Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

Ye, Jun; Roy, S.; Godjevac, Milinko; Reppa, Vasso; Baldi, Simone

doi:10.1109/jas.2021.1003913

Cited by 13 publications

(11 citation statements)

References 33 publications

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“…The development of science and technology has led to more and more control methods being applied to the field of AUVs, such as PID [25], S-plane [26], adaptive [27], sliding mode [28], and other controllers. The design of controllers for marine vessels or robots usually needs to consider their multiple constraint terms [29], suffered from uncertainty disturbances [30,31], as well as control stability, robustness, etc., to design a suitable controller by combining various factors.…”

Section: Related Workmentioning

confidence: 99%

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

Lyu,

Sun,

Wang

et al. 2023

JMSE

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This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-based end-to-end perception–planning–execution method to overcome the challenges associated with training and learning in end-to-end approaches that directly output control forces. In this approach, the state set is established based on the environment information, the action set is established based on the motion characteristics of the AUV, and the control execution force set is established based on the control constraints. The mapping relations between each set are trained using deep reinforcement learning, enabling the AUV to perform the corresponding action in the current state, thereby accomplishing tasks in an end-to-end manner. Furthermore, we introduce the hindsight experience replay (HER) method in the perception planning mapping process to enhance stability and sample efficiency during training. Finally, we conduct simulation experiments encompassing planning, execution, and end-to-end performance evaluation. Simulation training demonstrates that our proposed method exhibits improved decision-making capabilities and real-time obstacle avoidance during planning. Compared to global planning, the end-to-end algorithm comprehensively considers constraints in the AUV planning process, resulting in more realistic AUV actions that are gentler and more stable, leading to controlled tracking errors.

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Section: Related Workmentioning

confidence: 99%

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

Lyu,

Sun,

Wang

et al. 2023

JMSE

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“…For the hoisting crane with heavy parts at sea, its operation quality is directly related to the execution efficiency of related work. Because of the relatively heavy lifting weight, it is difficult to adjust the positioning manually [1][2] . In view of this, the positioning research based on heavy lifting crane has attracted more and more attention.…”

Section: Introductionmentioning

confidence: 99%

Research on automatic positioning of hoisting crane for heavy parts at sea based on BIM and RFID technology

Liu

2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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Due to the constraints of crane positioning, its behavior accuracy is relatively low. Therefore, the research on automatic positioning of crane for offshore heavy parts based on BIM and RFID technology is proposed. This paper makes a comprehensive analysis of the positioning constraints of offshore heavy lifting cranes from three perspectives: environmental constraints, operational constraints and safety constraints, and determines the corresponding control elements. In the positioning process, BIM and RFID technology are introduced, and the actual running state data of the hoisting crane is collected with the help of RFID technology. Based on the analysis results of the positioning constraint control elements of the hoisting crane with heavy offshore parts, the identification tag of RFID technology is set up. BIM model is used to decompose the scope of construction area and hoisting tasks in the early stage of hoisting, which is coupled with the data acquisition results of RFID technology to realize the location of specific positions. In the test results, the positioning error of the offshore heavy lifting crane in the X direction is stable within 0.4m, and the positioning error of the offshore heavy lifting crane in the Y direction is stable within 0.3m, which has high accuracy.

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“…The particular circumstances are that environmental disturbances can arise from winds, currents, or wave; and these can have typically unknown magnitudes at each instant of time, and it is also difficult to get accurate measurements. By robustifying the DP system, crane vessels can enhance their ability to withstand external disturbances, maintain precise positioning, and ensure the safety and success of heavy lifting operations 22 . For these problems, the methodology of disturbance observers have typically been proposed in marine applications to address these unknown disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…By robustifying the DP system, crane vessels can enhance their ability to withstand external disturbances, maintain precise positioning, and ensure the safety and success of heavy lifting operations. 22 For these problems, the methodology of disturbance observers have typically been proposed in marine applications to address these unknown disturbances. Related works on these are described in efforts such as the heave stabilization of surface effect ships, 23 trajectory tracking for surface vehicles, 24 and dynamic localization of ships 25 etc.…”

Section: Introductionmentioning

confidence: 99%

Synchronized tracking control for dynamic positioning vessel

Liang,

Zhang,

et al. 2023

Intl J Robust & Nonlinear

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This article addresses the time‐synchronized control problem for a floating liquefied natural gas (FLNG) vessel and a liquefied natural gas carrier (LNGC) in side‐by‐side offloading operation. For such a problem, the existing finite‐time control approaches available are essentially only designed to ensure that the system state‐variables converge within a certain time. Specifically how each state‐variable converges is typically not considered in the design process. Along this line, the approach of time‐synchronized design in this paper attains the more comprehensive and desirable outcome of having the marine vessels' state‐variables arrive at the sliding mode surface in a fixed time. Also additionally, the state‐variables converge at the same moment. This approach thus can attain synchronous tracking of the LNGC to the FLNG vessel. All these desirable properties and outcomes of control system convergence are rigorously proved in the work here via the methods of Lyapunov‐type theorems and associated stability analysis. Furthermore, in order to ensure that the approaches developed here are suitably applicable in real‐world engineering conditions (with system uncertainties and external disturbances), robustness extensions are also incorporated. In the presented work, the analytical issues and the methodologies in the designed control system solution under the conditions of the disturbed systems are discussed in detail. The numerical simulations conducted verify the merits of the proposed algorithm. Comparative numerical simulations on the developed scheme, and on traditional control, are carried out to demonstrate the important and improved outcomes attained with the time‐synchronized features.

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Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

Cited by 13 publications

References 33 publications

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

Research on automatic positioning of hoisting crane for heavy parts at sea based on BIM and RFID technology

Synchronized tracking control for dynamic positioning vessel

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