Landing is the most challenging and risky aspect of multirotor drone flight, and only simple landing methods exist for autonomous drones. We explore methods for autonomous drone landing in two scenarios. In the first scenario, we examine methods for landing on known landing pads using fiducial markers and a gimbal-mounted monocular camera. This method has potential in drone applications where a drone must land more accurately than GPS can provide (e.g.~package delivery in an urban canyon). We expand on previous methods by actuating the drone's camera to track the marker over time, and we address the complexities of pose estimation caused by fiducial marker orientation ambiguity. In the second scenario, and in collaboration with the RAVEN project, we explore methods for landing on solidified lava flows in Iceland, which serves as an analog environment for Mars and provides insight into the effectiveness of drone-rover exploration teams. Our drone uses a depth camera to visualize the terrain, and we are developing methods to analyze the terrain data for viable landing sites in real time with minimal sensors and external infrastructure requirements, so that the solution does not heavily influence the drone's behavior, mission structure, or operational environments.
翻译:着陆是多机器人无人机飞行中最具挑战性和风险的方面,只有简易的着陆方法存在自主无人机。 我们探索了两种情景下的自动无人机着陆方法。 在第二种情景中,我们研究使用浮标和金字塔上单望远镜在已知着陆场上着陆的方法。 这种方法在无人机应用中具有潜力,无人机必须比全球定位系统更精确地着陆(例如,在城市峡谷内运送垃圾袋) 。 我们扩大了以往的方法,通过操作无人机的相机来跟踪标记时间,我们解决了由浮标方向模糊性导致的构成估计的复杂性。 在第二种情景中,我们与RAVEN项目合作,我们探索了在冰岛的固化熔岩流上着陆的方法,作为火星的模拟环境,并深入了解无人机探测器勘探队的有效性。 我们的无人机使用深度相机来对地形进行视觉化,我们正在开发方法来分析实时可行着陆点的地形数据,同时有最低的传感器和外部基础设施要求,因此解决方案不会严重影响无人机的行为、任务结构或操作环境。