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A Novel Simulation Platform for Underwater Data Muling Communications Using Autonomous Underwater Vehicles. COMPUTERS 2021. [DOI: 10.3390/computers10100119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Autonomous Underwater Vehicles (AUVs) are seen as a safe and cost-effective platforms for performing a myriad of underwater missions. These vehicles are equipped with multiple sensors which, combined with their long endurance, can produce large amounts of data, especially when used for video capturing. These data need to be transferred to the surface to be processed and analyzed. When considering deep sea operations, where surfacing before the end of the mission may be unpractical, the communication is limited to low bitrate acoustic communications, which make unfeasible the timely transmission of large amounts of data unfeasible. The usage of AUVs as data mules is an alternative communications solution. Data mules can be used to establish a broadband data link by combining short-range, high bitrate communications (e.g., RF and wireless optical) with a Delay Tolerant Network approach. This paper presents an enhanced version of UDMSim, a novel simulation platform for data muling communications. UDMSim is built upon a new realistic AUV Motion and Localization (AML) simulator and Network Simulator 3 (ns-3). It can simulate the position of the data mules, including localization errors, realistic position control adjustments, the received signal, the realistic throughput adjustments, and connection losses due to the fast SNR change observed underwater. The enhanced version includes a more realistic AML simulator and the antenna radiation patterns to help evaluating the design and relative placement of underwater antennas. The results obtained using UDMSim show a good match with the experimental results achieved using an underwater testbed. UDMSim is made available to the community to support easy and faster evaluation of underwater data muling oriented communications solutions and to enable offline replication of real world experiments.
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Visibility improvement in relation to turbidity and distance, and application to docking. ARTIFICIAL LIFE AND ROBOTICS 2020. [DOI: 10.1007/s10015-020-00606-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10092991] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper presents a high performance (low computationally demanding) monocular vision-based system for a hovering Autonomous Underwater Vehicle (AUV) in the context of autonomous docking process-MViDO system: Monocular Vision-based Docking Operation aid. The MViDO consists of three sub-modules: a pose estimator, a tracker and a guidance sub-module. The system is based on a single camera and a three spherical color markers target that signal the docking station. The MViDO system allows the pose estimation of the three color markers even in situations of temporary occlusions, being also a system that rejects outliers and false detections. This paper also describes the design and implementation of the MViDO guidance module for the docking manoeuvres. We address the problem of driving the AUV to a docking station with the help of the visual markers detected by the on-board camera, and show that by adequately choosing the references for the linear degrees of freedom of the AUV, the AUV is conducted to the dock while keeping those markers in the field of view of the on-board camera. The main concepts behind the MViDO are provided and a complete characterization of the developed system is presented from the formal and experimental point of view. To test and evaluate the MViDO detector and pose an estimator module, we created a ground truth setup. To test and evaluate the tracker module we used the MARES AUV and the designed target in a four-meter tank. The performance of the proposed guidance law was tested on simulink/Matlab.
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Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10041256] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater environments, communication, localization, and navigation of AUVs are considered challenges due to the impossibility of relying on radio communications and global positioning systems. For a long time, acoustic systems have been the main approach for solving these challenges. However, they present their own shortcomings, which are more relevant for AUV teams. As a result, researchers have explored different alternatives. To summarize and analyze these alternatives, a review of the literature is presented in this paper. Finally, a summary of collaborative AUV teams and missions is also included, with the aim of analyzing their applicability, advantages, and limitations.
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Youakim D, Cieslak P, Dornbush A, Palomer A, Ridao P, Likhachev M. Multirepresentation, Multiheuristic A* search‐based motion planning for a free‐floating underwater vehicle‐manipulator system in unknown environment. J FIELD ROBOT 2020. [DOI: 10.1002/rob.21923] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Dina Youakim
- Computer Vision & Robotics InstituteUniversity of Girona Girona Catalonia Spain
| | - Patryk Cieslak
- Computer Vision & Robotics InstituteUniversity of Girona Girona Catalonia Spain
| | - Andrew Dornbush
- The Robotics InstituteCarnegie Mellon University Pittsburgh Pennsylvania
| | - Albert Palomer
- Computer Vision & Robotics InstituteUniversity of Girona Girona Catalonia Spain
| | - Pere Ridao
- Computer Vision & Robotics InstituteUniversity of Girona Girona Catalonia Spain
| | - Maxim Likhachev
- The Robotics InstituteCarnegie Mellon University Pittsburgh Pennsylvania
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Lwin KN, Myint M, Mukada N, Yamada D, Matsuno T, Saitou K, Godou W, Sakamoto T, Minami M. Sea Docking by Dual-eye Pose Estimation with Optimized Genetic Algorithm Parameters. J INTELL ROBOT SYST 2019. [DOI: 10.1007/s10846-018-0970-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Docking Control of an Autonomous Underwater
Vehicle Using Reinforcement Learning. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9173456] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
To achieve persistent systems in the future, autonomous underwater vehicles (AUVs) willneed to autonomously dock onto a charging station. Here, reinforcement learning strategies wereapplied for the first time to control the docking of an AUV onto a fixed platform in a simulationenvironment. Two reinforcement learning schemes were investigated: one with continuous stateand action spaces, deep deterministic policy gradient (DDPG), and one with continuous state butdiscrete action spaces, deep Q network (DQN). For DQN, the discrete actions were selected as stepchanges in the control input signals. The performance of the reinforcement learning strategies wascompared with classical and optimal control techniques. The control actions selected by DDPG sufferfrom chattering effects due to a hyperbolic tangent layer in the actor. Conversely, DQN presents thebest compromise between short docking time and low control effort, whilst meeting the dockingrequirements. Whereas the reinforcement learning algorithms present a very high computational costat training time, they are five orders of magnitude faster than optimal control at deployment time,thus enabling an on-line implementation. Therefore, reinforcement learning achieves a performancesimilar to optimal control at a much lower computational cost at deployment, whilst also presentinga more general framework.
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Chavez AG, Mueller CA, Doernbach T, Birk A. Underwater navigation using visual markers in the context of intervention missions. INT J ADV ROBOT SYST 2019. [DOI: 10.1177/1729881419838967] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Intervention missions, that is, underwater manipulation tasks, for example, in the context of oil-&-gas production, require a high amount of precise, robust navigation. In this article, we describe the use of an advanced vision system suited for deep-sea operations, which in combination with artificial markers on target structures like oil-&-gas production-Christmas-trees significantly boosts navigation performance. The system is validated in two intensive field tests running off the shore of Marseille, France. In the experiments, a commercial remotely operated vehicle equipped with the system and a mock-up structure with an oil-&-gas production panel is used to evaluate the navigation performance.
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Affiliation(s)
- Arturo Gomez Chavez
- Department of Computer Science and Electrical Engineering, Jacobs University Bremen, Bremen, Germany
| | - Christian A Mueller
- Department of Computer Science and Electrical Engineering, Jacobs University Bremen, Bremen, Germany
| | - Tobias Doernbach
- Department of Computer Science and Electrical Engineering, Jacobs University Bremen, Bremen, Germany
| | - Andreas Birk
- Department of Computer Science and Electrical Engineering, Jacobs University Bremen, Bremen, Germany
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