Visual-based Landing Guidance System of UAV with Deep Learning Technique for Environments of Visual-detection Impairment

• Published in: International journal of control, automation, and systems Vol. 20; no. 5; pp. 1735 - 1744
• Main Authors: Lee, Minjae, Shin, Sung Gyun, Jang, Seungsoo, Cho, Woosung, Kim, Sungkyum, Han, Sangsoo, Choi, Chanho, Kim, Jooyeon, Kim, Youngmin, Kim, Song Hyun
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.05.2022; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Algorithms; Control; Control algorithms; Control theory; Deep learning; Engineering; Landing; Machine learning; Mechatronics; Regular Papers; Robotics; Unmanned aerial vehicles; 제어계측공학; Deep learning; vision-impairment environments; UAV; multirotor; vision-based landing guidance; landing mark detection
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-020-0586-3

Most vision-based landing algorithms cannot be applied in a severe vision-detection environment. However, the application of a deep learning technique to vision-based landing algorithm can solve the problem of a severe vision-detection environment, especially in vision-impaired environments. Based on this fact, a novel-landing concept with deep learning technique is proposed in this study. Three main techniques applied for guided landing are 1) deep learning for accurate landing mark detection; 2) location-memorized system for coping temporal failure of the landing mark detection; 3) Unmanned Aerial Vehicle control algorithm for vibration minimization in vision sensor. The proposed system successfully and accurately guided the multicopter onto the landing area without failure in vision-impaired environments. The results show that the proposed landing algorithm can overcome environmental restrictions in operating multicopters.