[논문]물류 이송을 위한 딥러닝 기반 특정 사람 추종 모빌리티 제어 연구

유영준; 강성훈; 김주환; 노성인; 이기현; 이승용; 이철희

doi:10.7839/ksfc.2023.20.4.001

물류 이송을 위한 딥러닝 기반 특정 사람 추종 모빌리티 제어 연구
Study of Deep Learning Based Specific Person Following Mobility Control for Logistics Transportation 원문보기

드라이브ㆍ컨트롤 = Journal of drive and control, v.20 no.4, 2023년, pp.1 - 8

유영준 (Department of Construction Machinery Engineering, Inha University) , 강성훈 (Department of Mechanical Engineering, Inha University) , 김주환 (Department of Mechanical Engineering, Inha University) , 노성인 (Department of Mechanical Engineering, Inha University) , 이기현 (Department of Mechanical Engineering, Inha University) , 이승용 (Department of Mechanical Engineering, Inha University) , 이철희 (Department of Construction Machinery Engineering, Inha University)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (10)

그림 Fig. 1 Mobility to follow a specific person
그림 Fig. 2 Results of people detection
그림 Fig. 3 Results of measured a person distance
그림 Fig. 4 Flow chart of detect specific person
그림 Fig. 5 Simulation results of PID control to follow a specific person
그림 Fig. 6 Result of generated 3D point map and 2D grid map
그림 Fig. 7 Method of SLAM and person following
그림 Fig. 8 Results of mobility and person paths to follow a specific person
그림 Fig. 9 Experiment with a mobility that follows a specific person
표 Table 1 Root mean squared errors of drive path

참고문헌 (17)

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