지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Vol.41 No.11
- 발행연도
- 2024.11
- 수록면
- 841 - 847 (7page)
- DOI
- 10.7736/JKSPE.024.068
이용수
초록· 키워드
오류제보하기
Excavators are crucial heavy equipment on construction sites, performing diverse earthwork tasks. The construction worksite is experiencing a labor shortage due to an aging workforce. Training new operators requires significant time and resources. Furthermore, the construction environment is hazardous, with a higher rate of excavator-related accidents. Autonomous excavators offer an effective solution by reducing the need for operators in risky environments and substituting skilled workers. Trajectory planning algorithms are vital for autonomous excavators, with skilled operators’ paths serving as important references. However, many studies do not adequately consider skilled operators’ methods or the actual excavation environment. This paper introduced a rule-based algorithm for excavation trajectory planning using terrain data. Based on analysis results of skilled operators’ paths, the proposed algorithm categorizes the excavation process into three stages, depending on the usage rate of the excavator"s joints. Terrain data were derived by projecting point clouds from a stereo depth camera onto a side plane. The path was modified if the excavation volume exceeded a set limit to avoid excessive load. The algorithm was tested with a 30-ton excavator, demonstrating validation of operability and efficiency similar to that of skilled operators.
목차
1. 서론
2. 굴착 경로 생성 알고리즘
3. 실험
4. 실험 결과
5. 결론
REFERENCES
참고문헌 (12)
참고문헌 신청
Yao, Z. W., Zhao, S. C., Tan, X. D., Wei, W., Wang, Y., (2023), Real-time task-oriented continuous digging trajectory planning for excavator arms, Automation in Construction, 152, 104916.
Brown, S., Harris, W., Brooks, R. D., Dong, X. S., (2021), Fatal and nonfatal struck-by injuries in the construction industry, 2011-2019, CPWR - The Center for Construction Research and Training; National Institute for Occupational Safety and Health.
Yuan, C., Li, S., Cai, H., (2017), Vision-based excavator detection and tracking using hybrid kinematic shapes and key nodes, Journal of Computing in Civil Engineering, 31(1), 04016038.
Feng, C., Shen, Y., Wang, J., Wang, Q., Suo, Z., Su, F., (2024), Task-unit based trajectory generation for excavators utilizing expert operator skills, Automation in Construction, 158, 105247.
Yang, Y., Pan, J., Long, P., Song, X., Zhang, L., (2020), Time variable minimum torque trajectory optimization for autonomous excavator. https://doi.org/10.48550/arXiv.2006
Brown, S., Harris, W., Brooks, R. D., Dong, X. S., (2021), Fatal and nonfatal struck-by injuries in the construction industry, 2011-2019, CPWR - The Center for Construction Research and Training; National Institute for Occupational Safety and Health.
Yuan, C., Li, S., Cai, H., (2017), Vision-based excavator detection and tracking using hybrid kinematic shapes and key nodes, Journal of Computing in Civil Engineering, 31(1), 04016038.
Feng, C., Shen, Y., Wang, J., Wang, Q., Suo, Z., Su, F., (2024), Task-unit based trajectory generation for excavators utilizing expert operator skills, Automation in Construction, 158, 105247.
Yang, Y., Pan, J., Long, P., Song, X., Zhang, L., (2020), Time variable minimum torque trajectory optimization for autonomous excavator. https://doi.org/10.48550/arXiv.2006
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