Leader-follower formation enabled by pressure sensing in free-swimming undulatory robotic fish

TL;DR

This paper demonstrates that a robotic fish can achieve leader-follower formation swimming by using pressure sensors and neural network control, mimicking fish lateral line sensing for effective navigation in fluid environments.

Contribution

It introduces a novel pressure sensing-based control method for robotic fish formation swimming, trained via imitation learning with minimal data.

Findings

Effective leader-follower tracking within 200 mm distance.

Successful use of pressure sensors and neural networks for navigation.

Achieved formation swimming with less than one hour of training.

Abstract

Fish use their lateral lines to sense flows and pressure gradients, enabling them to detect nearby objects and organisms. Towards replicating this capability, we demonstrated successful leader-follower formation swimming using flow pressure sensing in our undulatory robotic fish ($\mu$Bot/MUBot). The follower $\mu$Bot is equipped at its head with bilateral pressure sensors to detect signals excited by both its own and the leader's movements. First, using experiments with static formations between an undulating leader and a stationary follower, we determined the formation that resulted in strong pressure variations measured by the follower. This formation was then selected as the desired formation in free swimming for obtaining an expert policy. Next, a long short-term memory neural network was used as the control policy that maps the pressure signals along with the robot motor commands and the Euler angles (measured by the onboard IMU) to the steering command. The policy was trained to imitate the expert policy using behavior cloning and Dataset Aggregation (DAgger). The results show that with merely two bilateral pressure sensors and less than one hour of training data, the follower effectively tracked the leader within distances of up to 200 mm (= 1 body length) while swimming at speeds of 155 mm/s (= 0.8 body lengths/s). This work highlights the potential of fish-inspired robots to effectively navigate fluid environments and achieve formation swimming through the use of flow pressure feedback.