Path-Planning Method Based on Reinforcement Learning for Cooperative Two-Crane Lift Considering Load Constraint

被引：0
|
作者：
An, Jianqi [1 ,2 ,3 ]
Ou, Huimin [1 ,2 ,3 ]
Wu, Min [1 ,2 ,3 ]
Chen, Xin [1 ,2 ,3 ]
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Peoples R China
[2] Hubei Key Lab Adv Control & Intelligent Automat Co, Wuhan 430074, Peoples R China
[3] Minist Educ, Engn Res Ctr Intelligent Technol Geoexplorat, Wuhan 430074, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2025年 / 55卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Cranes; Load modeling; Three-dimensional displays; Heuristic algorithms; Vehicle dynamics; Payloads; Gravity; Planning; Path planning; Mathematical models; Cooperative two-crane lift; lift-path planning; load distribution; Q-learning; reinforcement learning; ENVIRONMENTS; ROBOT;
D O I：
10.1109/TSMC.2025.3539318
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In a two-crane cooperative lift process, unreasonable load distribution on the two cranes may cause one of the cranes to overload, which may cause a dangerous overturn accident. Therefore, the load distribution should be taken as a constraint to yield a safe path for a cooperative lift. Besides, the load distribution on the two cranes varies with the changing postures of the cranes. However, the explicit relationship between the load distribution and the postures has not been reported. Therefore, this article first presents a relationship model between the postures of the two cranes and the load distribution on them. Next, a new path-planning method based on reinforcement learning is explained, which utilizes the load constraint as the optimization object in the cooperative two-crane lift. Simulation results show that the new method yields a short lift path with reasonable load distribution.
引用
收藏
页码：2913 / 2923
页数：11
相关论文
共 37 条
  • [31] A Reinforcement Learning-based Path Planning Method for Complex Thin-walled Structures in 3D Printing
    Ge, Jingyi
    Wang, Yi
    Li, Jiayi
    Bai, Huiwen
    Liu, Linsheng
    Wang, Shengfa
    Xue, Xinwei
    Li, Fengqi
    2021 5TH INTERNATIONAL CONFERENCE ON INNOVATION IN ARTIFICIAL INTELLIGENCE (ICIAI 2021), 2021, : 234 - 240
  • [32] A reinforcement learning-based evolutionary algorithm for the unmanned aerial vehicles maritime search and rescue path planning problem considering multiple rescue centers
    Zhan, Haowen
    Zhang, Yue
    Huang, Jingbo
    Song, Yanjie
    Xing, Lining
    Wu, Jie
    Gao, Zengyun
    MEMETIC COMPUTING, 2024, 16 (03) : 373 - 386
  • [33] Deep Policy-Gradient Based Path Planning and Reinforcement Cooperative Q-Learning Behavior of Multi-Vehicle Systems
    Afifi, Ahmed M.
    Alhosainy, Omar H.
    Elias, Catherine M.
    Shehata, Omar M.
    Morgan, Elsayed I.
    2019 IEEE INTERNATIONAL CONFERENCE OF VEHICULAR ELECTRONICS AND SAFETY (ICVES 19), 2019,
  • [34] RETRACTED: Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots (Retracted Article)
    Zhang, Weimin
    Wang, Guoyong
    JOURNAL OF ROBOTICS, 2022, 2022
  • [35] Reinforcement learning-based route planning method for connected vehicles considering real-time road traffic information
    Fu, Zhumu
    Li, Luyan
    Zhu, Longlong
    Tao, Fazhan
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2024,
  • [36] Deep reinforcement learning-based path planning method for underwater gliders in unknown 3D marine environment
    Jiang, Nan
    Zhao, Qinghai
    Wang, Jirong
    SHIPS AND OFFSHORE STRUCTURES, 2025, 20 (01) : 121 - 132
  • [37] 3D UAV path planning in unknown environment: A transfer reinforcement learning method based on low-rank adaption
    Bo, Lan
    Zhang, Tiezhu
    Zhang, Hongxin
    Hong, Jichao
    Liu, Mingjie
    Zhang, Caihong
    Liu, Benyou
    ADVANCED ENGINEERING INFORMATICS, 2024, 62
1234