Abstract
Operating drones in urban environments often means they need to land on rooftops, which can have different geometries and surface irregularities. Accurately detecting roof inclination using conventional sensing methods, such as vision-based or acoustic techniques, can be unreliable, as measurement quality is strongly influenced by external factors including weather conditions and surface materials. To overcome these challenges, we propose a novel unmanned aerial manipulator morphology featuring a dual-arm aerial manipulator with an omnidirectional 3D workspace and extended reach. Building on this design, we develop a proprioceptive contact detection and contact localization strategy based on a momentum-based torque observer. This enables the UAM to infer the inclination of slanted surfaces blindly - through physical interaction - prior to touchdown. We validate the approach in flight experiments, demonstrating robust landings on surfaces with inclinations of up to 30.5 degrees and achieving an average surface inclination estimation error of 2.87 degrees over 9 experiments at different incline angles.
Abstract (translated)
在城市环境中操作无人机通常意味着它们需要降落在具有不同几何形状和表面不规则性的屋顶上。使用传统的传感方法，如基于视觉或声学的方法来准确检测屋顶的倾斜角度可能不可靠，因为测量质量会受到外部因素（包括天气条件和表面材料）的影响。 为了解决这些问题，我们提出了一种新颖的无人航空机械手形态设计，该设计采用双臂空中机械手，并具有全方位3D工作空间和扩展的工作范围。在此基础上，我们开发了一种基于动量扭矩观察器的本体感受接触检测与定位策略。这种方法使UAM（Unmanned Aerial Manipulator）能够在不依赖视觉信息的情况下通过物理互动推断出倾斜表面的角度，在触地之前进行准确判断。 我们在飞行实验中验证了这一方法的有效性，展示了在不同倾斜角度下（最多30.5度），平均表面倾斜角估计误差仅为2.87度的良好性能。
URL
https://arxiv.org/abs/2602.10703
