Detection and tracking of chemical trails in bio-inspired sensory systems

Huang, Yunpeng; Yen, Jeannette; Kanso, Eva

doi:10.1080/17797179.2017.1321207

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…Other organisms, such as the blue crab, rely on a combination of flow and chemical sensing for tracking odor plumes in foraging behavior [7,8]. Similar response to chemically-flavored laminar and turbulent trails is documented in aquatic and air-born organisms following female pheromones, including copepods [9,10] and moths [11,12].…”

Section: Introductionmentioning

confidence: 91%

Training bioinspired sensors to classify flows

2018

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We consider the inverse problem of classifying flow patterns from local sensory measurements. This problem is inspired by the ability of various aquatic organisms to respond to ambient flow signals, and is relevant for translating these abilities to underwater robotic vehicles. In Colvert, Alsalman and Kanso, B&B (2018), we trained neural networks to classify vortical flows by relying on a single flow sensor that measures a 'time history' of the local vorticity. Here, we systematically investigate the effects of distinct types of sensors on the accuracy of flow classification. We consider four types of sensors-vorticity, flow velocities parallel and transverse to the direction of flow propagation, and flow speed-and show that the networks trained using transverse velocity outperform other networks, even when subjected to aggressive data corruption. We then train the network to classify flow patterns instantaneously, using a spatially-distributed array of sensors and a single 'one time' sensory measurement. The network, based on a handful of spatially-distributed sensors, exhibits remarkable accuracy in flow classification. These results lay the groundwork for developing learning algorithms for the dynamic deployment of sensory arrays in unsteady flows.

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Section: Introductionmentioning

confidence: 91%

Training bioinspired sensors to classify flows

2018

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“…To respond to this limited and localized information, with no memory of past measurements, we provided the follower only control over its turning rate (the dot denotes time derivative), as opposed to direct control over its heading angle θ [19]. The follower’s motion is thus described by the nonholonomic unicycle model [45, 47, 66],…”

Section: Resultsmentioning

confidence: 99%

Interpretable and Generalizable Strategies for Stably Following Hydrodynamic Trails

Hang,

Jiao,

Heydari

et al. 2023

Preprint

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Aquatic organisms offer compelling evidence that local flow sensing alone, without vision, is sufficient to guide them to the source of a vortical flow field, be it a swimming or stationary object. However, the feedback mechanisms that allow a flow-sensitive follower to track hydrodynamic trails remain opaque. Here, using high-fidelity fluid simulations and Reinforcement Learning (RL), we discovered two equally effective policies for trail following. While not apriori obvious, the RL policies led to parsimonious response strategies, analogous to Braitenberg’s simplest vehicles, where a follower senses local flow signals and turns away from or towards the direction of stronger signal. We analyzed the stability of the RLinspired strategies in ideal and simulated flows and demonstrated their robustness in tracking unfamiliar flows using diverse types of sensors. Our findings uncovered a surprising connection between the stability of hydrodynamic trail following and sense-to-response time delays, akin to those observed in the sensorimotor systems of aquatic organisms, and could guide future designs of flow-responsive autonomous robots.

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“…Algorithms that follow the gradient of the signal are particularly convenient when the signal itself is spatiotemporally smooth, with sufficiently high amplitude. In many situations, these gradient-based algorithms need to be combined with signal detection algorithms [48,49]. Gradient-based methods have been also used to cooperatively control a network of mobile sensors to climb or descend an environmental gradient [50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Flowtaxis in the wakes of oscillating airfoils

Colvert

Dong

et al. 2020

Theor. Comput. Fluid Dyn.

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Many aquatic organisms from copepods to harbor seals are able to detect and respond to flow disturbances. The physiological mechanisms underlying such behavior remain a challenge for current and future research. Here, we propose a simplified flow sensing scenario in which a mobile sensor reorients its heading in response to local flow stimuli, with the goal of tracing the wakes created by oscillating airfoils to their source. Specifically, we engineer a feedback control strategy where the sensory measurements are based on transverse vorticity gradients. Through numerical experiments, we assess the efficacy of the sensor in following topologically distinct wakes. We demonstrate that the strategy is robust to variations in the wake itself, and we arrive at empirical rules that the sensor's initial position and orientation must satisfy in order to successfully locate the airfoil. We conclude by commenting on the relevance of the model and results to animal behavior and bio-inspired underwater robotics. We also discuss current and future opportunities for employing machine learning tools to devise and improve these sensory control strategies.

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Detection and tracking of chemical trails in bio-inspired sensory systems

Cited by 7 publications

References 28 publications

Training bioinspired sensors to classify flows

Training bioinspired sensors to classify flows

Interpretable and Generalizable Strategies for Stably Following Hydrodynamic Trails

Flowtaxis in the wakes of oscillating airfoils

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