Data-Driven Dynamic Modeling for a Swimming Robotic Fish

Yu, Junzhi; Yuan, Jun; Wu, Zhengxing; Tan, Min

doi:10.1109/tie.2016.2564338

Cited by 62 publications

(14 citation statements)

References 32 publications

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“…The large-amplitude elongated-body theory 18 proposed later extends its applications to larger lateral deformation, which makes it the most acceptable theory. In addition, Yu et al 125 proposed a data-driven approach. In their method, the dynamic model is first derived with the Morrison equation and the strip method, and the parameters are directly identified from experimental data and integrated into the dynamic model to reshape it.…”

Section: Modeling and Control Of Robotic Fishesmentioning

confidence: 99%

A comprehensive review on fish-inspired robots

et al. 2022

International Journal of Advanced Robotic Systems

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Recently, the increasing interest in underwater exploration motivates the development of aquatic unmanned vehicles. To execute hazardous tasks in an unknown or even hostile environment, researchers have directed on developing biomimetic robots inspired by the extraordinary maneuverability, cruising speed, and propulsion efficiency of fish. Nevertheless, the performance of current prototypes still has gaps compared with that of real fishes. In this review, recent approaches in structure designs, actuators, and sensors are presented. In addition, the theoretical methods for modeling the robotic fishes are consolidated, and the control strategies are offered. Finally, the current challenges are summarized, and possible future directions are deeply discussed. It is expected that the emergence of new engineering and biological technologies will enhance the field of robotic fish for further advancement.

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Section: Modeling and Control Of Robotic Fishesmentioning

confidence: 99%

A comprehensive review on fish-inspired robots

et al. 2022

International Journal of Advanced Robotic Systems

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“…The Lagrange method is simple in derivation but complex in calculation and is only applicable to robotic fish with fewer joints [34]. The Morrison method is simple and intuitive but not suitable for large robotic fish [35]. The Navier-Stokes equation is accurate in calculation, but it takes a long time [16].…”

Section: ) Gravity and Buoyancy Of The Bodymentioning

confidence: 99%

Research on the Turning Maneuverability of a Bionic Robotic Dolphin

et al. 2022

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This paper studies four patterns for the maneuverability of robotic dolphins: turning with flapping flippers, turning with swing flippers, turning with head offset, and turning with head-flipper coordination. Turning maneuverability is one of the important evaluation indexes for the performance of underwater bionic robots, and existing literature has rarely theoretically or experimentally evaluated turning maneuverability for the latter two turning patterns. In this paper, we establish a dynamic model for the high maneuverability of underwater bionic robots and discuss the influence of motion design parameters, including the frequency of the pectoral fins and the offset angle of the head, on the turning maneuverability, focusing on the turning angular velocity and turning radius. To verify the effectiveness and control accuracy of the dynamic model, we develop a multidegree-of-freedom (multi-DOF) and high-maneuverability bionic robotic dolphin and test its maneuverability through experiments. Extensive experimental results demonstrate that the established dynamic model can successfully predict the turning maneuverability of robotic dolphins. In addition, the turning methods in this paper have better turning performance than some robotic fish. This paper provides a reference for the establishment of a maneuvering dynamics model for underwater bionic robots and a theoretical basis for the design of a turning maneuver control system. INDEX TERMSRobotic dolphin, dynamic model, turning pattern, maneuverability.

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“…While traditional DTs have been developed for systems that we have a solid grasp of (in other words, model-based ), data-driven and AI-equipped DTs help with complex robotic systems for which building high-fidelity dynamics models is not feasible ( model-free ). The latter has been applied in more and more cases, even for biomimetic robotic system development (e.g., robotic fish [ 258 ]). Table 4 summarizes AI-equipped DTs and categorizes them based on learning algorithms used and subfields, such as control (detailed in Section 3.2 ), planning (detailed in Section 3.3 ), and HRI/HRC (detailed in Section 3.4 ).…”

Section: Advanced Roboticsmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

194

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Data-Driven Dynamic Modeling for a Swimming Robotic Fish

Cited by 62 publications

References 32 publications

A comprehensive review on fish-inspired robots

A comprehensive review on fish-inspired robots

Research on the Turning Maneuverability of a Bionic Robotic Dolphin

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

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