Autonomous decision-making scheme for multi-ship collision avoidance with iterative observation and inference

Wang, Tengfei; Wu, Qing; Zhang, Jinfen; Wu, Bing; Wang, Yang

doi:10.1016/j.oceaneng.2019.106873

Cited by 64 publications

(23 citation statements)

References 37 publications

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“…(2) Compared with the normal situation, when the communication fails, the unmanned ship needs to predict the collision avoidance intention of the target ship more and predict the content of the decision. Although there are special countermeasures in previous studies [27], this is still not easy. Therefore, the software system uses a different combination of CPTs from the normal situation when dealing with PE 8.…”

Section: Sense Of Responsibilitymentioning

confidence: 96%

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A Comparative Assessment of Collision Risk of Manned and Unmanned Vessels

Wang

Diaconeasa

et al. 2020

JMSE

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It is expected that the prototypes of unmanned merchant ships will be deployed in the next few years. However, there is no specific research on whether the introduction of unmanned ships will reduce the risk of ship collision accidents in which communication between vessels is critical. This work constitutes an attempt to bridge the gap identified above by applying the Hybrid Causal Logic (HCL) methodology to model general-level collision scenarios of unmanned ships. The HCL methodology has been selected for its proven applicability to risk assessments, even when empirical data may be insufficient. Collision scenarios involving unmanned ships have been created in which manned ships of the conventional collision scenario HCL model are replaced with unmanned ships. Then, collision scenarios capturing the interactions between a manned ship and an unmanned ship were modeled. By comparing the qualitative and quantitative results of the different scenarios, we can see that the introduction of unmanned ships may effectively reduce the occurrence of ship collision accidents.

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Section: Sense Of Responsibilitymentioning

confidence: 96%

“…Out of all the global maritime accidents, the ship collision accident constituted as much as 36% of the total amount [27]. According to the investigation, most of the accidents were navigation-related human errors [28].…”

Section: Overview Of the Hcl Model For Ship Collision Risk Analyses Of The M-m Scenariomentioning

confidence: 99%

A Comparative Assessment of Collision Risk of Manned and Unmanned Vessels

Wang

Diaconeasa

et al. 2020

JMSE

Self Cite

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“…is paper comprehensively refers to the selection of the number of neurons in literature [15] and literature [16] and finally determines the number of neurons in this paper. e DDPG algorithm predicts the best execution action in the current state according to formula (2), where μ is the hidden layer network parameter and π is the learned strategy. At the same time, in the last layer of the network, the Tanh activation function is used to limit the network single output action value between [−1, 1], and finally, the network output action is converted into action in the continuous action range.…”

Section: Improvement Of Ddpg Algorithmmentioning

confidence: 99%

“…With the development of economic globalization, trade between countries is getting closer. Ships have been an important means of transportation in international trade and national transportation due to their characteristics of large transportation capacity, low energy consumption, small transportation cost, and green environment protection, which has a pivotal position in economic development [1,2]. e development of economy has put forward requirements for maritime intelligent transportation, and ship automation is the most basic and urgent part of the solution.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP

Zhang

Cao

et al. 2021

Journal of Advanced Transportation

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Deep Reinforcement Learning (DRL) is widely used in path planning with its powerful neural network fitting ability and learning ability. However, existing DRL-based methods use discrete action space and do not consider the impact of historical state information, resulting in the algorithm not being able to learn the optimal strategy to plan the path, and the planned path has arcs or too many corners, which does not meet the actual sailing requirements of the ship. In this paper, an optimized path planning method for coastal ships based on improved Deep Deterministic Policy Gradient (DDPG) and Douglas–Peucker (DP) algorithm is proposed. Firstly, Long Short-Term Memory (LSTM) is used to improve the network structure of DDPG, which uses the historical state information to approximate the current environmental state information, so that the predicted action is more accurate. On the other hand, the traditional reward function of DDPG may lead to low learning efficiency and convergence speed of the model. Hence, this paper improves the reward principle of traditional DDPG through the mainline reward function and auxiliary reward function, which not only helps to plan a better path for ship but also improves the convergence speed of the model. Secondly, aiming at the problem that too many turning points exist in the above-planned path which may increase the navigation risk, an improved DP algorithm is proposed to further optimize the planned path to make the final path more safe and economical. Finally, simulation experiments are carried out to verify the proposed method from the aspects of plan planning effect and convergence trend. Results show that the proposed method can plan safe and economic navigation paths and has good stability and convergence.

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“…In the concrete dam deformation monitoring system, uncertainty arises since the interaction among the dam body, the dam foundation, and the monitoring system require different monitoring methods. The instrument precision variance, instrument performance degradation, and other factors also increase the uncertainty of the measured monitoring values [26,27]. In addition, the process of both manual and automatic monitoring can introduce errors and noises, which contributes to another source of uncertainty in monitoring data.…”

Section: Uncertaintymentioning

confidence: 99%

A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces

Wang

Zhu

et al. 2021

Applied Sciences

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A concrete dam is an important water-retaining hydraulic structure that stops or restricts the flow of water or underground streams. It can be regarded as a constantly changing complex system. The deformation of a concrete dam can reflect its operation behaviors most directly among all the effect quantities. However, due to the change of the external environment, the failure of monitoring instruments, and the existence of human errors, the obtained deformation monitoring data usually miss pieces, and sometimes the missing pieces are so critical that the remaining data fail to fully reflect the actual deformation patterns. In this paper, the composition, characteristics, and contamination of the concrete dam deformation monitoring information are analyzed. From the single-value missing data completion method based on the nonlocal average method, a multi-value missing data completion method using BP (back propagation) mapping of spatial adjacent points is proposed to improve the accuracy of analysis and pattern prediction of concrete dam deformation behaviors. A case study is performed to validate the proposed method.

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Autonomous decision-making scheme for multi-ship collision avoidance with iterative observation and inference

Cited by 64 publications

References 37 publications

A Comparative Assessment of Collision Risk of Manned and Unmanned Vessels

A Comparative Assessment of Collision Risk of Manned and Unmanned Vessels

An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP

A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces

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