Omnidirectional Dual-Arm Aerial Manipulator with Proprioceptive Contact Localization for Landing on Slanted Roofs

2026-02-11

AI Summary

This work addresses the challenge of reliable slope estimation for drone landing on inclined rooftops in urban environments, where conventional vision- or acoustics-based methods are prone to interference from weather conditions and surface materials. To overcome this limitation, the authors propose a dual-arm aerial manipulator featuring an omnidirectional 3D workspace and extended reachability, integrated with a momentum-based torque observer to enable vision-free, proprioceptive contact detection and localization. This approach enables, for the first time, fully blind estimation of surface inclination and robust landing without external sensing. Flight experiments demonstrate that the system achieves stable landings on slopes up to 30.5°, with an average inclination estimation error of only 2.87° across nine trials at varying angles.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision
• Application Domains: Mobility, Driving & Flight

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• aerial manipulation
• slanted roof landing
• contact localization
• inclination estimation
• urban drone operation

Innovation

• omnidirectional dual-arm aerial manipulator
• proprioceptive contact localization
• momentum-based torque observer
• blind surface inclination estimation
• robust landing on slanted roofs