Tunable stiffness enables fast and efficient swimming in fish-like robots

Zhong, Qiang; Zhu, Joseph; Fish, Frank E.; Kerr, Sarah J.; Downs, Abigail M; Bart‐Smith, Hilary; Quinn, Daniel

doi:10.1126/scirobotics.abe4088

Cited by 107 publications

(70 citation statements)

References 72 publications

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“…The connection between body mechanics and swimming performance is still being examined, but in robotic models, adding stiff vertebral centra and reducing the amount of flexible notochordal material increased the stiffness of an artificial vertebral column and led to higher speed steady swimming (Long et al, 2011 ). More recently, increasing the stiffness of a tuna robot increased its swimming speed, up to an optimum, but then only decreased the speed slightly as stiffness increased further (Zhong et al, 2021 ). Thus, we suggest that these differences result in pelagic fishes having overall stiffer backbones, which may help them to swim continuously (Lauder, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, their more flexible vertebral columns may allow them more behavioral flexibility. For example, a greater range of speeds are possible if an animal can modulate its body stiffness (Wolf et al, 2020 ; Zhong et al, 2021 ). The more passively flexible bodies of demersal and benthic fishes may thus give them more active control of their body stiffness leading to a greater diversity of swimming modes.…”

Section: Discussionmentioning

confidence: 99%

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Internal vertebral morphology of bony fishes matches the mechanical demands of different environments

Baxter

Cohen

Donatelli

et al. 2022

Ecology and Evolution

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Fishes have repeatedly evolved characteristic body shapes depending on how close they live to the substrate. Pelagic fishes live in open water and typically have narrow, streamlined body shapes; benthic and demersal fishes live close to the substrate; and demersal fishes often have deeper bodies. These shape differences are often associated with behavioral differences: pelagic fishes swim nearly constantly, demersal fishes tend to maneuver near the substrate, and benthic fishes often lie in wait on the substrate. We hypothesized that these morphological and behavioral differences would be reflected in the mechanical properties of the body, and specifically in vertebral column stiffness, because it is an attachment point for the locomotor musculature and a central axis for body bending. The vertebrae of bony fishes are composed of two cones connected by a foramen, which is filled by the notochord. Since the notochord is more flexible than bony vertebral centra, we predicted that pelagic fishes would have narrower foramina or shallower cones, leading to less notochordal material and a stiffer vertebral column which might support continuous swimming. In contrast, we predicted that benthic and demersal fishes would have more notochordal material, making the vertebral column more flexible for diverse behaviors in these species. We therefore examined vertebral morphology in 79 species using micro‐computed tomography scans. Six vertebral features were measured including notochordal foramen diameter, centrum body length, and the cone angles and diameters for the anterior and posterior vertebral cones, along with body fineness. Using phylogenetic generalized least squares analyses, we found that benthic and pelagic species differed significantly, with larger foramina, shorter centra, and larger cones in benthic species. Thus, morphological differences in the internal shape of the vertebrae of fishes are consistent with a stiffer vertebral column in pelagic fishes and with a more flexible vertebral column in benthic species.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Internal vertebral morphology of bony fishes matches the mechanical demands of different environments

Baxter

Cohen

Donatelli

et al. 2022

Ecology and Evolution

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“…Fish can modulates body stiffness actively to adjust its swimming performance through muscles, tendons and other biological tissues [179][180][181]. Through testing biological structures, Long Jr supported the biological hypothesis that fish swimming behaviors are controlled by the body stiffness and the stiffness can be altered by the vertebral column [182].…”

Section: Body Stiffnessmentioning

confidence: 99%

Research Development on Fish Swimming

Jiang

2022

Chin. J. Mech. Eng.

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Fishes have learned how to achieve outstanding swimming performance through the evolution of hundreds of millions of years, which can provide bio-inspiration for robotic fish design. The premise of designing an excellent robotic fish include fully understanding of fish locomotion mechanism and grasp of the advanced control strategy in robot domain. In this paper, the research development on fish swimming is presented, aiming to offer a reference for the later research. First, the research methods including experimental methods and simulation methods are detailed. Then the current research directions including fish locomotion mechanism, structure and function research and bionic robotic fish are outlined. Fish locomotion mechanism is discussed from three views: macroscopic view to find a unified principle, microscopic view to include muscle activity and intermediate view to study the behaviors of single fish and fish school. Structure and function research is mainly concentrated from three aspects: fin research, lateral line system and body stiffness. Bionic robotic fish research focuses on actuation, materials and motion control. The paper concludes with the future trend that curvature control, machine learning and multiple robotic fish system will play a more important role in this field. Overall, the intensive and comprehensive research on fish swimming will decrease the gap between robotic fish and real fish and contribute to the broad application prospect of robotic fish.

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“…Fish-like robots usually have more than one body segment in their skeleton with joints that could be elastic. Recent work, for example 73 , shows that in robots with elastic joints, tunable stiffness enables faster and more efficient swimming. Surprisingly, analogous results exist for ground-based multisegment nonholonomic systems, see for example 74,75 , where effective stiffness tunable by periodic forcing leads to different limit cycles of varying efficiency, and multistable configurations for fast turning.…”

Section: Data Availability Statementmentioning

confidence: 99%

Physics-Informed Reinforcement Learning for Motion Control of a Fish-like Swimming Robot

Rodwell

Tallapragada

2022

Preprint

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Motion control of fish-like swimming robots presents many challenges due to the unstructured environment and un-modelled governing physics of the fluid-robot interaction. Commonly used low-fidelity control models using simplified formulas for drag and lift force do not capture key physics that can play an important role in the dynamics of small-sized robots with limited actuation. Deep Reinforcement Learning (DRL) holds considerable promise for motion control of robots with complex dynamics. Reinforcement learning methods require large amounts of data exploring a large subset of the relevant state space, which can be expensive, time consuming or unsafe to obtain. Data from simulations can be used in the initial stages of DRL, but in the case of swimming robots, large numbers of simulations are also infeasible from a time and computational resources perspective. Surrogate models that capture the primary physics of the system can be a useful starting point for training a DRL agent which is subsequently transferred to train with a higher fidelity simulation. We demonstrate the utility of such physics-informed reinforcement learning to train a policy that can enable velocity and path tracking for a planar swimming (fish-like) rigid Joukowski hydrofoil. This is done through a curriculum where the DRL agent is first trained to track limit cycles in a velocity space for a representative nonholonomic system and then transferred to to train on a small simulation data set of the swimmer. The results show the utility of physics-informed reinforcement learning for the control of fish-like swimming robots.

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Tunable stiffness enables fast and efficient swimming in fish-like robots

Cited by 107 publications

References 72 publications

Internal vertebral morphology of bony fishes matches the mechanical demands of different environments

Internal vertebral morphology of bony fishes matches the mechanical demands of different environments

Research Development on Fish Swimming

Physics-Informed Reinforcement Learning for Motion Control of a Fish-like Swimming Robot

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