Towards a UUV Launch and Recovery System on a Slowly Moving Submarine

Irani, Rishad A.; Limited, Rolls-Royce Canada; Kehoe, D; Spencer, W.; Watt, George D.; Gillis, Colin B.; Carretero, Juan A.; Dubay, Rickey

doi:10.3940/rina.ws.2014.19

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…This process, similar to aerial refueling, allows the AUV to dock with an underwater base station for a brief period to charge and exchange data. There are various underwater docking stations (DS) used for AUV docking and recovery, which can include landing on the seafloor, being carried by a mother ship, unmanned surface vehicle, or submarine, depending on the mission (Irani et al, 2014; Raspante, 2012; Renilson, 2014; Sarda & Dhanak, 2016; Watt et al, 2016). In addition, there are various types of DS that can support AUVs, such as planar, funnel, and box‐shaped types (Li et al, 2015; Trslic et al, 2020; Yang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A stereo visual navigation method for docking autonomous underwater vehicles

Xu,

Jiang,

et al. 2023

Journal of Field Robotics

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Recovering and recharging autonomous underwater vehicles (AUVs) on a regular basis allows for long‐term underwater activities. This research provides a vision‐based navigation strategy for AUVs to independently identify and reconstruct the docking station (DS). The proposed framework includes a light beacon detection approach, a filter‐based light beacon matching method, and a fusion pose estimation method for DS positioning. Four green LED light beacons are mounted symmetrically on the docking ring, enabling the stereo camera to observe them from close range. A method for detecting light beacons is proposed that assures detection accuracy by identifying false positives. On a single frame from a stereo camera, filter‐based matching follows the light beacons precisely. In addition, we construct a position estimate method that significantly improves accuracy and efficiency by consisting of analyzation and iteration. A series of virtual and real‐world experiments demonstrate that our methodology can provide AUVs with reliable docking navigation.

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Section: Introductionmentioning

confidence: 99%

A stereo visual navigation method for docking autonomous underwater vehicles

Xu,

Jiang,

et al. 2023

Journal of Field Robotics

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“…Nowadays, the extension of the functions of large submersibles using underwater manipulators for the dynamic deployment and recovery of unmanned underwater vehicles (UUV) is a major research direction [1][2][3]. UUVs have the characteristics of strong concealment, high intelligence, lower construction costs and no casualties.…”

Section: Introductionmentioning

confidence: 99%

Vibration Suppression Trajectory Planning of Underwater Flexible Manipulators Based on Incremental Kriging-Assisted Optimization Algorithm

Huang

Tang

Chen

et al. 2023

JMSE

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It is of great significance to expand the functions of submarines by carrying underwater manipulators with a large working space. To suppress the flexible vibration of underwater manipulators, an improved sparrow search algorithm (ISSA) combining an elite strategy and a sine algorithm is proposed for the trajectory planning of underwater flexible manipulators. In this method, the vibration evaluation function is established based on the precise dynamic model of the underwater flexible manipulator and considering complex motion and vibration constraints. Simulation results show that the ISSA algorithm requires only 1/3.68 of the time of PSO. Compared to PSO, SSA and the opposition-based learning sparrow search algorithm (OBLSSA), the optimization performance is improved by 17.3%, 13.1% and 9.7%, respectively. However, because the complex dynamics model of the underwater flexible manipulator leads to large computational effort and a long optimization time, ISSA is difficult to apply directly in practice. To obtain a large number of optimization results in a shorter time, an incremental Kriging-assisted ISSA (IKA-ISSA) is proposed in this paper. Simulation results show that IKA-ISSA has good nonlinear approximation ability and the optimization time is only 3% of that of the ISSA.

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