An LSTM‐based approach to precise landing of a UAV on a moving platform. Issue 1 (22nd April 2022)

• Record Type: Journal Article
• Authors: Luo, Wei; Ebel, Henrik; Eberhard, Peter
• Is Part Of: International journal of mechanical system dynamics. Volume 2:Issue 1(2022)
• Page Start: 99
• Page End: 107
• Publication Date: 2022-04-22
• Subjects: Kalman filter -- long short‐term memory (LSTM) -- trajectory prediction -- unmanned aerial vehicle (UAV); Mechanical engineering; Dynamics; Electronic journals; Periodicals; 620.104
• DOI: 10.1002/msd2.12036
• Languages: English
• ISSNs: 2767-1399

Abstract

Abstract: A machine learning‐based method for the precise landing of an unmanned aerial vehicle on a moving mobile platform is proposed. The proposed approach attempts to predict the mobile platform's future trajectory based on the past states of the mobile platform. To that end, it combines a long short‐term memory‐based neural network with a Kalman filter. Hence, it aims at combining the advantages of a machine learning method with those of a state estimation method from established control theory. Based on the predicted trajectory, the unmanned aerial vehicle attempts to land precisely on the moving mobile platform. The experiment is conducted in the Gazebo simulation platform with a quadrotor and an omnidirectional mobile robot, and the proposed method is compared with the single‐method approaches of using only either the Kalman filter or the machine learning method alone.