Unmanned aerial vehicles (UAVs), specifically quadrotors, have revolutionized various industries with their maneuverability and versatility, but their safe operation in dynamic environments heavily relies on effective collision avoidance techniques. This paper introduces a novel technique for safely navigating a quadrotor along a desired route while avoiding kinematic obstacles. We propose a new constraint formulation that employs control barrier functions (CBFs) and collision cones to ensure that the relative velocity between the quadrotor and the obstacle always avoids a cone of vectors that may lead to a collision. By showing that the proposed constraint is a valid CBF for quadrotors, we are able to leverage its real-time implementation via Quadratic Programs (QPs), called the CBF-QPs. Validation includes PyBullet simulations and hardware experiments on Crazyflie 2.1, demonstrating effectiveness in static and moving obstacle scenarios. Comparative analysis with literature, especially higher order CBF-QPs, highlights the proposed approach's less conservative nature. Simulation and Hardware videos are available here: https://tayalmanan28.github.io/C3BF-UAV/

翻译：无人机，尤其是四旋翼飞行器，凭借其机动性和多功能性已彻底改变了多个行业，但其在动态环境中的安全运行高度依赖于有效的碰撞规避技术。本文提出了一种新方法，可在沿期望路径安全导航四旋翼飞行器的同时规避运动障碍物。我们引入了一种新的约束公式，利用控制障碍函数和碰撞锥，确保四旋翼与障碍物之间的相对速度始终避开可能导致碰撞的向量锥。通过证明所提约束是四旋翼的有效控制障碍函数，我们得以借助二次规划实现其实时应用，称为CBF-QP。验证工作包括PyBullet仿真和Crazyflie 2.1硬件实验，展示了其在静态和移动障碍物场景中的有效性。与文献中的方法（特别是高阶CBF-QP）进行的对比分析突显了所提方法较不保守的特性。仿真与硬件视频可访问：https://tayalmanan28.github.io/C3BF-UAV/