Emergence of robust self-organized undulatory swimming based on local hydrodynamic force sensing

Thandiackal, Robin; Melo, Kamilo; Paez, Laura; Herault, Johann; Kano, Takeshi; Akiyama, Kyoichi; Boyer, Frédéric; Ryczko, Dimitri; Ishiguro, Akio; Ijspeert, Auke Jan

doi:10.1126/scirobotics.abf6354

Cited by 83 publications

(64 citation statements)

References 104 publications

(135 reference statements)

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“…Optimal sensor locations for artificial swimmers have been also investigated to improve the efficiency (Verma et al, 2019;Weber et al, 2020), especially when the number of biomimetic neuromasts is limited. The sensing of external hydrodynamic stress may improve the control robustness in single-fish self-organized undulatory swimming (Thandiackal et al, 2021), as well as the propulsive efficiency (Tytell et al, 2010). In collective swimming, the interaction of the flow field and the surface stress between neighboring fish have been established by several recent studies (Daghooghi and Borazjani, 2015;Chen et al, 2016;Peng et al, 2018, Li et al, 2019a2019b;Verma et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamical Fingerprint of a Neighbour in a Fish Lateral Line

Kolomenskiy

et al. 2022

Front. Robot. AI

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For fish, swimming in group may be favorable to individuals. Several works reported that in a fish school, individuals sense and adjust their relative position to prevent collisions and maintain the group formation. Also, from a hydrodynamic perspective, relative-position and kinematic synchronisation between adjacent fish may considerably influence their swimming performance. Fish may sense the relative-position and tail-beat phase difference with their neighbors using both vision and the lateral-line system, however, when swimming in dark or turbid environments, visual information may become unavailable. To understand how lateral-line sensing can enable fish to judge the relative-position and phase-difference with their neighbors, in this study, based on a verified three-dimensional computational fluid dynamics approach, we simulated two fish swimming adjacently with various configurations. The lateral-line signal was obtained by sampling the surface hydrodynamic stress. The sensed signal was processed by Fast Fourier Transform (FFT), which is robust to turbulence and environmental flow. By examining the lateral-line pressure and shear-stress signals in the frequency domain, various states of the neighboring fish were parametrically identified. Our results reveal that the FFT-processed lateral-line signals in one fish may potentially reflect the relative-position, phase-differences, and the tail-beat frequency of its neighbor. Our results shed light on the fluid dynamical aspects of the lateral-line sensing mechanism used by fish. Furthermore, the presented approach based on FFT is especially suitable for applications in bioinspired swimming robotics. We provide suggestions for the design of artificial systems consisting of multiple stress sensors for robotic fish to improve their performance in collective operation.

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Section: Introductionmentioning

confidence: 99%

Hydrodynamical Fingerprint of a Neighbour in a Fish Lateral Line

Kolomenskiy

et al. 2022

Front. Robot. AI

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“…Because a sine-based control method has the problem of discontinuous output, artificial central pattern generators (CPGs) composed of nonlinear oscillators have become the mainstream motion control method of robots. At present, the main CPG models include Matsuoka oscillators [32,33], Hopf oscillators [22,34] and phase oscillators [35,36]. Control methods based on CPGs have been successfully applied to several fish-like robots, and switching between different behaviors has been realized.…”

Section: Introductionmentioning

confidence: 99%

Course Control of a Manta Robot Based on Amplitude and Phase Differences

Hao

Cao

et al. 2022

JMSE

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Due to external interference, such as waves, the success of underwater missions depends on the turning performance of the vehicle. Manta rays use two broad pectoral fins for propulsion, which provide better anti-interference ability and turning performance. Inspired by biological yaw modes, we use the phase difference between the pectoral fins to realize fast course adjustment and the amplitude difference to realize precise adjustment. We design a bionic robot with pectoral fins and use phase oscillators to realize rhythmic motion. An expected phase difference transition equation is introduced to realize a fast and smooth transition of the output, and the parameters are adjusted online. We combine the phase difference and amplitude difference yaw modes to realize closed-loop course control. Through course interference and adjustment experiments, it is verified that the combined mode is more effective than a single mode. Finally, a rectangular trajectory swimming experiment demonstrates continuous mobility of the robot under the combined mode.

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“…In robotics, feedback from continuum joints is typically provided by sensing changes in length and curvature [ 14 ]. However, it has recently been demonstrated that stable swimming rhythms can be generated through sensing of hydrodynamic pressures on the body [ 17 ]. Fish possess few, if any, length sensors like muscle spindles [ 18 , 19 ], but can sense body curvature in the spinal cord and through stretching of the skin [ 20 – 22 ].…”

Section: Introductionmentioning

confidence: 99%

Corollary discharge enables proprioception from lateral line sensory feedback

2021

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Animals modulate sensory processing in concert with motor actions. Parallel copies of motor signals, called corollary discharge (CD), prepare the nervous system to process the mixture of externally and self-generated (reafferent) feedback that arises during locomotion. Commonly, CD in the peripheral nervous system cancels reafference to protect sensors and the central nervous system from being fatigued and overwhelmed by self-generated feedback. However, cancellation also limits the feedback that contributes to an animal’s awareness of its body position and motion within the environment, the sense of proprioception. We propose that, rather than cancellation, CD to the fish lateral line organ restructures reafference to maximize proprioceptive information content. Fishes’ undulatory body motions induce reafferent feedback that can encode the body’s instantaneous configuration with respect to fluid flows. We combined experimental and computational analyses of swimming biomechanics and hair cell physiology to develop a neuromechanical model of how fish can track peak body curvature, a key signature of axial undulatory locomotion. Without CD, this computation would be challenged by sensory adaptation, typified by decaying sensitivity and phase distortions with respect to an input stimulus. We find that CD interacts synergistically with sensor polarization to sharpen sensitivity along sensors’ preferred axes. The sharpening of sensitivity regulates spiking to a narrow interval coinciding with peak reafferent stimulation, which prevents adaptation and homogenizes the otherwise variable sensor output. Our integrative model reveals a vital role of CD for ensuring precise proprioceptive feedback during undulatory locomotion, which we term external proprioception.

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Emergence of robust self-organized undulatory swimming based on local hydrodynamic force sensing

Cited by 83 publications

References 104 publications

Hydrodynamical Fingerprint of a Neighbour in a Fish Lateral Line

Hydrodynamical Fingerprint of a Neighbour in a Fish Lateral Line

Course Control of a Manta Robot Based on Amplitude and Phase Differences

Corollary discharge enables proprioception from lateral line sensory feedback

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