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.

翻译：在城市环境中操作无人机通常意味着需要降落在屋顶上，而屋顶可能具有不同的几何形状和表面不规则性。使用传统传感方法（如基于视觉或声学的技术）准确检测屋顶倾角可能并不可靠，因为测量质量受天气条件和表面材料等外部因素的强烈影响。为克服这些挑战，我们提出了一种新颖的无人空中机械手构型，其特征是具有全向三维工作空间和扩展工作范围的双臂空中机械手。基于此设计，我们开发了一种基于动量观测器的本体感知接触检测与接触定位策略。这使得无人空中机械手能够在触地前，通过物理交互“盲估”倾斜表面的倾角。我们在飞行实验中验证了该方法，成功在倾角高达30.5度的表面上实现了鲁棒着陆，并在9次不同倾角的实验中实现了平均2.87度的表面倾角估计误差。