Journal of Drive and Control (드라이브 ㆍ 컨트롤)

The Korean Society for Fluid Power and Construction Equipment (유공압건설기계학회)
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Study of Deep Learning Based Specific Person Following Mobility Control for Logistics Transportation

물류 이송을 위한 딥러닝 기반 특정 사람 추종 모빌리티 제어 연구

  • Yeong Jun Yu (Department of Construction Machinery Engineering, Inha University) ;
  • SeongHoon Kang (Department of Mechanical Engineering, Inha University) ;
  • JuHwan Kim (Department of Mechanical Engineering, Inha University) ;
  • SeongIn No (Department of Mechanical Engineering, Inha University) ;
  • GiHyeon Lee (Department of Mechanical Engineering, Inha University) ;
  • Seung Yong Lee (Department of Mechanical Engineering, Inha University) ;
  • Chul-hee Lee (Department of Construction Machinery Engineering, Inha University)
  • 유영준 ;
  • 강성훈 ;
  • 김주환 ;
  • 노성인 ;
  • 이기현 ;
  • 이승용 ;
  • 이철희
  • Received : 2023.07.28
  • Accepted : 2023.10.04
  • Published : 2023.12.01
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Abstract

In recent years, robots have been utilized in various industries to reduce workload and enhance work efficiency. The following mobility offers users convenience by autonomously tracking specific locations and targets without the need for additional equipment such as forklifts or carts. In this paper, deep learning techniques were employed to recognize individuals and assign each of them a unique identifier to enable the recognition of a specific person even among multiple individuals. To achieve this, the distance and angle between the robot and the targeted individual are transmitted to respective controllers. Furthermore, this study explored the control methodology for mobility that tracks a specific person, utilizing Simultaneous Localization and Mapping (SLAM) and Proportional-Integral-Derivative (PID) control techniques. In the PID control method, a genetic algorithm is employed to extract the optimal gain value, subsequently evaluating PID performance through simulation. The SLAM method involves generating a map by synchronizing data from a 2D LiDAR and a depth camera using Real-Time Appearance-Based Mapping (RTAB-MAP). Experiments are conducted to compare and analyze the performance of the two control methods, visualizing the paths of both the human and the following mobility.

Keywords

Acknowledgement

이 논문은 행정안전부 자연재난 정책연계형 기술 개발 사업의 지원을 받아 수행된 연구임(2022-MOIS35-005)

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