SUNFISH®: A human-portable exploration AUV for complex 3D environments

Richmond, Kristof; Flesher, Chris; Lindzey, Laura; Tanner, Neal; Stone, William C.

doi:10.1109/oceans.2018.8604899

Cited by 22 publications

(18 citation statements)

References 30 publications

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“…The volume of works in the literature addressing the exploration of confined flooded spaces such as caves and tunnels, relating mostly to marine archaeology and geological prospecting [ 26 ], narrows down dramatically, which serves as an indicator of the bigger technical challenges posed by such environments. These challenges reflect on three main aspects of the UVs tailored for them [ 27 ]: their constrained mechanical design relating both shape and size, and their necessary high maneuverability; their difficulties in acquiring detailed positional information; and their required autonomy. These three aspects, detailed further in the following paragraphs, shape drastically the works in this field.…”

Section: Related Workmentioning

confidence: 99%

“…The need for high maneuverability and positional control has been signaled in the literature with the departure from the forward-moving torpedo-shaped submersibles typical from open water exploration [ 20 , 21 ] to, often, ellipsoidal [ 28 ], spherical [ 23 ], or cubical [ 22 ] shapes, able to perform movements in six degrees of freedom (DOF) and with hovering capabilities: hence their generic name, hovering AUVs (HAUVs). Following this line of work, the DEPTHX UV was carried out the exploration and mapping of hot springs [ 28 ]; the submersibles Sentry [ 29 ] and Mesobot [ 22 ] performed oceanographic surveys and observations of marine life; and U-CAT [ 26 ], IMOTUS-1 [ 23 ], and and SUNFISH [ 27 ] were used to autonomously explore and map archaeological sites, storage tanks, and underwater caves, respectively. These three latter works, which can be considered the closest existing works to our UX-1 robot not only in terms of their field of application but also regarding their guidance, are discussed further below.…”

Section: Related Workmentioning

confidence: 99%

“…Existing UVs for confined flooded spaces base therefore their positioning on inputs on their relative pose captured on-board, either with respect to their position in previous time steps like the inertial measurement units (IMUs), fiber-optic gyroscopes (FOGs), and Doppler velocity logs (DVLs) or with respect to their environment, like the sonar [ 23 , 26 , 27 ]. These inputs, partial and noisy, in the case of sonar because acoustic signals suffer from multipath in narrow and intricate pathways, are integrated using accumulative processing such as dead reckoning, Kalman filters, or Simultaneous Localization and Mapping (SLAM) techniques [ 21 , 26 ].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Guidance for Autonomous Underwater Vehicles in Confined Semistructured Environments

Milosevic

Fernández

Domínguez

et al. 2020

Sensors

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In this work, we present the design, implementation, and testing of a guidance system for the UX-1 robot, a novel spherical underwater vehicle designed to explore and map flooded underground mines. For this purpose, it needs to navigate completely autonomously, as no communications are possible, in the 3D networks of tunnels of semistructured but unknown environments and gather various geoscientific data. First, the overall design concepts of the robot are presented. Then, the guidance system and its subsystems are explained. Finally, the system’s validation and integration with the rest of the UX-1 robot systems are presented. A series of experimental tests following the software-in-the-loop and the hardware-in-the-loop paradigms have been carried out, designed to simulate as closely as possible navigation in mine tunnel environments. The results obtained in these tests demonstrate the effectiveness of the guidance system and its proper integration with the rest of the systems of the robot, and validate the abilities of the UX-1 platform to perform complex missions in flooded mine environments.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Guidance for Autonomous Underwater Vehicles in Confined Semistructured Environments

Milosevic

Fernández

Domínguez

et al. 2020

Sensors

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“…A feature reacquisition system with a low-cost sonar and navigation sensors was described in [19]. More recently, Sunfish [20] -an underwater SLAM system using a multibeam sonar, an underwater dead-reckoning system based on a fiber-optic gyroscope (FOG) IMU, acoustic DVL, and pressure-depth sensors -has been developed for autonomous cave exploration. Vision and visual-inertial based SLAM systems also developed in [21], [22], [23] for underwater reconstruction and navigation.…”

Section: Related Workmentioning

confidence: 99%

SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

Rahman

Rekleitis

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain.Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization -one of the main problems affecting other packages in underwater domain -by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness.

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“…Rahman et al (2018) developed and tested in different underwater environments, included a submerged cave a SLAM approach integrating data from acoustic, visual, inertial and depth sensors. Richmond et al (2018) designed a compact, highly manoeuvrable autonomous vehicle with real-time mapping capabilities.…”

Section: Underwater Cavesmentioning

confidence: 99%

3d Virtualization of an Underground Semi-Submerged Cave System

Nocerino

Menna

Farella

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Underwater caves represent the most challenging scenario for exploration, mapping and 3D modelling. In such complex environment, unsuitable to humans, highly specialized skills and expensive equipment are normally required. Technological progress and scientific innovation attempt, nowadays, to develop safer and more automatic approaches for the virtualization of these complex and not easily accessible environments, which constitute a unique natural, biological and cultural heritage. This paper presents a pilot study realised for the virtualization of 'Grotta Giusti' (Fig. 1), an underground semi-submerged cave system in central Italy. After an introduction on the virtualization process in the cultural heritage domain and a review of techniques and experiences for the virtualization of underground and submerged environments, the paper will focus on the employed virtualization techniques. In particular, the developed approach to simultaneously survey the semi-submersed areas of the cave relying on a stereo camera system and the virtualization of the virtual cave will be discussed.

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SUNFISH^®: A human-portable exploration AUV for complex 3D environments

Cited by 22 publications

References 30 publications

Guidance for Autonomous Underwater Vehicles in Confined Semistructured Environments

Guidance for Autonomous Underwater Vehicles in Confined Semistructured Environments

SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

3d Virtualization of an Underground Semi-Submerged Cave System

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