# An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
## 1 Introduction  
In today's world, Autonomous Underwater Vehicles (AUVs) are implemented 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 conventional AUVs 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]. A single robot with multiple capabilities cannot necessarily accomplish a complicated task successfully, whereas a robot swarm in which each robot has its own functions, can be more flexible, robust, and cost-effective and do complex tasks [10]. Recent studies [11; 12; 13; 14; 15] show that robotic fish formation can improve swimming efficiency. However, 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 [16; 1]. Fish have a unique sensory system, lateral line system (LLS), which helps them sense fluid changes and the spacing between themselves and other objects. It has a great significance forpredation, avoiding enemies, breeding and formation swimming [17; 18; 19; 20]. In recent years, researchers try to incorporate LLS into engineering applications as an alternative method for sensing the fluid environment around AUV. [PERSON] et al. [21] adopted laterally distributed parallel pressure sensor arrays and presented methods for the analysis of the surrounding steady and unsteady flows to provide a simplistic mimic of the fish's posterior lateral line organ, which can extract control-related information. [PERSON] et al. [22] used pressure sensors to detect flow parameters (flow speed, vortex shedding frequency, and cylinder diameter), and the results provide insight into the sensory ecology of schools of fish and give implications for the design of autonomous underwater vehicles. Inspired by the lateral line of fish, [PERSON] et al. [23] proposed a new kind of near-field detection system to be applied to detect impending walls, approaching obstacles and other target objects. [PERSON] et al. [24] could detect the moving objects located in front of the sensor by an artificial lateral line flow sensor. Our work is inspired by the LLS of fish and we try to construct the predictive model of flow velocity and the judgement model of spacing for robotic fish formation based on the monitoring sensors attached on the robotic fish surface. To get enough monitoring data for analysis, a large number of experiments should be conducted with different physical conditions. At the same time, eigenvalues should be sorted out to improve the data quality. In traditional physics experiments, the data monitored by sensors are difficult to collect, and the errors caused by instruments and materials are inevitable. In addition, some specific conditions are difficult to achieve in real physical experiments. Therefore, we use the numerical simulation method based on Computational Fluid Dynamics (CFD) in this work. [PERSON] et al. [25] used CFD methods to investigate the hydrodynamics of bio-inspired fish swimming, especially the spatial distribution and temporal variation of the near-body pressure of fish are studied.