Earticle

초록

영어

This paper presents the design and experimental validation of an intelligent tire alignment and lifting control system for an under-vehicle autonomous parking robot. The proposed system enables the robot to autonomously enter beneath a vehicle, recognize tire positions using a LiDAR-based sensing module, and perform precise lifting through a fork-type mechanism. A YOLOv8 instance segmentation algorithm is employed to detect tire regions from LiDAR point cloud data and estimate their geometric centers. The detected tire positions are then matched with a vehicle database to determine the correct alignment for lifting. Experiments were conducted on three different vehicle types under various surface conditions. The results show that the proposed system achieved a tire recognition accuracy exceeding 95%, a lifting success rate of 100%, and an average lifting operation time of 12.3 seconds. These results demonstrate the reliability and practicality of the proposed method for real-world autonomous parking applications.

목차

Abstract
1. 서론
2. 시스템 구성
2.1 전체 시스템
2.2 구동부 및 리프팅 메커니즘
2.3 제어부 구성
2.4 인식부 구성
3. 라이다 기반 타이어 인식 알고리즘
3.1 포인트 클라우드 데이터 전처리
3.2 YOLOv8 기반 타이어 영역 분할 및 중심 추정
3.3 차량 데이터베이스 매칭 및 타이어 위치 구분
4. 라이다 기반 타이어 정렬 및 리프팅 제어알고리즘
4.1 차량 하부 진입 경로 및 정렬 알고리즘
4.2 리프팅 동작 제어 시퀀스
5. 실험 및 결과 분석
5.1 인공지능 학습 데이터 구축 및 학습
5.2 다양한 차종 및 주차 조건에서의 인식 성능평가
5.3 로봇 진입 차량 리프팅 제어 결과
6. 결론 및 향후 과제
References

키워드

저자

• 최승준 [ Seung Jun Choi | Halla University, Dept. of Future Mobility Engineering ]
• 고국원 [ Kuk Won Ko | Member, Dept. of Future Mobility Engineering ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620