Bio-inspired flow sensing and prediction for fish-like undulating locomotion: A CFD-aided approach

Zhou, Han; Hu, Tianjiang; Low, Kin Huat; Ma, Zhiliang; Wang, Guangming; Xu, Hui

doi:10.1016/s1672-6529(14)60132-3

Cited by 24 publications

(10 citation statements)

References 43 publications

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“…For the above kinematic model, the three-dimensional effect of anguilliform swimmers can be neglected [25]. Therefore, we adopt the two-dimensional model in this work and only consider the robotic fish in the same 2D plane.…”

Section: Geometric Modelmentioning

confidence: 99%

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An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods

Yang

et al. 2019

Applied Sciences

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Autonomous Underwater Vehicle (AUV) has become a hotspot in the field of robot in recent years. As a special kind of AUV, the robotic fish can achieve better propulsion efficiency and maneuverability than traditional AUVs. Studies show that robotic fish formation can save energy and perform more complex tasks than single robotic fish, but it is difficult to maintain a stable formation because the nearby environmental condition is hard to obtain. Inspired by the lateral line system (LLS) of fish, this paper constructs a predictive model of flow velocity and a judgement model of spacing between individual platforms for robotic fish formation through monitoring sensors on robotic fish surface. The models are built by methods of polynomial fitting and neural networks based on Computational Fluid Dynamics (CFD) simulation. The results show that the flow velocity predicted by our model could reduce the error to 0.4%, and the spacing judgement accuracy could reach at least 80%. The findings are useful for maintaining a stable formation and will provide significant guidance for the control of robotic fish formation and sensor installation position on the robotic fish surface.

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Section: Geometric Modelmentioning

confidence: 99%

“…Zhou et al [25] used CFD methods to investigate the hydrodynamics of bio-inspired fish swimming, especially the spatial distribution and temporal variation of the near-body pressure of fish are studied. They also provided an algorithm to predict the inlet flow velocity by using the near-body pressure at distributed spatial points.…”

Section: Introductionmentioning

confidence: 99%

An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods

Yang

et al. 2019

Applied Sciences

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“…The research can be divided into two parts. One is the engineering design of sensors based on setting nature as a model, which is called biomimetics [12][13][14], and the other one is the hydrodynamic mechanism of LLS [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Robust Classification Method for Underwater Targets Using the Chaotic Features of the Flow Field

Xinghua

Qin

2020

JMSE

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Fish can sense their surrounding environment by their lateral line system (LLS). In order to understand the extent to which information can be derived via LLS and to improve the adaptive ability of autonomous underwater vehicles (AUVs), a novel strategy is presented, which directly uses the information of the flow field to distinguish the object obstacle. The flow fields around different targets are obtained by the numerical method, and the pressure signal on the virtual lateral line is studied based on the chaos theory and fast Fourier transform (FFT). The compounded parametric features, including the chaotic features (CF) and the power spectrum density (PSD), which is named CF-PSD, are used to recognize the kinds of obstacles. During the research of CF, the largest Lyapunov exponent (LLE), saturated correlation dimension (SCD), and Kolmogorov entropy (KE) are taken into account, and PSD features include the number, amplitude, and position of wave crests. A two-step support vector machine (SVM) is built and used to classify the shapes and incidence angles based on the CF-PSD. It is demonstrated that the flow fields around triangular and square targets are chaotic systems, and the new findings indicate that the object obstacle can be recognized directly based on the information of the flow field, and the consideration of a parametric feature extraction method (CF-PSD) results in considerably higher classification success.

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“…However, the potential flow theory is based on the assumption of idealized fluid, and the details of the flow field cannot be described accurately. With the development of computer technology, computational fluid dynamics (CFD) has become the main hydrodynamics method [28][29][30][31][32][33]. Based on these studies, we determined the structures of neuromasts and the flow fields around different targets.…”

Section: Introductionmentioning

confidence: 99%

A Novel Obstacle Localization Method for an Underwater Robot Based on the Flow Field

Xinghua

Qin

2019

JMSE

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Because the underwater environment is complex, autonomous underwater vehicles (AUVs) have difficulty locating their surroundings autonomously. In order to improve the adaptive ability of AUVs, this paper presents a novel obstacle localization strategy based on the flow features. Like fish, the strategy uses the flow field information directly to locate the object obstacles. Two different localization methods are provided and compared. The first method, which is named the Method of Spatial Distribution (MSD), is based on the spatial distribution of the flow field. The second method, which is named the Method of Amplitude Variation (MAV), is provided by the amplitude variation of the flow field. The flow field around spherical targets is obtained by a numerical method, and both methods use the parallel velocity component on the virtual lateral line. During the study, different target numbers, detective ratios, spacing ratios, and flow velocities are taken into account. It is demonstrated that both methods are able to locate object obstacles. However, the prediction accuracy of MAV is higher than that of MSD. That implies that MAV is more robust than MSD. These new findings indicate that the object obstacles can be directly located based on the flow field information and robust flow sensing is perhaps not based on the spatial distribution of the flow field but rather, on its fluctuation range.

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Bio-inspired flow sensing and prediction for fish-like undulating locomotion: A CFD-aided approach

Cited by 24 publications

References 43 publications

An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods

An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods

Robust Classification Method for Underwater Targets Using the Chaotic Features of the Flow Field

A Novel Obstacle Localization Method for an Underwater Robot Based on the Flow Field

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