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,特别是四旋翼无人机,凭借其机动性和多功能性,已经在各个行业引起了革命性的变化,但它们在动态环境中的安全运行完全依赖有效的避撞技术。本文介绍了一种新的技术,用于在避开动态障碍物的同时安全地沿着期望的路线导航四旋翼。所提出的方法采用控制边界函数,并利用碰撞锥确保四旋翼的速度和障碍物的速度始终指向彼此相反。特别地,我们提出了一种新的约束形式,确保四旋翼和障碍物之间的相对速度始终避开可能导致碰撞的向量锥。通过证明所提出的约束是四旋翼的有效控制边界函数(CBFs),我们能够利用其通过二次规划(QPs)的实时实现,被称为CBF-QPs。通过在多种情况下展示与移动障碍物的避撞,我们验证了所提出的CBF-QPs的有效性。此外,我们将所提出的方法与文献中提到的CBF-QPs进行了比较,特别是着名的高阶CBF-QPs(HO-CBF-QPs),我们展示了它相对保守,与所提出的方法相比。这种比较也在模拟中详细显示。