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

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.

Review of Computational Fluid Dynamics Analysis in Biomimetic Applications for Underwater Vehicles

“…The exceptional propulsion performance and agile maneuverability of fish undoubtedly arouse interest in biomimetic robotic fish research. These biomimetic robotic fish play a crucial role in the design and analysis of underwater propulsion devices, prompting academia to intensify research and discussion on this subject [2,[86][87][88][89]. Particularly for the application in small AUVs, biomimetic underwater robotic fish propulsion devices hold immense potential and could be extensively exploited and utilized in future scientific research.…”