Compared with traditional underwater vehicles, bio-inspired fish robots have the advantages of high efficiency, high maneuverability, low noise, and minor fluid disturbance. Therefore, they have gained an increasing research interest, which has led to a great deal of remarkable progress theoretically and practically in recent years. In this review, we first highlight our enhanced scientific understanding of bio-inspired propulsion and sensing underwater and then present the research progress and performance characteristics of different bio-inspired robot fish, classified by the propulsion method. Like the natural fish species they imitate, different types of bionic fish have different morphological structures and distinctive hydrodynamic properties. In addition, we select two pioneering directions about soft robotic control and multi-phase robotics. The hybrid dynamic control of soft robotic systems combines the accuracy of model-based control and the efficiency of model-free control, and is considered the proper way to optimize the classical control model with the intersection of multiple machine learning algorithms. Multi-phase robots provide a broader scope of application compared to ordinary bionic robot fish, with the ability of operating in air or on land outside the fluid. By introducing recent progress in related fields, we summarize the advantages and challenges of soft robotic control and multi-phase robotics, guiding the further development of bionic aquatic robots.

Recent research on robotic fish mainly focused on the bionic structure design and realizing the movement with smart materials. Although many robotic fish have been proposed, most of these works were oriented toward shallow water environments and are mostly built with purely rigid structures, limiting the mobility and practical usability of robotic fish. Inspired by the stability of the real manta ray, a manta ray robot design is proposed with soft material made flapping wing based on an open-source ROV (Remotely Operated Vehicle). The flapping wing structure with three different materials mimics the wide pectoral fins of real manta rays, which have bones, muscles, and skin. Furthermore, its modular design makes it easy to install and disassemble. The kinematic and hydrodynamic analysis of the manta ray robot are simulated in this paper. The actual manta ray robot is fabricated and several sets of test are performed in the pool. The robot can swim forward continually and stably with a simple rolling and pitching pattern.

The rapid advancement of underwater wireless communication technologies is critical to unlocking the full potential of marine resource exploration and environmental monitoring. This paper reviews recent progress in three primary modalities: underwater acoustic communication, radio frequency (RF) communication, and underwater optical wireless communication (UWOC), each designed to address specific challenges posed by complex underwater environments. Acoustic communication, while effective for long-range transmission, is constrained by ambient noise and high latency; recent innovations in noise reduction and data rate enhancement have notably improved its reliability. RF communication offers high-speed, short-range capabilities in shallow waters, but still faces challenges in hardware miniaturization and accurate channel modeling. UWOC has emerged as a promising solution, enabling multi-gigabit data rates over medium distances through advanced modulation techniques and turbulence mitigation. Additionally, bio-inspired approaches such as electric field communication provide energy-efficient and robust alternatives under turbid conditions. This paper further examines the practical integration of these technologies in underwater platforms, including autonomous underwater vehicles (AUVs), highlighting trade-offs between energy efficiency, system complexity, and communication performance. By synthesizing recent advancements, this review outlines the advantages and limitations of current underwater communication methods and their real-world applications, offering insights to guide the future development of underwater communication systems for robotic and vehicular platforms.

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Fish-inspired motion is an important research area with many applications in real-world tasks such as underwater vehicles or robotic fish control design. Owing to robust, smooth, and coordinated oscillatory signals generated by Central Pattern Generators (CPGs) for locomotion control of robots with multiple degrees of freedom, CPGs are the most versatile solution for robotic control systems, especially in robotic fish. However, tuning central pattern generator parameters is difficult for complex mechanical system designs. Besides, most current CPG-based methods only consider one aspect (e.g., speed), which widens the gap between theory and practice in robotic fish design. Also, it may affect the practical applicability of the designed motion model to a certain extent. This paper addresses this problem by constructing a multi-objective evolutionary design of a central pattern generator network to control the proposed biomimetic robotic fish. A new CPG model is proposed to help biomimetic robotic fish swim efficiently. In addition, an efficient multi-objective evolutionary algorithm proposed in our previous work is also applied to assist the biomimetic robotic fish in obtaining faster-swimming speed, good stability of the head, and higher propulsive efficiency simultaneously. Considering that the result of multi-objective optimization is a set of non-dominated solutions rather than a solution, a screening method based on fuzzy theory is adopted to assist decision-makers in selecting the most appropriate solution. Based on this, the control model of biomimetic robotic fish is constructed. The proposed control model is simulated and compared with seven well-known algorithms and a series of robotic fish designs. After that, the proposed control model is validated with extensive experiments on the actual biomimetic robotic fish. Simulations and experiments demonstrate the proposed control model’s effectiveness and good performance, especially when the control model has been applied to the real biomimetic robotic fish.

Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed algorithm with the aim of enhancing the exploration and exploitation abilities of DMOEOA. To demonstrate the advantages of the proposed algorithm, various benchmarks have been selected with five different multiobjective optimization algorithms. The test results indicate that DMOEOA does exhibit better performances in these problems with a better balance between convergence and distribution. In addition, the new proposed algorithm is applied to the structural optimization of an elastic truss with the other five existing multiobjective optimization algorithms. The obtained results demonstrate that DMOEOA is not only an algorithm with good performance for benchmark problems but is also expected to have a wide application in real-world engineering optimization problems.