论文标题
预测性角度潜在的基于现场的障碍避免动态无人机飞行
Predictive Angular Potential Field-based Obstacle Avoidance for Dynamic UAV Flights
论文作者
论文摘要
近年来，无人驾驶汽车（UAV）用于大量检查和视频捕获任务。但是，在障碍附近手动控制无人机是具有挑战性的，并且构成了碰撞的高风险。即使对于自动飞行，全球导航计划也可能太慢，无法应对新近感知的障碍。诸如风之类的干扰可能会导致与计划中的轨迹偏离。 在这项工作中，我们提出了一种快速的预测障碍方法，该方法不依赖于更高级别的本地化或映射，并保持无人机的动态飞行功能。它直接在LIDAR范围内实时运行，并通过计算范围图像内的角电位字段来调整当前飞行方向。随后根据轨迹预测和接触时间估计来确定速度幅度。 使用硬件中的模拟评估我们的方法。它可以使无人机保持安全距离，同时允许比以前直接在传感器数据上运行的反应性障碍物方法更高的飞行速度。
In recent years, unmanned aerial vehicles (UAVs) are used for numerous inspection and video capture tasks. Manually controlling UAVs in the vicinity of obstacles is challenging, however, and poses a high risk of collisions. Even for autonomous flight, global navigation planning might be too slow to react to newly perceived obstacles. Disturbances such as wind might lead to deviations from the planned trajectories. In this work, we present a fast predictive obstacle avoidance method that does not depend on higher-level localization or mapping and maintains the dynamic flight capabilities of UAVs. It directly operates on LiDAR range images in real time and adjusts the current flight direction by computing angular potential fields within the range image. The velocity magnitude is subsequently determined based on a trajectory prediction and time-to-contact estimation. Our method is evaluated using Hardware-in-the-Loop simulations. It keeps the UAV at a safe distance to obstacles, while allowing higher flight velocities than previous reactive obstacle avoidance methods that directly operate on sensor data.
