Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software development, especially in aging but still functional fleets, is described as being in a very early and emerging phase. This presents great challenges and opportunities for researchers and engineers to develop maritime autonomous systems. Recent progress in sensor and communication technology has introduced the use of autonomous surface vehicles (ASVs) in applications such as coastline surveillance, oceanographic observation, multi-vehicle cooperation, and search and rescue missions. Advanced artificial intelligence technology, especially deep learning (DL) methods that conduct nonlinear mapping with self-learning representations, has brought the concept of full autonomy one step closer to reality. This article reviews existing work on the implementation of DL methods in fields related to ASV. First, the scope of this work is described after reviewing surveys on ASV developments and technologies, which draws attention to the research gap between DL and maritime operations. Then, DL-based navigation, guidance, control (NGC) systems and cooperative operations are presented. Finally, this survey is completed by highlighting current challenges and future research directions. 

Survey of Deep Learning for Autonomous Surface Vehicles in Marine Environments

With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development.

/pdf/survey-on-the-developments-of-unmanned-marine-vehicles-1ndmlind.pdf

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

A constrained locking sweeping method and velocity obstacle based path planning algorithm for unmanned surface vehicles in complex maritime traffic scenarios

Abstract The three research topics, ship collision risk assessment, ship traffic hotspot detection and prediction, and collision-avoidance based ship path planning, are vital for next-generation vessel traffic management and monitoring systems. The system development is closely related to big data analytics and artificial intelligence for restricted waters. This study, therefore, aims to analyse the state-of-the art of these three topics over the latest decade, identify research gaps, and shed light on future research avenues. To achieve these three objectives, we critically and systematically review related articles that were published during the period between 2011 and 2021. We believe that this comprehensive and critical literature review would have a significant and profound impact on the formal safety assessment and vessel traffic management, and monitoring studies because it is not only an extension but also an essential continuity work of the literature review on maritime waterway risk assessment and prediction, as well as ship path guidance for ship collision risk mitigation in accordance with current automation vessels development and modern intelligent port construction. 

A review of ship collision risk assessment, hotspot detection and path planning for maritime traffic control in restricted waters

Mobile crowd sensing (MCS) is a popular sensing paradigm that recruits participants carrying sensing devices to collect data for specific tasks in a sensing area. In an MCS system, when a participant executes limited multiple location-based tasks according to different task accessing sequences, its executing time which depends on its travelling path is different. Therefore, how to perform an efficient path planning to find the optimal executing time for each participant is of vital importance. In this paper, we revisit the path planning problem in MCS and propose a novel path planning method based on the beetle swarm optimization (BSO) algorithm. First, a participant executing time is defined as the sum of the moving time along its travelling path and the sensing time for executing all tasks on this path. Then, to minimize it, an improved BSO algorithm is utilized to find the optimal path for each participant. In particular, a largest-order-value (LOV) rule is introduced to convert each position of beetles from a multi-dimensional vector into a travelling path for the participant. The simulation results verify that the proposed method is superior to other baselines in terms of the participant executing time and shows a faster convergence speed. 

Revisiting Path Planning Problem Towards Participant Executing Time Optimization in Mobile Crowd Sensing

In recent years, unmanned surface vehicles have emerged in an endless stream, and more and more special functions have been given. The unmanned surface vehicles has experienced explosive growth in China, and in addition to military applications, the civilian market has also shown strong demand. In this paper, we discussed the relevant researches and constructions in China, and we summed up the trends of development and application related to the unmanned surface vehicle in China. Meanwhile, the studies on the unmanned surface vehicles for design, perception, navigation control, modular design and so on are reviewed. We proposed the in-depth research strategy and combined application suggestions of unmanned surface vehicles, also prospected the future development direction and market prospect of unmanned surface vehicles.

Review and Application of Unmanned Surface Vehicle in China

Course control is one of the key research points for intelligent navigation of ships. Based on the optimization characteristics of Beetle Swarm Optimization (BSO) algorithm, this paper designs a ship course online self-optimization controller. Firstly, the ship MMG model is established, and the rudder turning rate constraint is introduced to establish the course control objective function. Then, based on the PSO algorithm, the BAS algorithm is improved to the BSO algorithm, and the BSO-PID algorithm is used for online course control. Through a series of simulation experiments, it shows that the method of self-optimizing PID parameters by BSO algorithm has better control effect on ship course control. Furthermore, it lays the foundation for further research in combination with other algorithms.

Ship Course Control Based on BSO-PID Online Self-Optimization Algorithm

Ship collisions can cause great losses of lives and property and brings enormous negative impacts to the maritime environment. To improve the intelligence and autonomy in assisting ships to make safe and quick decisions to avoid potential collisions, this paper proposes an optimization method for multiple ships to avoid collisions, based on predictive risk evaluation. Firstly, each ship makes trajectory predictions based on ship maneuverability model, and the inter-ship collision risk parameters calculation within a prediction horizon are calculated. To ensure that the ships choose suitable rudder angles to finish the collision avoidance process in an efficient way, an optimization problem is formulated using the knowledge with respect to the inter-ship collision risks and solved with a commercial solver. Preliminary results show that the proposed method can provide ships with the rudder angles they should choose and the corresponding rudder steering time to keep enough safe distance with the other ships.

Optimizing the anti-collision decisions between multiple ships based on predictive risk evaluations

With the continuous development of waterway traffic technology, the freight volume of inland waterway ships is increasing, and the waterway traffic environment becomes more and more complex. In the complex traffic environment of curved waterways with the participation of multiple ships, the traditional ship trajectory planning method is inefficient and often cannot meet the actual needs. To reduce the difficulty of trajectory planning and improve the planning efficiency, this paper proposes to complete the trajectory planning of inland waterway ships based on Frenet coordinate system, which converts the high-dimensional planning problem into multiple low-dimensional planning problems and greatly reduces the difficulty of ship planning. Frenet coordinates can intuitively reflect the motion relationship of different objects in the waterway, and the method is obvious for simplifying the trajectory planning problem and improving the efficiency of ship trajectory planning in the complex inland waterway traffic environment.

Research on the trajectory planning method of inland waterway vessels based on Frenet coordinate system

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies. 

Development of NC Program Simulation Software Based on AutoCAD

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xuming Wang

Author Tools

Chat about Author