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$200 \text{mm}(=1$body length) while swimming at speeds of$155 \text{mm} / \mathrm{s}(=0.8$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. Video—https://youtu.be/DIDYGi9Td0I

Here we present the development of an autonomous modular swimming robot. This robot, named µBot 2.0, was upgraded from our previous robot platform µBot and features onboard computing, sensing, and power. Its compact size and modularity render the robot an ideal platform for studying bio-inspired robot swimming. The robot is equipped with a micro controller in its head that communicates with external computers through Bluetooth Low Energy (BLE) and sends motor commands to the body segments via Inter-Integrated Circuit (I2C) protocol. Each body segment has a customized printed circuit board (PCB) that receives commands and controls the electromagnetic actuator for generating body movements. The robot head is also equipped with an Inertial Measurement Unit (IMU) to measure its heading and a battery for power. In this work, a µBot 2.0 with three actuators was assembled and the swimming performance was tested. The robot actuators were activated via rhythmic motor input from a central pattern generator (CPG). Experimental results showed that the swimming speed was highly sensitive to the frequency of the motor input, with a maximum swimming speed of 130 mm/s (equivalent to 0.7 body length per second) at 6 Hz. The robot also had the capability to correct its heading with IMU feedback and follow desired paths using a line-of-sight (LOS) guidance law with an overhead camera. Our results demonstrate the effectiveness of the robot's design and its potential in a variety of aquatic applications.