We propose distributed control laws for a group of anonymous mobile agents to form desired circle formations when the agents move in the one-dimensional space of a circle. The agents are modeled by kinematic points. They share the common knowledge of the orientation of the circle, but are oblivious and anonymous. Moreover, each agent can only sense the relative positions of its neighboring two agents that are immediately in front of or behind itself. Distributed control strategies are designed for the agents using only the information of the relative positions of their two neighbors and also the given desired distances to its neighboring two agents. To make the control strategies more practical, we discuss the corresponding sampled-data control laws, and utilizing the technique of adopting time-varying gains, we obtain control laws that are able to guide the agents to form the desired circle formation within any given finite time. One feature of the proposed control laws is that they guarantee that the spatial ordering of the agents are preserved throughout the system's evolution, and thus no collision may take place during the process of forming circle formations. Both theoretical analysis and numerical simulations are given to show the effectiveness of the proposed formation control strategies.

This paper is concerned with the coordination control of multiple biomimetic robotic fish in highly dynamic aquatic environments by building a hybrid centralized system. With the aid of the results of biorobotics and control techniques, a radio-controlled multijoint robotic fish and its locomotion control are developed. To enable a closed control loop, a visual subsystem that is responsible for tracking of multiple moving objects is constructed and implemented in real time. Furthermore, a behavior-based hierarchical architecture in conjunction with fuzzy reinforcement learning is proposed to accomplish effective coordination among multiple swimming robots. Finally, experiments on 2vs2 water polo game are carried out to verify the proposed coordination control scheme. Over the past eight years, this multirobot platform has been successfully applied to international underwater robot competitions to promote innovative research and education in underwater robotics.

The lateral line system (LLS) is a mechanoreceptive organ system with which fish and aquatic amphibians can effectively sense the surrounding flow field. The reverse Kármán vortex street (KVS), known to be a typical thrust-producing wake, is commonly observed in fish-like locomotion and is known to be generated by fish's tails. The vortex street generally reflects the motion information of the fish. A fish can use LLS to detect such vortex streets generated by its neighboring fish, thus sensing its own state and the states of its neighbors in a school of fish. Inspired by this typical biological phenomenon, we design a robotic fish with an onboard artificial lateral line system (ALLS) composed of pressure sensor arrays and use it to detect the reverse KVS-like vortex wake generated by its adjacent robotic fish. Specifically, the vortex wake results in hydrodynamic pressure variations (HPVs) in the flow field. By measuring the HPV using the ALLS and extracting meaningful information from the pressure sensor readings, the oscillating frequency/amplitude/offset of the adjacent robotic fish, the relative vertical distance and the relative yaw/pitch/roll angle between the robotic fish and its neighbor are sensed efficiently. This work investigates the hydrodynamic characteristics of the reverse KVS-like vortex wake using an ALLS. Furthermore, this work demonstrates the effectiveness and practicability of an artificial lateral line in local sensing for adjacent underwater robots, indicating the potential to improve close-range interaction and cooperation within a group of underwater vehicles through the application of ALLSs in the near future.

The fish-like robot is one of the typical underwater robots, which has the advantage of high maneuverability with low noise due to its bioinspired structure and biomimetic locomotion. However, it is challenging to efficiently design motion controllers for such robots to achieve satisfactory performance on specific control tasks in the real underwater environment, since the complex fluid-structure interaction exists during their swimming and exact dynamic models are absent. In this article, we propose a learning framework, incorporating a simulation system and a training methodology, to autonomously and fast train in simulation to create control policies that are capable of directly applying to a type of physical fish-like robots to perform motion control tasks. First, we construct a simulation system combining a data-driven environment and a computational fluid dynamics (CFD)-based environment, thus well balancing the simulation accuracy and the calculation speed. Second, we design a training methodology to train deep reinforcement learning (DRL)-based policies for the robot in our constructed simulation system to perform a specific control task. Then, we use two typical motion control tasks to verify our proposed framework. One is the path-following control task, which is a one-objective problem with dense rewards, while the other is the pose control task which is a two-objective problem with sparse rewards. For each task, the DRL-based control policy trained by our learning framework is directly deployed on the physical fish-like robot to perform the task in the real world. Experimental results show that the policies trained in simulation still work well in the real world, and perform even better in terms of control accuracy and stability compared with the traditional control methods, thus demonstrating the effectiveness of our learning framework.

Controlling biomimetic underwater robots in unknown flow fields remains a challenge due to the strong nonlinearity of the fluid. This article investigates the attitude holding task of a robotic fish swimming in reality. Such a typical sensing-based control task requires the fish to keep a desired angle of attack in an unknown and even varied incoming flow. To this end, we propose a learning-based approach by using a deep neural network directly maps the raw data of sensors equipped on the robot to the continuous control signals in an end-to-end manner. First, based on experimental data of the physical robot, we construct a data-driven simulation environment including three modules of dynamic, sensor, and control. The dynamic and sensor modules are established to model the dynamics of the fish and to generate its sensors’ data, based on which a deep reinforcement learning (DRL) algorithm in the control module is trained to get a control policy. Then, we directly deploy the trained policy to a physical robotic fish for attitude holding task. Experimental results demonstrate the robustness and effectiveness of the DRL policy and, thus, verify the success of our approach to achieving sim-to-real transfer.