Omnidirectional Dual-Arm Aerial Manipulator with Proprioceptive Contact Localization for Landing on Slanted Roofs
Martijn B.J. Brummelhuis, Nathan F. Lepora, Salua Hamaza
AI summary
Problem
Conventional drones struggle to land safely on slanted urban roofs because exteroceptive sensors fail in poor conditions and standard platforms cannot align with inclined surfaces without risking hard impacts.
Approach
The team designed a dual-arm aerial manipulator with an omnidirectional workspace and developed a momentum-based torque observer that uses internal motor and IMU feedback to detect contact points and calculate surface inclination blindly before touchdown.
Key results
- Novel dual-arm aerial manipulator with a 3D omnidirectional workspace and low-inertia limbs
- Proprioceptive, sensorless contact detection and localization pipeline using a momentum-based torque observer
- Successful flight experiments demonstrating robust landings on surfaces with inclinations up to 30.5°
- Average surface inclination estimation error of 2.87° across nine flight tests
Why it matters
Enables reliable drone deployment in complex urban environments by eliminating reliance on fragile external sensors for rooftop landing operations.
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 ma- nipulator 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◦and achieving an average surface inclination estimation error of 2.87◦over 9 experiments at different incline angles.