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

Shuman Li,Wenjing Yang,Liyang Xu,Chao Li

doi:10.3390/app9173573

Shuman Li, Wenjing Yang + Show 2 more

Open Access

https://doi.org/10.3390/app9173573

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Journal: Applied sciences	Publication Date: Sep 1, 2019
Citations: 7	License type: CC BY 4.0

Affiliation: National University of Defense Technology

Abstract

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.

Highlights

In today’s world, Autonomous Underwater Vehicles (AUVs) are implementedor a variety of tasks, including ocean surveys, mine clearance and data collection in the ocean and river environments [1].It is a well known fact that, in nature, the fish propels itself by the coordinate motion of its body, fins, and tail, achieving higher propulsive efficiency and better maneuverability than the conventionalAUVs powered by rotary propellers with the same power consumption [2]
Through analyzing the contours in pressure fields of two fishes in the phalanx formation shown in Figure 13, we found that the pressure generated by the swing of fish body will affect the adjacent fish and the flow field on both sides of the fish body are not symmetrical
We construct the models for single robotic fish, the tandem formation and the phalanx formation to predict the inlet flow velocity and judge the spacing between individual platforms in the formation

Summary

Introduction

In today’s world, Autonomous Underwater Vehicles (AUVs) are implementedor a variety of tasks, including ocean surveys, mine clearance and data collection in the ocean and river environments [1].It is a well known fact that, in nature, the fish propels itself by the coordinate motion of its body, fins, and tail, achieving higher propulsive efficiency and better maneuverability than the conventionalAUVs powered by rotary propellers with the same power consumption [2]. To improve the efficiency of AUVs, more research studies are performed on application, design, and control of underwater robotic fish in recent years [3,4,5,6,7,8,9]. In the underwater environment, the velocity and viscosity of fluids, the complex geometry environment condition and even the interaction between robots can affect the stability of underwater robots formation. It is necessary for a robotic fish in a formation to get basic environmental information around itself, such as the inlet flow velocity and the spacing between itself and other robotic fish, which are useful for stable formation. With an underwater robot formation, unlike air or land robots, it is difficult to obtain the information through traditional means such as communication, GPS, and acoustic or visual systems [1,16]

Methods

Results

Conclusion