Conference paper Open Access

Experimental Investigation of Soft-Landing of Quadrotors via Induced Wind Modeling Approach

Saeed Rafee Nekoo; Pedro J. Sanchez Cuevas; José Ángel Acosta; Guillermo Heredia; Anibal Ollero

This paper presents an experimental study of the soft-landing problem in quadrotors using the induced wind modeling approach. The landing phase has been typically one of the critical phases in drone flight. Landing complexity drastically increases when the drone needs to land on sensitive sites, such as platforms or rack of pipes in refineries (for inspection purposes), in which explosive material is running through or there exist flammable/explosive material in the environment. Multirotor unmanned aerial vehicles (UAVs) are usually lightweight platforms and they are significantly disturbed by the aerodynamic ground effect while landing; so, near the ground, those drones are subjected to an external disturbance in proximity to the ground. In this situation, the airflow can be reflected after reaching the ground, disturbing the performance of the rotors significantly. This paper aims to model the induced wind velocity, caused by the propellers to see and consider the ground effect during the landing. The reduction of the total thrust near the ground provides a smooth landing and avoids bumping. The complex wind modeling formulation and limitation of the commercialized autopilots make the implementation a challenging task. Herein we propose how to incorporate the proposed soft-landing algorithm within an existing UAV autopilot. Experimental results show that the proposed approach successfully replicates the wind modeling which leads to a soft-landing.

This work is supported by the HYFLIERS project (HYbrid FLying-rolling with-snakE-aRm robot for contact inspection) funded by the European Commission H2020 Programme under grant agreement ID: 779411 (https://cordis.europa.eu/project/rcn/213049)] and by the ARTIC project funded by the Ministerio de Ciencia, Innovación y Universidades under the grant agreement ID: RTI2018-102224-B-I00.
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