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

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 com...

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Bibliographic Details
Published in:Applied sciences 2019-09, Vol.9 (17), p.3573
Main Authors: Li, Shuman, Yang, Wenjing, Xu, Liyang, Li, Chao
Format: Article
Language:English
Subjects:
Autonomous underwater vehicles
Computational fluid dynamics
Computer applications
Efficiency
Environmental conditions
Environmental perception
Error reduction
Experiments
Fish
Flow velocity
Fluid dynamics
Fluid mechanics
Hydrodynamics
Kinematics
Lateral line
Learning algorithms
Machine learning
Maneuverability
Neural networks
Numerical analysis
Polynomials
Position sensing
Prediction models
Pressure distribution
Robot control
robotic fish
Robotics
Robots
robots formation
Sensors
Simulation
Swimming
Task complexity
Underwater vehicles
Viscosity
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9173573