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Intelligence and Autonomy for Underwater Robotic Vehicles

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

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 26504

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Special Issue Editors

Prof. Dr. Claudio Rossi

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Guest Editor

Centre for Automation and Robotics UPM-CSIC, 28500 Madrid, Spain
Interests: artificial intelligence; robotics; biomimetics; optimization; evolutionary algorithms
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sergio Dominguez

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Guest Editor

Centre for Automation and Robotics UPM-CSIC, 28500 Madrid, Spain
Interests: intelligent control robotics and cybernetics robots; intelligent machines autonomous systems; computer vision
Special Issues, Collections and Topics in MDPI journals

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

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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

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Published Papers (6 papers)

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Research

14 pages, 602 KiB

Open AccessArticle

Topological Navigation for Autonomous Underwater Vehicles in Confined Semi-Structured Environments

by Claudio Rossi, Adrian Caro Zapata, Zorana Milosevic, Ramon Suarez and Sergio Dominguez

Sensors 2023, 23(5), 2371; https://doi.org/10.3390/s23052371 - 21 Feb 2023

Cited by 2 | Viewed by 2338

Abstract

In this work, we present the design, implementation, and simulation of a topology-based navigation system for the UX-series robots, a spherical underwater vehicle designed to explore and map flooded underground mines. The objective of the robot is to navigate autonomously in the 3D network of tunnels of a semi-structured but unknown environment in order to gather geoscientific data. We start from the assumption that a topological map has been generated by a low-level perception and SLAM module in the form of a labeled graph. However, the map is subject to uncertainties and reconstruction errors that the navigation system must address. First, a distance metric is defined to compute node-matching operations. This metric is then used to enable the robot to find its position on the map and navigate it. To assess the effectiveness of the proposed approach, extensive simulations have been carried out with different randomly generated topologies and various noise rates. Full article

(This article belongs to the Special Issue Intelligence and Autonomy for Underwater Robotic Vehicles)

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23 pages, 7781 KiB

Open AccessArticle

Towards the Design and Implementation of an Image-Based Navigation System of an Autonomous Underwater Vehicle Combining a Color Recognition Technique and a Fuzzy Logic Controller

by Yu-Hsien Lin, Chao-Ming Yu and Chia-Yu Wu

Sensors 2021, 21(12), 4053; https://doi.org/10.3390/s21124053 - 12 Jun 2021

Cited by 16 | Viewed by 4085

Abstract

This study proposes the development of an underwater object-tracking control system through an image-processing technique. It is used for the close-range recognition and dynamic tracking of autonomous underwater vehicles (AUVs) with an auxiliary light source for image processing. The image-processing technique includes color space conversion, target and background separation with binarization, noise removal with image filters, and image morphology. The image-recognition results become more complete through the aforementioned process. After the image information is obtained for the underwater object, the image area and coordinates are further adopted as the input values of the fuzzy logic controller (FLC) to calculate the rudder angle of the servomotor, and the propeller revolution speed is defined using the image information. The aforementioned experiments were all conducted in a stability water tank. Subsequently, the FLC was combined with an extended Kalman filter (EKF) for further dynamic experiments in a towing tank. Specifically, the EKF predicts new coordinates according to the original coordinates of an object to resolve data insufficiency. Consequently, several tests with moving speeds from 0.2 m/s to 0.8 m/s were analyzed to observe the changes in the rudder angles and the sensitivity of the propeller revolution speed. Full article

(This article belongs to the Special Issue Intelligence and Autonomy for Underwater Robotic Vehicles)

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28 pages, 2092 KiB

Open AccessArticle

Functional Self-Awareness and Metacontrol for Underwater Robot Autonomy

by Esther Aguado, Zorana Milosevic, Carlos Hernández, Ricardo Sanz, Mario Garzon, Darko Bozhinoski and Claudio Rossi

Sensors 2021, 21(4), 1210; https://doi.org/10.3390/s21041210 - 9 Feb 2021

Cited by 18 | Viewed by 5494

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 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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20 pages, 4786 KiB

Open AccessArticle

Online 3-Dimensional Path Planning with Kinematic Constraints in Unknown Environments Using Hybrid A* with Tree Pruning

by Jonatan Scharff Willners, Daniel Gonzalez-Adell, Juan David Hernández, Èric Pairet and Yvan Petillot

Sensors 2021, 21(4), 1152; https://doi.org/10.3390/s21041152 - 6 Feb 2021

Cited by 27 | Viewed by 4757

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 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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22 pages, 8022 KiB

Open AccessArticle

Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer

by Song Kang, Yongfeng Rong and Wusheng Chou

Sensors 2020, 20(24), 7084; https://doi.org/10.3390/s20247084 - 10 Dec 2020

Cited by 24 | Viewed by 3354

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 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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16 pages, 10497 KiB

Open AccessArticle

Evaluation of Several Feature Detectors/Extractors on Underwater Images towards vSLAM

by Franco Hidalgo and Thomas Bräunl

Sensors 2020, 20(15), 4343; https://doi.org/10.3390/s20154343 - 4 Aug 2020

Cited by 28 | Viewed by 4865

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, 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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