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. The proposed approach employs control barrier functions and utilizes collision cones to ensure that the quadrotor's velocity and the obstacle's velocity always point away from each other. In particular, we propose a new constraint formulation that ensures 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 control barrier function (CBFs) for quadrotors, we are able to leverage on its real-time implementation via Quadratic Programs (QPs), called the CBF-QPs. We validate the effectiveness of the proposed CBF-QPs by demonstrating collision avoidance with moving obstacles under multiple scenarios. This is shown in the pybullet simulator.Furthermore we compare the proposed approach with CBF-QPs shown in literature, especially the well-known higher order CBF-QPs (HO-CBF-QPs), where in we show that it is more conservative compared to the proposed approach. This comparison also shown in simulation in detail.

翻译：无人机（UAV），特别是四旋翼飞行器，凭借其机动性和多功能性彻底改变了多个行业，但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。本文提出了一种新型技术，用于在避开运动学障碍物的同时安全沿着预设路径引导四旋翼飞行器。该方法采用控制障碍函数并利用碰撞锥，确保四旋翼飞行器速度与障碍物速度始终相互远离。具体而言，我们提出一种新的约束公式，保证四旋翼飞行器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飞行器的有效控制障碍函数（CBFs），我们能够利用二次规划（QPs）实现其实时部署，即所谓的CBF-QPs。通过在多种场景下展示对移动障碍物的碰撞规避效果，验证了所提CBF-QPs的有效性，相关仿真在pybullet模拟器中完成。此外，我们将所提方法与文献中已有CBF-QPs（特别是广泛使用的高阶CBF-QPs，HO-CBF-QPs）进行比较，结果表明后者相较于所提方法更为保守。该比较也在仿真中进行了详细展示。