A survey of motion control for marine vehicles

Qi, Ai-Chun; Han, Qing‐Long; Wang, Yulong

doi:10.1109/chicc.2015.7260289

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…The research starts with a critical review of key surveys related to USVs and also DM [22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41], most focusing only on USVs, where a few vaguely focus on USVs for DM.…”

Section: Groundworkmentioning

confidence: 99%

“…Ashrafiuon et al [26] present a review of non-linear tracking and setpoint control for USVs. Qi et al [32] present a survey on motion control for USVs and UUVs considering different types of propellers. Azzeri et al [33] review course keeping control systems for USVs.…”

Section: Groundworkmentioning

confidence: 99%

“…There are no surveys dedicated to USVs where the focus is on DM, demonstrating the novelty of this paper. This chart is organized in six categories:Disaster management [7,18,19,21,49,50,51]General surveys regarding USVs and unmanned systems [23,24,25,28,31,48]Guidance, Navigation and Control [22,26,29,32,33,35,36,39,40,47]Higher-level autonomy [37,38,46]Structure inspection [41,45]Environmental monitoring [30,42,43,44]…”

Section: Groundworkmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Jorge

Granada

Maidana

et al. 2019

Sensors

142

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Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle—usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps.

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Section: Groundworkmentioning

confidence: 99%

Section: Groundworkmentioning

confidence: 99%

Section: Groundworkmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Jorge

Granada

Maidana

et al. 2019

Sensors

142

View full text Add to dashboard Cite

show abstract

“…Therefore, prior to the deployment of fully functional autonomous vessels, concerted efforts must be directed towards developing intelligent control systems. Although numerous motion control systems have been documented in the literature [5], substantial work remains to bridge the gap between the predicted behavior and the actual responses of vessels to their surroundings [6].…”

Section: Introductionmentioning

confidence: 99%

Shallow and Deep Learning Models for Vessel Motions Forecasting during Adverse Weather Conditions

M. Walker,

Coraddu,

Savio

et al. 2023

Moses

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Accurately forecasting vessel motions is a critical step towards achieving fast and accurate intelligent vessel control systems. Intelligent vessel control relies on accurate predictions of vessel motion to make informed decisions regarding control, maneuvering, and positioning, particularly during times of exogenous loading caused by adverse weather conditions. Hence, by accurately forecasting vessel motion accurately, the control system can anticipate potential issues (i.e., excessive trim or roll) and prescribe corrective actions before they become problematic. In this study, the authors propose two approaches to address the problem of vessel motion forecasting. The first approach relies on classical shallow learning models, whereas the second approach involves the use of state-of-the-art deep learning models for improved accuracy at further forecast horizons. Unlike shallow models, deep models can learn the required features directly from the data and therefore do not require a priori knowledge or additional features engineering. By leveraging deep learning models, the authors show that vessel motions can be forecasted further into the future without a significant loss in accuracy, thereby improving the overall effectiveness of the intelligent vessel control system. To support their statements, the authors use real operational data and compare the performance of the shallow and deep learning models. The results show that deep learning outperforms shallow learning models in terms of accuracy without a significant increase in the computational demand. Additionally, the authors demonstrate that their models remain accurate even under adverse weather conditions, indicating that they have practical applicability for vessel motions forecasting and can potentially improve the overall effectiveness of intelligent vessel control systems.

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Guidance and control methodologies for marine vehicles: A survey

Karimi

2021

Control Engineering Practice

111

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A survey of motion control for marine vehicles

Cited by 4 publications

References 23 publications

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Shallow and Deep Learning Models for Vessel Motions Forecasting during Adverse Weather Conditions

Guidance and control methodologies for marine vehicles: A survey

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