presented. The proposed algorithm expands an existing 2D sampling-based approach. The original 2D approach deals with only two unmanned aircraft. Each of the two aircraft shares location information with a ground-based path planner computer, which would send back the avoidance waypoints after performing the 2D sampling. The algorithm proposed in this article can handle any number of drones in the 3D space by performing either 2D or 3D sampling. The proposed work shows a 10-fold enhancement in terms of the number of unmanned aerial vehicle collisions. The presented results also contribute to enabling a better understanding of what is expected of integrating more drones in dense skies.

中文翻译：

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基于采样的无人机空中交通集成、路径规划和防撞

有时被称为无人驾驶飞机或无人机正在继续成为更现实应用的一部分。无人机在公共空域的整合正在成为一个亟待解决的重要问题。随着无人驾驶飞行器的数量及其应用的大幅增加，必须更加注意拥有更多无人驾驶飞机的空中交通，以防止碰撞并保持安全的天空。无人机空中交通整合与监管已成为不同监管机构的优先事项，并引起了世界各地许多研究人员的更大兴趣。在这项研究中，提出了一种基于采样的空中交通集成、路径规划和避免碰撞的方法。所提出的算法扩展了现有的基于 2D 采样的方法。最初的 2D 方法只处理两架无人驾驶飞机。两架飞机中的每一架都与地面路径规划计算机共享位置信息，该计算机将在执行 2D 采样后发回避让航路点。本文提出的算法可以通过执行 2D 或 3D 采样来处理 3D 空间中的任意数量的无人机。拟议的工作在无人驾驶飞行器碰撞的数量方面显示了 10 倍的增强。所呈现的结果还有助于更好地理解在密集的天空中集成更多无人机的预期。本文提出的算法可以通过执行 2D 或 3D 采样来处理 3D 空间中的任意数量的无人机。拟议的工作在无人驾驶飞行器碰撞的数量方面显示了 10 倍的增强。所呈现的结果还有助于更好地理解在密集的天空中集成更多无人机的预期。本文提出的算法可以通过执行 2D 或 3D 采样来处理 3D 空间中的任意数量的无人机。拟议的工作在无人驾驶飞行器碰撞的数量方面显示了 10 倍的增强。所呈现的结果还有助于更好地理解在密集的天空中集成更多无人机的预期。