The effect of waist twisting on walking speed of an amphibious salamander like robot

Yin, Xinyan; Jia, Lichao; Wang, Chen; Xie, Guangming

doi:10.1007/s10409-015-0532-4

Cited by 6 publications

(9 citation statements)

References 11 publications

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“…We have developed a quadruped robot and demonstrated via experiments that decentralized control with cross-coupled sensory feedback (Suzuki et al, 2019 ) enables effective sprawling locomotion. Unlike most previous works based on CPGs with inter-oscillator couplings (Ijspeert et al, 2007 ; Crespi et al, 2013 ; Yin et al, 2016 ; Ijspeert, 2020 ), or on gait patterns based on geometric mechanics (Zhong et al, 2018 ), our model uses sensory-couplings through bidirectional feedback between the legs and trunk ( Figure 2A ). Owing to this mechanism, the robot can quickly converge to stable gaits, achieve high locomotion performances, and adapt to leg failure.…”

Section: Discussionmentioning

confidence: 99%

“…Sprawling locomotion in vertebrate animals is controlled by a distributed neural network called the central pattern generator (CPG) and sensory feedback from peripheral nerves, according to experiments with salamanders (Cabelguen et al, 2003 ). Based on these findings, several neural network models have been proposed for sprawling robots to emulate and investigate sprawling locomotion (Ijspeert et al, 2007 ; Harischandra et al, 2011 ; Crespi et al, 2013 ; Yin et al, 2016 ; Zhong et al, 2018 ). However, most of the models were based on open-loop control.…”

Section: Introductionmentioning

confidence: 99%

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Sprawling Quadruped Robot Driven by Decentralized Control With Cross-Coupled Sensory Feedback Between Legs and Trunk

et al. 2021

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Quadruped animals achieve agile and highly adaptive locomotion owing to the coordination between their legs and other body parts, such as the trunk, head, and tail, that is, body–limb coordination. This study aims to understand the sensorimotor control underlying body–limb coordination. To this end, we adopted sprawling locomotion in vertebrate animals as a model behavior. This is a quadruped walking gait with lateral body bending used by many amphibians and lizards. Our previous simulation study demonstrated that cross-coupled sensory feedback between the legs and trunk helps to rapidly establish body–limb coordination and improve locomotion performance. This paper presented an experimental validation of the cross-coupled sensory feedback control using a newly developed quadruped robot. The results show similar tendencies to the simulation study. Sensory feedback provides rapid convergence to stable gait, robustness against leg failure, and morphological changes. Our study suggests that sensory feedback potentially plays an essential role in body–limb coordination and provides a robust, sensory-driven control principle for quadruped robots.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sprawling Quadruped Robot Driven by Decentralized Control With Cross-Coupled Sensory Feedback Between Legs and Trunk

et al. 2021

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“…Previous studies have modeled the CPG components using abstract oscillators (Ijspeert et al, 2005 , 2007 ; Knüsel et al, 2013 ; Yin et al, 2016 ), single bursting neurons (Liu et al, 2018 , 2020 ), integrate-and-fire neurons (Ijspeert, 2001 ; Bem et al, 2003 ; Harischandra et al, 2011 ; Knüsel et al, 2013 ) and detailed networks of three compartment Hodgkin-Huxley neurons (Bicanski et al, 2013 ).…”

Section: Related Modeling Workmentioning

confidence: 99%

“…Many models include four joints between the girdles and a single degree of freedom (DOF) per limb (Ijspeert, 2001 ; Ijspeert et al, 2005 , 2007 ; Suzuki et al, 2019a ) or three DOFs per limb (Harischandra et al, 2010 , 2011 ; Liu et al, 2018 , 2020 ). The simplest model has one of each (Yin et al, 2016 ), while other models have one joint between the girdles and two DOFs per limb (Zhong et al, 2018 ; Suzuki et al, 2019b ). Bem et al ( 2003 ) have modeled the swimming salamander as a chain of ten links, corresponding roughly to three trunk joints and no limbs.…”

Section: Related Modeling Workmentioning

confidence: 99%

Reproducing Five Motor Behaviors in a Salamander Robot With Virtual Muscles and a Distributed CPG Controller Regulated by Drive Signals and Proprioceptive Feedback

et al. 2020

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Diverse locomotor behaviors emerge from the interactions between the spinal central pattern generator (CPG), descending brain signals and sensory feedback. Salamander motor behaviors include swimming, struggling, forward underwater stepping, and forward and backward terrestrial stepping. Electromyographic and kinematic recordings of the trunk show that each of these five behaviors is characterized by specific patterns of muscle activation and body curvature. Electrophysiological recordings in isolated spinal cords show even more diverse patterns of activity. Using numerical modeling and robotics, we explored the mechanisms through which descending brain signals and proprioceptive feedback could take advantage of the flexibility of the spinal CPG to generate different motor patterns. Adapting a previous CPG model based on abstract oscillators, we propose a model that reproduces the features of spinal cord recordings: the diversity of motor patterns, the correlation between phase lags and cycle frequencies, and the spontaneous switches between slow and fast rhythms. The five salamander behaviors were reproduced by connecting the CPG model to a mechanical simulation of the salamander with virtual muscles and local proprioceptive feedback. The main results were validated on a robot. A distributed controller was used to obtain the fast control loops necessary for implementing the virtual muscles. The distributed control is demonstrated in an experiment where the robot splits into multiple functional parts. The five salamander behaviors were emulated by regulating the CPG with two descending drives. Reproducing the kinematics of backward stepping and struggling however required stronger muscle contractions. The passive oscillations observed in the salamander's tail during forward underwater stepping could be reproduced using a third descending drive of zero to the tail oscillators. This reduced the drag on the body in our hydrodynamic simulation. We explored the effect of local proprioceptive feedback during swimming and forward terrestrial stepping. We found that feedback could replace or reduce the need for different drives in both cases. It also reduced the variability of intersegmental phase lags toward values appropriate for locomotion. Our work suggests that different motor behaviors do not require different CPG circuits: a single circuit can produce various behaviors when modulated by descending drive and sensory feedback.

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“…Sprawling locomotion in vertebrate animals is controlled by a distributed neural network called the central pattern generator (CPG) and sensory feedback from peripheral nerves, according to experiments with salamanders (Cabelguen et al, 2003). Based on these findings, several neural network models have been proposed for sprawling robots to emulate and investigate sprawling locomotion (Ijspeert et al, 2007;Harischandra et al, 2011;Crespi et al, 2013;Yin et al, 2016;Zhong et al, 2018). However, most of the models were based on open-loop control.…”

Section: Introductionmentioning

confidence: 99%

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The effect of waist twisting on walking speed of an amphibious salamander like robot

Cited by 6 publications

References 11 publications

Sprawling Quadruped Robot Driven by Decentralized Control With Cross-Coupled Sensory Feedback Between Legs and Trunk

Sprawling Quadruped Robot Driven by Decentralized Control With Cross-Coupled Sensory Feedback Between Legs and Trunk

Reproducing Five Motor Behaviors in a Salamander Robot With Virtual Muscles and a Distributed CPG Controller Regulated by Drive Signals and Proprioceptive Feedback

Video_1.MP4

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