4.6 Article

Autonomous Quadcopter Precision Landing Onto a Heaving Platform: New Method and Experiment

期刊

IEEE ACCESS
卷 8, 期 -, 页码 167192-167202

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3022881

关键词

Robustness; Task analysis; Cameras; Marine vehicles; Vehicle dynamics; Compounds; Unmanned aerial vehicles; Autonomous landing; precision landing; moving target; quadcopter; heaving platform; ship deck; robust control; sliding mode control; disturbance observer

资金

  1. Ministry of Science and ICT (MSIT), South Korea, under the Information Technology Research Center (ITRC) support program [IITP-2020-2018-0-01423]
  2. Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2020R1A6A1A03038540]
  3. Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2018-0-01423-003] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

Nowadays, with the increasing popularity of quadcopter unmanned aerial vehicles in several real-world applications, achieving a fully autonomous quadcopter flight has become an imperative topic investigated in many studies. One of the most pressing issues in such a topic is the precision landing task, which always is devastatingly influenced by the ground effect and external disturbances. In this paper, we present an autonomous quadcopter landing algorithm allowing the vehicle to land robustly and precisely onto a heaving platform. Firstly, a robust control algorithm addressing the altitude flight under the ground effect and external disturbances is derived. We strictly prove the closed-loop system stability by using the Lyapunov theory. Secondly, a landing target state estimator is proposed to provide state estimations of the moving landing target. In addition, we propose a landing procedure to ensure the landing task is achieved safely and reliably. Finally, we use a DJI-F450 drone equipped with an infrared sensor and a laser ranging sensor as the experimental quadcopter platform and conduct experiments to evaluate the performance of our new algorithm in real flight conditions. The experimental results demonstrate the effectiveness of the proposed method.

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