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Applied Sciences
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Robotic Sailing and Support Technologies
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Robotic Sailing and Support Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 15446

Special Issue Editors

Dr. Antonio Carlos Domínguez Brito

E-Mail Website
Guest Editor
Instituto Universitario SIANI. Universidad de Las Palmas de Gran Canaria
Interests: robotic sailing; embedded systems; software engineering applied to robotics; software architectures; sensor networks; environmental monitoring
Dr. Colin Sauze

E-Mail Website
Guest Editor
Aberystwyth University, Wales, United Kingdom
Interests: robotic sailing; long-term autonomy; autonomous collision avoidance
Prof. Dr. José Carlos Alves

E-Mail Website
Guest Editor
Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
Interests: custom reconfigurable computing; embedded hardware/software systems; digital design; high endurance marine robotics; robotic sailing

Special Issue Information

Dear Colleagues,

Environmental monitoring of certain aquatic environments, from littoral to extensive marine protected areas in high seas, is still mainly done with manned vessels or moored buoys or sensing platforms. The development and implementation of legal commitments on aquatic environments under governmental jurisdiction is often limited or even impeded by factors suh as the extension of these areas, difficult weather conditions, and—finally—the cost of the human and technical means required.

Autonomous vessels, and in particular sailing robots, may reduce the cost of ocean data acquisition as platforms for collecting all kind of information, from meteorological and oceanographic data, to ecological and environmental information. Other applications of great interest for those monitoring platforms are marine traffic and pollution control, security, surveillance, and rescue, to name a few.

Autonomous sailing vessels constitute an ideal platform for data acquisition in water natural environments. Moreover, being wind-propelled, they offer a potential for long-term operation in the ocean, given the considerable reduction of power consumption they need for navigation. In addition, this reduced energy demand makes them especially suited for being fully solar-powered.

An ideal requirement for autonomous sailing vessels is to be able to perform adequately in “under-control” operating conditions, for extended periods of time in very demanding and changing environments. Consequently, long-term autonomy poses a fundamental problem in autonomous sailing as in many areas of research in robotics. This requirement obviously demands robust and seafaring boat designs, which can withstand rough oceanic conditions, but also dynamic path and route planning capacities onboard, to adapt to changing sea, wind, and weather conditions. Furthermore, long-term autonomy requires the vessel to be self-conscious of its operating conditions and performance, aside from an infrastructure for remote monitoring and control.

This Special Issue is centered on robotic sailing and support technologies and invites the submission of research articles in the following topics:

  • Naval architecture of autonomous vessels: new designs and advanced materials;
  • Modelling, simulation, control, and stability analysis;
  • Sensors and actuators for autonomous sailboats;
  • Embedded control and system architectures for autonomous sailing’
  • Collision avoidance, boat-to-boat communication;
  • Route planning, navigation, and optimization;
  • Machine learning and self-tuning control algorithms for autonomous sailboats;
  • Long term autonomy in robotic sailing;
  • Energy and power management;
  • Field applications of autonomous sailing robots;
  • Robotic boats in teaching and education;
  • Legal issues for autonomous vessels.
Dr. Antonio Carlos Domínguez Brito
Dr. Colin Sauze
Prof. Dr. José Carlos Alves
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • robotic sailing
  • long-term autonomy
  • route planning and navigation
  • embedded systems
  • sensors and actuators
  • collision avoidance

Published Papers (5 papers)

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Research

21 pages, 3568 KiB  
Article
A Data Processing Framework for Polar Performance Diagrams
by Valentin Dannenberg, Robert Schüler and Achill Schürmann
Appl. Sci. 2022, 12(6), 3085; https://0-doi-org.brum.beds.ac.uk/10.3390/app12063085 - 17 Mar 2022
Viewed by 3744
Abstract
Polar performance diagrams are commonly used to predict the performance of a sailing vessel under given wind conditions. They are, in particular, an essential part of robotic sailing vessels and a basis for weather routing algorithms. In this paper we introduce a new [...] Read more.
Polar performance diagrams are commonly used to predict the performance of a sailing vessel under given wind conditions. They are, in particular, an essential part of robotic sailing vessels and a basis for weather routing algorithms. In this paper we introduce a new framework for scientific work with such diagrams, which we make available as an open source Python package. It contains a model for the creation of polar performance diagrams from measurement data and supports different representations of polar performance diagrams for different tasks. The framework also includes several methods for the visualisation of polar performance diagrams, for example for scientific publications. Additionally, the presented framework solves basic tasks for the future development of weather-routing algorithms in a far more general manner than other methods did previously: it provides the calculation of costs of a sailing trip using custom cost functions, suggestions of optimal steering using convex hull calculations and a more flexible calculation of isochrone points, using custom weather models. Altogether, the presented framework allows future researchers to more easily handle polar performance diagrams. The corresponding Python package is compatible with various established file formats. Full article
(This article belongs to the Special Issue Robotic Sailing and Support Technologies)
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22 pages, 5925 KiB  
Article
Continuous Autonomous Ship Learning Framework for Human Policies on Simulation
by Junoh Kim, Jisun Park and Kyungeun Cho
Appl. Sci. 2022, 12(3), 1631; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031631 - 04 Feb 2022
Cited by 2 | Viewed by 1674
Abstract
Considering autonomous navigation in busy marine traffic environments (including harbors and coasts), major study issues to be solved for autonomous ships are avoidance of static and dynamic obstacles, surface vehicle control in consideration of the environment, and compliance with human-defined navigation rules. The [...] Read more.
Considering autonomous navigation in busy marine traffic environments (including harbors and coasts), major study issues to be solved for autonomous ships are avoidance of static and dynamic obstacles, surface vehicle control in consideration of the environment, and compliance with human-defined navigation rules. The reinforcement learning (RL) algorithm, which demonstrates high potential in autonomous cars, has been presented as an alternative to mathematical algorithms and has advanced in studies on autonomous ships. However, the RL algorithm, through interactions with the environment, receives relatively fewer data from the marine environment. Moreover, the open marine environment causes difficulties for autonomous ships in learning human-defined navigation rules because of excessive degrees of freedom. This study proposes a sustainable, intelligent learning framework for autonomous ships (ILFAS), which helps solve these difficulties and learns navigation rules specified by human beings through neighboring ships. The application of case-based RL enables the participation of humans in the RL learning process through neighboring ships and the learning of human-defined rules. Cases built as curriculums can achieve high learning effects with fewer data along with the RL of layered autonomous ships. The experiment aims at autonomous navigation from a harbor, where marine traffic occurs on a neighboring coast. The learning results using ILFAS and those in an environment where random marine traffic occurs are compared. Based on the experiment, the learning time was reduced by a tenth. Moreover, the success rate of arrival at a destination was higher with fewer controls than the random method in the new marine traffic scenario. ILFAS can continuously respond to advances in ship manufacturing technology and changes in the marine environment. Full article
(This article belongs to the Special Issue Robotic Sailing and Support Technologies)
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15 pages, 11418 KiB  
Article
Horizon Targeted Loss-Based Diverse Realistic Marine Image Generation Method Using a Multimodal Style Transfer Network for Training Autonomous Vessels
by Jisun Park, Tae Hyeok Choi and Kyungeun Cho
Appl. Sci. 2022, 12(3), 1253; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031253 - 25 Jan 2022
Cited by 1 | Viewed by 2192
Abstract
Studies on virtual-to-realistic image style transfer have been conducted to minimize the difference between virtual simulators and real-world environments and improve the training of artificial intelligence (AI)-based autonomous driving models using virtual simulators. However, when applying an image style transfer network architecture that [...] Read more.
Studies on virtual-to-realistic image style transfer have been conducted to minimize the difference between virtual simulators and real-world environments and improve the training of artificial intelligence (AI)-based autonomous driving models using virtual simulators. However, when applying an image style transfer network architecture that achieves good performance using land-based data for autonomous vehicles to marine data for autonomous vessels, structures such as horizon lines and autonomous vessel shapes often lose their structural consistency. Marine data exhibit substantial environmental complexity, which depends on the size, position, and direction of the vessels because there are no lanes such as those for cars, and the colors of the sky and ocean are similar. To overcome these limitations, we propose a virtual-to-realistic marine image style transfer method using horizon-targeted loss for marine data. Horizon-targeted loss helps distinguish the structure of the horizon within the input and output images by comparing the segmented shape. Additionally, the design of the proposed network architecture involves a one-to-many style mapping technique, which is based on the multimodal style transfer method to generate marine images of diverse styles using a single network. Experiments demonstrate that the proposed method preserves the structural shapes on the horizon more accurately than existing algorithms. Moreover, the object detection accuracy using various augmented training data was higher than that observed in the case of training using only virtual data. The proposed method allows us to generate realistic data to train AI models of vision-based autonomous vessels by actualizing and augmenting virtual images acquired from virtual autonomous vessel simulators. Full article
(This article belongs to the Special Issue Robotic Sailing and Support Technologies)
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19 pages, 5239 KiB  
Article
An Engineering Design Approach for the Development of an Autonomous Sailboat to Cross the Atlantic Ocean
by Tanaka Akiyama, Jean-Francois Bousquet, Kostia Roncin, Graham Muirhead and Alexandra Whidden
Appl. Sci. 2021, 11(17), 8046; https://0-doi-org.brum.beds.ac.uk/10.3390/app11178046 - 30 Aug 2021
Cited by 6 | Viewed by 3046
Abstract
Over the past decade, interest in autonomous vessels significantly increased as the technology improved, especially in the automotive industry. Unlike cars, ships travel in a wild environment and maritime lanes are not limited by white lines. This makes the design of fully autonomous [...] Read more.
Over the past decade, interest in autonomous vessels significantly increased as the technology improved, especially in the automotive industry. Unlike cars, ships travel in a wild environment and maritime lanes are not limited by white lines. This makes the design of fully autonomous vessels even more challenging. Additionally, the need to reduce greenhouse gas emissions led to a renewed interest in wind propulsion. Sailboats have several advantages, such as full energy autonomy and a limited environmental impact. The Microtransat Challenge, which consists of crossing the Atlantic Ocean, is a tremendous test field. This paper describes, within that frame, a design procedure for the development of a robust fully autonomous sailboat to be deployed for long-term missions. In this paper, the mechanical and electronic design strategies are presented. A focus is on reliability and power management. Moreover, a test procedure for validating each design increment is described as well as a path plan that considers the risk of collision and weather routing with wind and currents. The Microtransat remains a challenge that no autonomous ship has ever succeeded (and has been completed by a single unmanned vessel, SB Met in 2018). However, the results by Breizh Tigresse and Sealeon in 2015 and 2018 made a step forward in terms of time and distance. They are presented and analyzed in this work. Full article
(This article belongs to the Special Issue Robotic Sailing and Support Technologies)
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Graphical abstract

18 pages, 1680 KiB  
Article
Design, Modeling, and Simulation of a Wing Sail Land Yacht
by Vítor Tinoco, Benedita Malheiro and Manuel F. Silva
Appl. Sci. 2021, 11(6), 2760; https://0-doi-org.brum.beds.ac.uk/10.3390/app11062760 - 19 Mar 2021
Cited by 1 | Viewed by 3661
Abstract
Autonomous land yachts can play a major role in the context of environmental monitoring, namely, in open, flat, windy regions, such as iced planes or sandy shorelines. This work addresses the design, modeling, and simulation of a land yacht probe equipped with a [...] Read more.
Autonomous land yachts can play a major role in the context of environmental monitoring, namely, in open, flat, windy regions, such as iced planes or sandy shorelines. This work addresses the design, modeling, and simulation of a land yacht probe equipped with a rigid free-rotating wing sail and tail flap. The wing was designed with a symmetrical airfoil and dimensions to provide the necessary thrust to displace the vehicle. Specifically, it proposes a novel design and simulation method for free rotating wing sail autonomous land yachts. The simulation relies on the Gazebo simulator together with the Robotic Operating System (ROS) middleware. It uses a modified Gazebo aerodynamics plugin to generate the lift and drag forces and the yawing moment, two newly created plugins, one to act as a wind sensor and the other to set the wing flap angular position, and the 3D model of the land yacht created with Fusion 360. The wing sail aligns automatically to the wind direction and can be set to any given angle of attack, stabilizing after a few seconds. Finally, the obtained polar diagram characterizes the expected sailing performance of the land yacht. The described method can be adopted to evaluate different wing sail configurations, as well as control techniques, for autonomous land yachts. Full article
(This article belongs to the Special Issue Robotic Sailing and Support Technologies)
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