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

页码：2913 / 2923

页数：11

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