A neural network based landing method for an unmanned aerial vehicle with soft landing gears

Tools

Luo, Cai and Zhao, Weikang and Du, Zhenpeng and Yu, Leijian (2019) A neural network based landing method for an unmanned aerial vehicle with soft landing gears. Applied Sciences, 9 (15). 2976. ISSN 2076-3417 (https://doi.org/10.3390/app9152976)

[thumbnail of Luo-etal-AS-2019-A-neural-network-based-landing-method-for-an-unmanned-aerial-vehicle]
Preview
 Text. Filename: Luo_etal_AS_2019_A_neural_network_based_landing_method_for_an_unmanned_aerial_vehicle.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (7MB)| Preview

Abstract

This paper presents the design, implementation, and testing of a soft landing gear together with a neural network-based control method for replicating avian landing behavior on non-flat surfaces. With full consideration of unmanned aerial vehicles and landing gear requirements, a quadrotor helicopter, comprised of one flying unit and one landing assistance unit, is employed. Considering the touchdown speed and posture, a novel design of a soft mechanism for non-flat surfaces is proposed, in order to absorb the remaining landing impact. The framework of the control strategy is designed based on a derived dynamic model. A neural network-based backstepping controller is applied to achieve the desired trajectory. The simulation and outdoor testing results attest to the effectiveness and reliability of the proposed control method.

CORE (COnnecting REpositories)