Maritime Autonomous Surface Ships (MASS) have received increasing attention from industrial practitioners, researchers, and policymakers because of industry 4.0 and the digitization of the maritime industry. Crucial questions related to security, safety of personnel and vessels, and socio-economic domains have been addressed to a certain extent. In recent years, China has arisen as one of the leading maritime players worldwide, and unmanned vessels could remarkably influence the Chinese maritime industry. However, there is still a lack of systematic studies aiming to develop a deep understanding of potential advantages and challenges associated with the deployment of unmanned vessels in China. Therefore, using a mixed-method research design, this study attempts to obtain valuable insights based on the viewpoints of the key Chinese stakeholders concerning unmanned vessels, including the benefits, the restrictions, the obstacles to large-scale implementation, the risks, and how to mitigate possible implementation barriers. The main advantage of deploying unmanned ships was found to be the reduction in the ship crew size or complete elimination of the ship crew, which would reduce the operating costs and eliminate human errors on board the ships. Nevertheless, along with important advantages, a number of challenges associated with the development and deployment of unmanned ships were identified, including technological challenges, regulatory challenges, safety and security challenges, and technology investment challenges. All these challenges have to be adequately addressed by the relevant stakeholders to ensure the successful deployment of unmanned ships around the globe in the following years.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10106507 | PMC |
http://dx.doi.org/10.1016/j.heliyon.2023.e15182 | DOI Listing |
Sensors (Basel)
January 2025
College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China.
Aiming at the control challenges faced by unmanned surface vessels (USVs) in complex environments, such as nonlinearities, parameter uncertainties, and environmental perturbations, we propose a non-singular terminal integral sliding mode control strategy based on an extended state observer (ESO). The strategy first employs a third-order linear extended state observer to estimate the total disturbances of the USV system, encompassing both external disturbances and internal nonlinearities. Subsequently, a backstepping sliding mode controller based on the Lyapunov theory is designed to generate the steering torque control commands for the USV.
View Article and Find Full Text PDFSci Total Environ
January 2025
School of Engineering, Dali University, Yunnan 671003, China; National Observation and Research Station of Erhai Lake Ecosystem in Yunnan, Dali 671006, China.; Air-Space-Ground Integrated Intelligence and Big Data Application Engineering Research Center of Yunnan Provincial Department of Education, Yunnan 671003, China. Electronic address:
Sci Rep
December 2024
Navigation and Ship Engineering College, Dalian Ocean University, 116023, Dalian, China.
To improve the safety of ship navigation in complex sea areas and reduce planning time while achieving optimal path planning. The paper proposes an improved A* algorithm that incorporates ship collision risk assessment. The paper utilizes multi-scale raster maps to divide the sea chart in the context of complex sea areas, and combines the Line-of-sight (LOS) algorithm to solve the zigzag paths that may appear in this planning context.
View Article and Find Full Text PDFSensors (Basel)
October 2024
Marine Technology Ltd., 4/6 Roszczynialskiego St., 81-521 Gdynia, Poland.
IEEE Trans Neural Netw Learn Syst
October 2024
In this article, an optimal surrounding control algorithm is proposed for multiple unmanned surface vessels (USVs), in which actor-critic reinforcement learning (RL) is utilized to optimize the merging process. Specifically, the multiple-USV optimal surrounding control problem is first transformed into the Hamilton-Jacobi-Bellman (HJB) equation, which is difficult to solve due to its nonlinearity. An adaptive actor-critic RL control paradigm is then proposed to obtain the optimal surround strategy, wherein the Bellman residual error is utilized to construct the network update laws.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!