Controlling a Robotic Fish to Swim Along a Wall Using Hydrodynamic Pressure Feedback

Yen, Wei-Kuo; Sierra, Daniel Martinez; Guo, Jenhwa

doi:10.1109/joe.2017.2785698

Cited by 40 publications

(20 citation statements)

References 22 publications

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“…Certain species such as the Blind Cave Fish, which have evolved degenerated sight, rely heavily on this technique for navigation, and for inferring the shape and size of unfamiliar objects (von Campenhausen et al 1981;Windsor et al 2008;de Perera 2004). The lateral line system has inspired the design of artificial sensory arrays, given their potential to transform underwater navigation of robotic vehicles (Yang et al 2006(Yang et al , 2010Kottapalli et al 2012;Ježov et al 2012;Kruusmaa et al 2014;Asadnia et al 2015;Triantafyllou et al 2016;Strokina et al 2016;Kottapalli et al 2018;Yen et al 2018). Such mechanoreceptors would be a vital addition to the already available suite of visual and acoustic sensors, with the added advantage of low energy-consumption, since they operate via passive mechanical deformation.…”

Section: Introductionmentioning

confidence: 99%

“…The lateral line system has inspired the design of artificial sensory arrays, given their potential to transform underwater navigation of robotic vehicles (Yang et al 2006(Yang et al , 2010Ježov et al 2012;Kottapalli et al 2012;Kruusmaa et al 2014;Asadnia et al 2015;Strokina et al 2016;Triantafyllou, Weymouth & Miao 2016;Kottapalli et al 2018;Yen, Sierra & Guo 2018). Such mechanoreceptors would be a vital addition to the already available suite of visual and acoustic sensors, with the added advantage of low energy-consumption, since they operate via passive mechanical deformation.…”

Section: Introductionmentioning

confidence: 99%

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Optimal sensor placement for artificial swimmers

et al. 2019

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Natural swimmers rely for their survival on sensors that gather information from the environment and guide their actions. The spatial organization of these sensors, such as the visual fish system and lateral line, suggests evolutionary selection, but their optimality remains an open question. Here, we identify sensor configurations that enable swimmers to maximize the information gathered from their surrounding flow field. We examine twodimensional, self-propelled and stationary swimmers that are exposed to disturbances generated by oscillating, rotating and D-shaped cylinders. We combine simulations of the Navier-Stokes equations with Bayesian experimental design to determine the optimal arrangements of shear and pressure sensors that best identify the locations of the disturbance-generating sources. We find a marked tendency for shear stress sensors to be located in the head and the tail of the swimmer, while they are absent from the midsection. In turn, we find a high density of pressure sensors in the head along with a uniform distribution along the entire body. The resulting optimal sensor arrangements resemble neuromast distributions observed in fish and provide evidence for optimality in sensor distribution for natural swimmers. † Email address for correspondence: petros@ethz.ch arXiv:1906.07585v1 [physics.flu-dyn]

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal sensor placement for artificial swimmers

et al. 2019

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“…Inspired by the lateral-line of fish, pressure sensors are placed along lines on the sides of underwater robots to register obstacles in the environment [10,11]. Recently, it has been demonstrated that robots can successfully navigate along walls making use of pressure variations due to the wall effect [12]. Wall detection with differential pressure sensors (DPS) was also achieved by Xu and Mohseni [13].…”

Section: Underwater Navigationmentioning

confidence: 99%

Determination of Flow Parameters of a Water Flow Around an AUV Body

Hoth

Kowalczyk

2019

Robotics

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Autonomous underwater vehicles (AUVs) have changed the way marine environment is surveyed, monitored and mapped. Autonomous underwater vehicles have a wide range of applications in research, military, and commercial settings. AUVs not only perform a given task but also adapt to changes in the environment, e.g., sudden side currents, downdrafts, and other effects which are extremely unpredictable. To navigate properly and allow simultaneous localisation and mapping (SLAM) algorithms to be used, these effects need to be detected. With current navigation systems, these disturbances in the water flow are not measured directly. Only the indirect effects are observed. It is proposed to detect the disturbances directly by placing pressure sensors on the surface of the AUV and processing the pressure data obtained. Within this study, the applicability of different learning methods for determining flow parameters of a surrounding fluid from pressure on an AUV body are tested. This is based on CFD simulations using pressure data from specified points on the surface of the AUV. It is shown that support vector machines are most suitable for the given task and yield excellent results.

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“…Arranging these sensors in arrays on artificial swimmers has attracted attention to transform underwater sensing [3,[34][35][36][37][38][39]. Here, leveraging the intelligent distributed sensing inspired by the lateral line showed to be effective in robots moving in aquatic environments [40][41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Optimal Flow Sensing for Schooling Swimmers

et al. 2020

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Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other fish. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with numerical simulations of the two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and the number of the leading swimmers using surface only information.

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Controlling a Robotic Fish to Swim Along a Wall Using Hydrodynamic Pressure Feedback

Cited by 40 publications

References 22 publications

Optimal sensor placement for artificial swimmers

Optimal sensor placement for artificial swimmers

Determination of Flow Parameters of a Water Flow Around an AUV Body

Optimal Flow Sensing for Schooling Swimmers

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