Map-based localization in structured underwater environment using simulated hydrodynamic maps and an artificial lateral line

Fuentes‐Pérez, Juan Francisco; Muhammad, Naveed; Tuhtan, Jeffrey A.; Carbonell-Baeza, Ruth; Musall, Mark; Toming, Gert; Kruusmaa, Maarja

doi:10.1109/robio.2017.8324406

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…Hydrodynamic information was acquired by ALL systems and analyzed to estimate the speed. Compared with the flow rate diagram, the location of the robotic fish was found out in the flow rate maps which was simulated from hydrodynamic results [100] .…”

Section: Neighborhood Robotic Fish Perceptionmentioning

confidence: 99%

“…Zhang et al 2015 [83] 6 pressure sensors (Servoflo MS5401-BM) Laboratory experiment DeVries et al 2015 [84] 8 IPMC sensors and four embedded pressure sensors Laboratory experiment Position holding Salumäe et al 2013 [51] 5 pressure sensors (Intersema MS5407-AM) Laboratory experiment Localization Muhammad et al 2015 [99] 14 pressure sensors (Intersema MS5407-AM) Natural environment experiment Fuentes-Pérez et al 2017 [100] 16 pressure sensors Natural environment experiment…”

Section: Speed Estimationmentioning

confidence: 99%

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Fish Lateral Line Inspired Flow Sensors and Flow-aided Control: A Review

2021

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Any phenomenon in nature is potential to be an inspiration for us to propose new ideas. Lateral line is a typical example which has attracted more interest in recent years. With the aid of lateral line, fish is capable of acquiring fluid information around, which is of great significance for them to survive, communicate and hunt underwater. In this paper, we briefly introduce the morphology and mechanism of the lateral line first. Then we focus on the development of artificial lateral line which typically consists of an array of sensors and can be installed on underwater robots. A series of sensors inspired by the lateral line with different sensing principles have been summarized. And then the applications of artificial lateral line systems in hydrodynamic environment sensing and vortices detection, dipole oscillation source detection, and autonomous control of underwater robots have been reviewed. In addition, the existing problems and future foci in this field have been further discussed in detail. The current works and future foci have demonstrated that artificial lateral line has great potentials of applications and contributes to the development of underwater robots.

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Section: Neighborhood Robotic Fish Perceptionmentioning

confidence: 99%

Section: Speed Estimationmentioning

confidence: 99%

Fish Lateral Line Inspired Flow Sensors and Flow-aided Control: A Review

2021

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“…However, it is hard to deploy these tools in deep-sea or in unstructured environments. Mapbased localization methods in structured environments using bio-inspired flow sensing are presented in [26], [27], [28] that exploit the extraction of flow features and the speed or pressure estimation. An IMU and a laser-based vision system is utilized for the localization of underwater vehicles [29].…”

Section: Introductionmentioning

confidence: 99%

An Underactuated Vehicle Localization Method in Marine Environments

Alam

Reis

Bobadilla

et al. 2018

OCEANS 2018 MTS/IEEE Charleston

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The underactuated vehicles are apposite for the longterm deployment and data collection in spatiotemporally varying marine environments. However, these vehicles need to estimate their positions (states) with intrinsic sensing in their long-term trajectories. In previous studies, autonomous underwater vehicles have commonly used vision and range sensors for autonomous state estimation. Inspired by the intrinsic sensing and the persistent deployment, we investigate the localization problem (state estimation) for an inexpensive and underactuated drifting vehicle called a drifter. In this paper, we present a localization method for the drifter making use of the observations of a proprioceptive sensor, i.e., compass. We create the water flow pattern within a given region from ocean model predictions, develop a stochastic motion model, and analyze the persistent water flow behavior. Given a distribution of initial deployment states of the drifter at a particular depth of the water column within the region and the water flow pattern, our method finds attractors and their transient groups at the given depth as the persistent behavior of the water flow. A most-likely localized trajectory of the drifter for a sequence of compass observations is generated based on the persistent behavior of the water flow and hidden Markov model. Our simulation results based on data from ocean model predictions substantiate good performance of our proposed localization method with a low error rate of the state estimation in the long-term trajectory of the drifter.

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