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Special Issue "Intelligence and Autonomy for Underwater Robotic Vehicles"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Prof.Dr. Claudio Rossi
Website
Guest Editor
Centre for Automation and Robotics UPM-CSIC, Madrid, Spain
Interests: Artificial Intelligence; robotics; biomimetics; optimization; evolutionary algorithms
Prof.Dr. Sergio Dominguez
Website
Guest Editor
Centre for Automation and Robotics UPM-CSIC, Madrid, Spain
Interests: intelligent control robotics and cybernetics robots; intelligent machines autonomous systems; computer vision

Special Issue Information

Autonomous underwater vehicles have a wide range of possible applications. These include inspection in confined spaces like pipelines, natural and artificial caves, the interior of complex artificial structures, as well as deep-sea or other extreme natural environments. In all such cases, the impossibility of using tethers and the lack of high-bandwidth and reliable wireless communications with human operators make autonomy and intelligence a key feature of the required systems. This includes the capability of understanding their surrounding, self-localization, and motion planning, as well as high-level task/mission planning and self-awareness.

In this Special Issue, the latest advances in autonomy and intelligence for AUVs are addressed, with a special emphasis on real-world applications and field demonstrations.

Prof. Claudio Rossi
Prof. Sergio Dominguez
Guest Editors

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 papers will be 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. Sensors 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 2200 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

  • Autonomous underwater vehicles
  • Guidance, navigation, and control
  • Self-awareness
  • Mission planning
  • Path planning
  • Underwater SLAM
  • Underwater perception and sensor fusion
  • Manipulation and grasping
  • Near and wide range underwater communication systems
  • Multi-robot systems
  • Bio-inspired underwater robots

Published Papers (4 papers)

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Research

Open AccessArticle
Functional Self-Awareness and Metacontrol for Underwater Robot Autonomy
Sensors 2021, 21(4), 1210; https://doi.org/10.3390/s21041210 - 09 Feb 2021
Abstract
Autonomous systems are expected to maintain a dependable operation without human intervention. They are intended to fulfill the mission for which they were deployed, properly handling the disturbances that may affect them. Underwater robots, such as the UX-1 mine explorer developed in the [...] Read more.
Autonomous systems are expected to maintain a dependable operation without human intervention. They are intended to fulfill the mission for which they were deployed, properly handling the disturbances that may affect them. Underwater robots, such as the UX-1 mine explorer developed in the UNEXMIN project, are paradigmatic examples of this need. Underwater robots are affected by both external and internal disturbances that hamper their capability for autonomous operation. Long-term autonomy requires not only the capability of perceiving and properly acting in open environments but also a sufficient degree of robustness and resilience so as to maintain and recover the operational functionality of the system when disturbed by unexpected events. In this article, we analyze the operational conditions for autonomous underwater robots with a special emphasis on the UX-1 miner explorer. We then describe a knowledge-based self-awareness and metacontrol subsystem that enables the autonomous reconfiguration of the robot subsystems to keep mission-oriented capability. This resilience augmenting solution is based on the deep modeling of the functional architecture of the autonomous robot in combination with ontological reasoning to allow self-diagnosis and reconfiguration during operation. This mechanism can transparently use robot functional redundancy to ensure mission satisfaction, even in the presence of faults. Full article
(This article belongs to the Special Issue Intelligence and Autonomy for Underwater Robotic Vehicles)
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Open AccessArticle
Online 3-Dimensional Path Planning with Kinematic Constraints in Unknown Environments Using Hybrid A* with Tree Pruning
Sensors 2021, 21(4), 1152; https://doi.org/10.3390/s21041152 - 06 Feb 2021
Abstract
In this paper we present an extension to the hybrid A* (HA*) path planner. This extension allows autonomous underwater vehicle (AUVs) to plan paths in 3-dimensional (3D) environments. The proposed approach enables the robot to operate in a safe manner by accounting for [...] Read more.
In this paper we present an extension to the hybrid A* (HA*) path planner. This extension allows autonomous underwater vehicle (AUVs) to plan paths in 3-dimensional (3D) environments. The proposed approach enables the robot to operate in a safe manner by accounting for the vehicle’s motion constraints, thus avoiding collisions and ensuring that the calculated paths are feasible. Secondly, we propose an improvement for operations in unexplored or partially known environments by endowing the planner with a tree pruning procedure, which maintains a valid and feasible search-tree during operation. When the robot senses new obstacles in the environment that invalidate its current path, the planner prunes the tree of branches which collides with the environment. The path planning algorithm is then initialised with the pruned tree, enabling it to find a solution in a lower time than replanning from scratch. We present results obtained through simulation which show that HA* performs better in known underwater environments than compared algorithms in regards to planning time, path length and success rate. For unknown environments, we show that the tree pruning procedure reduces the total planning time needed in a variety of environments compared to running the full planning algorithm during replanning. Full article
(This article belongs to the Special Issue Intelligence and Autonomy for Underwater Robotic Vehicles)
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Open AccessArticle
Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer
Sensors 2020, 20(24), 7084; https://doi.org/10.3390/s20247084 - 10 Dec 2020
Abstract
In this paper, an output-feedback fuzzy adaptive dynamic surface controller (FADSC) based on fuzzy adaptive extended state observer (FAESO) is proposed for autonomous underwater vehicle (AUV) systems in the presence of external disturbances, parameter uncertainties, measurement noises and actuator faults. The fuzzy logic [...] Read more.
In this paper, an output-feedback fuzzy adaptive dynamic surface controller (FADSC) based on fuzzy adaptive extended state observer (FAESO) is proposed for autonomous underwater vehicle (AUV) systems in the presence of external disturbances, parameter uncertainties, measurement noises and actuator faults. The fuzzy logic system is incorporated into both the observers and controllers to improve the adaptability of the entire system. The dynamics of the AUV system is established first, considering the external disturbances and parameter uncertainties. Based on the dynamic models, the ESO, combined with a fuzzy logic system tuning the observer bandwidth, is developed to not only adaptively estimate both system states and the lumped disturbances for the controller, but also reduce the impact of measurement noises. Then, the DSC, together with fuzzy logic system tuning the time constant of the low-pass filter, is designed using estimations from the FAESO for the AUV system. The asymptotic stability of the entire system is analyzed through Lyapunov’s direct method in the time domain. Comparative simulations are implemented to verify the effectiveness and advantages of the proposed method compared with other observers and controllers considering external disturbances, parameter uncertainties and measurement noises and even the actuator faults that are not considered in the design process. The results show that the proposed method outperforms others in terms of tracking accuracy, robustness and energy consumption. Full article
(This article belongs to the Special Issue Intelligence and Autonomy for Underwater Robotic Vehicles)
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Open AccessArticle
Evaluation of Several Feature Detectors/Extractors on Underwater Images towards vSLAM
Sensors 2020, 20(15), 4343; https://doi.org/10.3390/s20154343 - 04 Aug 2020
Abstract
Modern visual SLAM (vSLAM) algorithms take advantage of computer vision developments in image processing and in interest point detectors to create maps and trajectories from camera images. Different feature detectors and extractors have been evaluated for this purpose in air and ground environments, [...] Read more.
Modern visual SLAM (vSLAM) algorithms take advantage of computer vision developments in image processing and in interest point detectors to create maps and trajectories from camera images. Different feature detectors and extractors have been evaluated for this purpose in air and ground environments, but not extensively for underwater scenarios. In this paper (I) we characterize underwater images where light and suspended particles alter considerably the images captured, (II) evaluate the performance of common interest points detectors and descriptors in a variety of underwater scenes and conditions towards vSLAM in terms of the number of features matched in subsequent video frames, the precision of the descriptors and the processing time. This research justifies the usage of feature detectors in vSLAM for underwater scenarios and present its challenges and limitations. Full article
(This article belongs to the Special Issue Intelligence and Autonomy for Underwater Robotic Vehicles)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Tailoring the Self-Organizing Map for Autonomous Robots (SOMAR): Evaluation under a realistic Gazebo-based UUV and USV aquatic multi-robot simulator
Authors: André G. Araújo, Micael S. Couceiro, Daryna Datsenko, João F. Ferreira, Rui P. Rocha
Affiliation: University of Coimbra, Portugal
Abstract: Endowing a multi-robot system with the ability to maintain a given predefined geometric shape over time, has been applied in a wide range of applications, namely in infrastructure inspection and maintenance, cooperative mapping, search and rescue, and others. Despite the panoply of multi-robot formation control approaches available in the literature, many challenges are still left untackled or lead to a suboptimal performance, especially when robots need to operate in real-world scenarios, wherein moving obstacles and other constraints need to be taken into account. This paper reports preliminary steps towards extending the self-organizing map neural network method for autonomous robot formation maintenance, leading to the Self-Organizing Map (SOM) for Autonomous Robots (SOMAR) architecture. In this paper, SOM is improved with an obstacle-free convex region local navigator, so that robots are able to efficiently avoid collisions with both static and moving obstacles, while maintaining, as much as possible, a given formation. SOMAR is compared with its predecessor under a realistic Gazebo-based aquatic multi-robot simulator.

Title: Online Path Planning with Kinematic Constraints in Unknown Environments
Authors: Jonatan Scharff Willners1*, Daniel Gonzalez-Adell1, Juan David Hernández2, Èric Pairet3, and Yvan Petillot1
Affiliation: 1 Institute of Sensors, Signals and Systems, Heriot-Watt University, Edinburgh, UK 2 Centre for Artificial Intelligence, Robotics and Human-Machine Systems (IROHMS), Cardiff University, Wales, UK; [email protected] 3 Edinburgh Centre for Robotics, Edinburgh, UK; [email protected] *Correspondence: [email protected]
Abstract: Place holder: When an AUV operates autonomously it is important that it is able to move between configurations. To ensure a safe and collision free operation the robot should include the surrounding environment and the motion capabilities of the robot during planning. During operation in unknown environments, the robot needs to be able to sense new obstacles an adapt its path if needed to avoid collision. In this publication we present a framework for path planning to handle safe operation in unknown environments. This is done by extending the capabilities of \ac{HA*} with a tree pruning procedure and to operate in the 3-Dimensional space. The tree pruning procedure enables improved re-planning capabilities through re-use of valid branches in the search-tree. By using the tree-pruning procedure we show a reduction in the overall planning time needed to solve motion planning queries compared to re-planning using the standard form of \ac{HA*} when the current path is deemed infeasible due to new obstacles observed in the environment. The approach is able to run online and adapt its path based on newly observed information about the environment.

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