Development of an Autonomous Surface Vehicle and Performance Evaluation of Autonomous Navigation Technologies

Choi, Jinwoo; Park, Jeonghong; Jung, Jongdae; Lee, Yoongeon; Choi, Hyun‐Taek

doi:10.1007/s12555-019-0686-0

Cited by 33 publications

(12 citation statements)

References 19 publications

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“…The surface vehicle developed in KRISO [24] is shown in Fig. 4; it is equipped with a dual-antenna GPS (Hemisphere V113) to acquire accurate heading references (less than 0.3°r ms), along with geodetic positions.…”

Section: A Data Acquisitionmentioning

confidence: 99%

Bathymetric Pose Graph Optimization With Regularized Submap Matching

Jung¹,

Park²,

Choi³

et al. 2022

IEEE Access

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The quality of sonar seabed mapping performed using autonomous underwater vehicles depends on the accuracy of the vehicle trajectory estimation. To reduce the accumulated pose estimation errors from dead reckoning, bathymetry observations from sonar sensors are often exploited within the framework of pose graph optimization, while the submaps of the seafloor are used to add loop-closure constraints to the pose graph by iterative closest point. However, matching with the submaps suffers from local minima because the seafloor is mostly flat and featureless. To resolve this issue, we regularized the sub-maps to enhance the spatial variations in the vertical direction; thus, we realized improved matching accuracy. Given these constraints, the pose graph can be optimized in real time and provide a corrected trajectory. The performance of the proposed method is validated through experiments using a surface vessel where the same navigation and sonar systems as used in the underwater vehicles are installed, in addition to a GPS receiver for ground truth acquisition.INDEX TERMS Bathymetric mapping, multibeam echosounder, pose graph optimization, simultaneous localization and mapping, unmanned marine vehicles.

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Section: A Data Acquisitionmentioning

confidence: 99%

Bathymetric Pose Graph Optimization With Regularized Submap Matching

Jung¹,

Park²,

Choi³

et al. 2022

IEEE Access

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Vlad Diaconita . acoustic information without prior information of source locations, as shown in [8], [11].…”

Section: Introductionmentioning

confidence: 99%

The Autonomous Platforms Inertial Dataset

et al. 2022

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One of the critical tasks required for fully autonomous functionality is the ability to achieve an accurate navigation solution; that is, to determine the platform position, velocity, and orientation. Various sensors, depending on the vehicle environment (air, sea, or land), are employed to achieve this goal. In parallel to the development of novel navigation and sensor fusion algorithms, machine-learning based algorithms are penetrating into the navigation and sensor fusion fields. An excellent example for this trend is pedestrian dead reckoning, used for indoor navigation, where both classical and machine learning approaches are used to improve the navigation accuracy. To facilitate machine learning algorithms' derivation and validation for autonomous platforms, a huge quantity of recorded sensor data is needed. Unfortunately, in many situations, such datasets are not easy to collect or are not publicly available. To advance the development of accurate autonomous navigation, this paper presents the autonomous platforms inertial dataset. It contains inertial sensor raw data and corresponding ground truth trajectories. The dataset was collected using a variety of platforms including a quadrotor, two autonomous underwater vehicles, a land vehicle, a remote controlled electric car, and a boat. A total of 805.5 minutes of recordings were made using different types of inertial sensors, global navigation satellite system receivers, and Doppler velocity logs. After describing the sensors that were employed for the recordings, a detailed description of the conducted experiments is provided. The autonomous platform inertial dataset is available at: https://github.com/ansfl/Navigation-Data-Project/.

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“…The comprehensive study and sustainable use of the oceans has been accelerated together with the development of underwater vehicles and underwater intervention technologies that have enable the accessing of remote sites [1]. Hence, it can be seen that ocean exploration is relying nowadays mostly on robotic and several advanced technologies [2][3][4]. Although several developments have been focused on remotely operated (ROV), autonomous (AUV) and surface (USV) vehicles [3,[5][6][7], it has been identified that other marine systems also require the combination of mechanics, electronics, software, and control, to be developed and appropriately integrated [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, it can be seen that ocean exploration is relying nowadays mostly on robotic and several advanced technologies [2][3][4]. Although several developments have been focused on remotely operated (ROV), autonomous (AUV) and surface (USV) vehicles [3,[5][6][7], it has been identified that other marine systems also require the combination of mechanics, electronics, software, and control, to be developed and appropriately integrated [8][9][10][11][12][13]. As it can be seen in literature, the development of underwater vehicles (AUVs and ROVs) has been accelerated in the second part of the last decade, considering different operation scenarios and operation conditions.…”

Section: Introductionmentioning

confidence: 99%

Modular Hardware Architecture for the Development of Underwater Vehicles Based on Systems Engineering

Aristizábal

Zuluaga

Rúa

et al. 2021

JMSE

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This paper addresses the development of a modular hardware architecture for the design/construction/operation of a remotely operated vehicle (ROV), based on systems engineering. The Vee model is first presented as a sequential process that emphasizes the validation processes with stakeholders and verification plans in the development and production stages of the ROV’s life cycle. The conceptual design process starts with the mapping of user requirements to engineering specifications, using the House of Quality (HoQ), a quality function deployment tool that allows executing a functional-division-based hardware design process that facilitates the integration of components and subsystems, as desired for modular architectures. Then, the functional division and hardware architectures are described, and their connection is made through the proposed system architecture that sets the foundation for the definition of a physical architecture, as it involves flows that connect abstract functions with a real context. Development and production stages are exemplified through the design, construction, and integration of some hardware components needed for the remotely operated vehicle Pionero500, and the operational stage briefly describes the first sea trials conducted for the ROV. Systems engineering has shown to be a very useful tool for the development of marine vehicles and marine engineering projects that require modular architectures.

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Development of an Autonomous Surface Vehicle and Performance Evaluation of Autonomous Navigation Technologies

Cited by 33 publications

References 19 publications

Bathymetric Pose Graph Optimization With Regularized Submap Matching

Bathymetric Pose Graph Optimization With Regularized Submap Matching

The Autonomous Platforms Inertial Dataset

Modular Hardware Architecture for the Development of Underwater Vehicles Based on Systems Engineering

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