Robotic Fish Research Articles

A dynamic model for underwater robotic fish with a servo actuated pectoral fin

The main objective of this work is to mimic and model pectoral fins based locomotion mechanisms observed in various labriform fishes like box-fish, angel fish, etc. accounting the previous problems using rigid joint connection for fins. The hydrodynamic forces acting on the pectoral fin are evaluated for a quasi-static state using blade element approach while taking into account the added mass. Implementing Newton–Euler equations, the kinematics and the dynamics of a free-swimming robot-fish are obtained. Thus, an analytical model is derived to calculate net forces and torques generated throughout the gait. Apart from a parametric study, simulations are performed to predict various output parameters like thrust, acceleration and swimming speed for some specific locomotion. For experimental validation, a rigid fish-like-body is fabricated with a pair of fins attached symmetrically on lateral sides of the body. Each fin is actuated using three servo-motors in [Ry Rz Ry] configurations to exhibit both the rowing and flapping gaits. A micro-controller is deployed to control the actuators by generating pulse-width modulation and read inertial sensors (a gyroscope and an accelerometer mounted at the centroid of the body) using I2C protocol. In addition, a Bluetooth module is also attached to it for communicating the sensor’s readouts to a computer (running MATLAB script). To achieve some specific motions of rowing gait, the fins are actuated appropriately while the robot is freely submerged underwater and the sensor’s data are recorded in real-time for parallel observations. Based on video motion analysis and the sensed data, the values of acceleration are calculated and compared with the theoretical values predicted by the model.

Enhanced Fireworks Algorithm-Auto Disturbance Rejection Control Algorithm for Robot Fish Path Tracking

The robot fish is affected by many unknown internal and external interference factors when it performs path tracking in unknown waters. It was proposed that a path tracking method based on the EFWA-ADRC (enhanced fireworks algorithmauto disturbance rejection control) to obtain high-quality tracking effect. ADRC has strong adaptability and robustness. It is an effective method to solve the control problems of nonlinearity, uncertainty, strong interference, strong coupling and large time lag. For the optimization of parameters in ADRC, the enhanced fireworks algorithm (EFWA) is used for online adjustment. It is to improve the anti-interference of the robot fish in the path tracking process. The multi-joint bionic robot fish was taken as the research object in the paper. It was established a path tracking error model in the Serret-Frenet coordinate system combining the mathematical model of robotic fish. It was focused on the forward speed and steering speed control rate. It was constructed that the EFWA-ADRC based path tracking system. Finally, the simulation and experimental results show that the control method based on EFWAADRC and conventional ADRC makes the robotic fish track the given path at 2:8s and 3:3s respectively, and the tracking error is kept within plus or minus 0:09m and 0:1m respectively. The new control method tracking steady-state error was reduces by 10% compared with the conventional ADRC. It was proved that the proposed EFWA-ADRC controller has better control effect on the controlled system, which is subject to strong interference.

Bottom-level motion control for robotic fish to swim in groups: modeling and experiments

Moving in groups is an amazing spectacle of collective behaviour in fish and has attracted considerable interest from many fields, including biology, physics and engineering. Although robotic fish have been well studied, including algorithms to simulate group swimming, experiments that demonstrate multiple robotic fish as a stable group are yet to be achieved. One of the challenges is the lack of a robust bottom-level motion control system for robotic fish platforms. Here we seek to overcome this challenge by focusing on the design and implementation of a motion controller for robotic fish that allows multiple individuals to swim in groups. As direction control is essential in motion control, we first propose a high-accuracy controller which can control a sub-carangiform robotic fish from one arbitrary position/pose (position and direction) to another. We then develop a hydrodynamic-model-based simulation platform to expedite the process of the parameter tuning of the controller. The accuracy of the simulation platform was assessed by comparing the results from experiments on a robotic fish using speeding and turning tests. Subsequently, extensive simulations and experiments with robotic fish were used to verify the accuracy and robustness of the bottom-level motion control. Finally, we demonstrate the efficacy of our controller by implementing group swimming using three robotic fish swimming freely in prescribed trajectories. Although the fluid environment can be complex during group swimming, our bottom-level motion control remained nominally accurate and robust. This motion control strategy lays a solid foundation for further studies of group swimming with multiple robotic fish.

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot’s previously recorded speeds.

Swimming Performance of a Tensegrity Robotic Fish.

We described a tensegrity robotic fish and detailed its overall structure, stiffness, and mechatronics. The main flexible structure of the robotic fish body was composed with a series of rigid segments linked with tensegrity joints by means of tension elements. Each rigid segment can rotate around tensegrity-compliant joint and have no direct contact with each other. The dominant vibrational mode of the tensegrity robotic fish can be excited by a single harmonic input to mimic the desired kinematics of locomotion. For our tensegrity robotic fish, the experimental results showed that its maximum stride length was about 0.5 body length per cycle; its Strouhal number was roughly between 0.45 and 0.55 near the biological data of carangiform swimmers. Two different vibrational modes that can be achieved would be demonstrated by the harmonic analysis technique. The results indicated that the swimming performance can be improved by using tensegrity joints.

Model Predictive Control-Based Path-Following for Tail-Actuated Robotic Fish

There has been an increasing interest in the use of autonomous underwater robots to monitor freshwater and marine environments. In particular, robots that propel and maneuver themselves like fish, often known as robotic fish, have emerged as mobile sensing platforms for aquatic environments. Highly nonlinear and often under-actuated dynamics of robotic fish present significant challenges in control of these robots. In this work, we propose a nonlinear model predictive control (NMPC) approach to path-following of a tail-actuated robotic fish that accommodates the nonlinear dynamics and actuation constraints while minimizing the control effort. Considering the cyclic nature of tail actuation, the control design is based on an averaged dynamic model, where the hydrodynamic force generated by tail beating is captured using Lighthill's large-amplitude elongated-body theory. A computationally efficient approach is developed to identify the model parameters based on the measured swimming and turning data for the robot. With the tail beat frequency fixed, the bias and amplitude of the tail oscillation are treated as physical variables to be manipulated, which are related to the control inputs via a nonlinear map. A control projection method is introduced to accommodate the sector-shaped constraints of the control inputs while minimizing the optimization complexity in solving the NMPC problem. Both simulation and experimental results support the efficacy of the proposed approach. In particular, the advantages of the control projection method are shown via comparison with alternative approaches.

Master-Slave Collaboration System of Unmanned Surface Vehicle and Robotic Fish

A brief introduction to the master-slave collaboration system of unmanned surface vehicle (USV) and robotic fish, which consists of an upper computer, master and slave is given in this paper. The upper computer set in the control center communicates with the master-slave through the wireless communication network. The master is set on USV. The slave is set on each robotic fish. Because the system adopts the USV as an intermediate station for signal transfer. The underwater detector, namely robotic fish, detect the signal and pass it to the upper machine by the USV, which enhances the power and increases the distance of signal transmission, improves the signal stability and the radius of operation activities of the whole robotic fish due to the close distance between fish and boats so that they can carry out real-time communication.

Robotic Fish Propelled by a Servo Motor and Ionic Polymer-Metal Composite Hybrid Tail

In this paper, a new robotic fish propelled by a hybrid tail, which is actuated by two active joints, is developed. The first joint is driven by a servo motor, which generates flapping motions for main propulsion. The second joint is actuated by a soft actuator, an ionic polymer-metal composite (IPMC) artificial muscle, which directs the propelled fluid for steering. A state-space dynamic model is developed to capture the two-dimensional (2D) motion dynamics of the robotic fish. The model fully captures the actuation dynamics of the IPMC soft actuator, two-link tail motion dynamics, and body motion dynamics. Experimental results have shown that the robotic fish is capable of swimming forward (up to 0.45 body length/s) and turning left and right (up to 40 deg/s) with a small turning radius (less than half a body length). Finally, the dynamic model has been validated with experimental data, in terms of steady-state forward speed and turning speed at steady-state versus flapping frequency.

Central Pattern Generator (CPG) Control of a Biomimetic Robot Fish for Multimodal Swimming

This paper introduces the design and control of a biomimetic robot fish for multimodal swimming. The biomimetic design consists of three parts: the rigid head, the wire-driven body and the compliant tail. The control is an improved Central Pattern Generator (CPG) with the high-level control command: (M, ω, B, R), where M is the amplitude, ω is the angular velocity, B is the offset and R is the time ratio between two phases forming one flapping cycle. This method differs from previous research in two aspects: (1) The CPG control is firstly implemented on the wire-driven robot fish. (2) The improved CPG model synthesizes symmetrical flapping in cruising and asymmetrical flapping in turning for the robot fish. The asymmetrical flapping refers to the asymmetry of the offset and the time ratio. This combination of the design and the control has several advantages over the existing multimodal swimming robot fishes. First, it uses just one driving motor for undulatory oscillation while the others need to use two or more motors. Second, with just one motor, the CPG control can be easily implemented. Third, the use of the time ratio, R, makes the robot fish turn more naturally and effectively. Experimental results show the robot fish achieved the maximum speed of 1.37 Body Length/Second (BL·s-1) and the largest turning rate of 457°/s. Additionally, in many swimming conditions, its Strouhal Number falls in the range from 0.2 to 0.4, which implies the robot fish is efficient.

A complete modeling for fish robots with actuators

PurposeFor precisely presenting the swimming behavior of fish robots underwater and the practical implementation purpose, this paper aims to investigate a well-formulated fish robot model which integrates the nonlinear rigid body dynamics, kinematics and models of actuators.Design/methodology/approachThis fish robot model is mainly built up by three basic parts: a balance mechanism, a four-links vibrator and a caudal fin. In the fish robot’s head, there is a balance mechanism used to control the rotations in pitch and roll directions of the fish robot by moving two movable masses. The four-links vibrator with three active joints actuated by DC motors is designed to vibrate the fish’s body. In the end of the fish robot body, a caudal fin which connects with the passive joint is developed to generate hydrodynamic thrust forces to propel the fish robot.FindingsFrom the real stability tests and control verification, it is obvious that this proposed model can precisely present the swimming behavior of fish robots and possesses the potential to develop a fish-like robotic prototype.Originality/valueA well-formulated model with dynamics of actuators is integrated for presenting the swimming behavior of carangiform locomotion type fish robots in this investigation. From the simulation results and the practical test of a real fish robot, the feasibility of this proposed model for building up real fish robots can be proven, and this proposed model is accurate enough to effectively present the swimming behavior of fish robots.

Efficient thrust generation in robotic fish caudal fins using policy search

Thrust generation is a crucial aspect of fish locomotion that depends on a variety of morphological and kinematic parameters. In this work, the kinematics of caudal fin motion of a robotic fish are optimised experimentally. The robotic fish actuates its caudal fin with flapping and rotation motion, and also measures the fin hydrodynamic force and torque. Total nine designs of the caudal fins are investigated, with three different shapes (or inclination angles) and three stiffness. The optimisation is based on a policy search (PS) algorithm, which is used to maximise the thrust-generation efficiency of the caudal fins. The authors first parametrise fin spanwise-rotation as a sinusoidal function using rotation amplitude and phase delay and test whether it is beneficial to thrust-generation efficiency. The result shows that the rotation does not contribute to the efficiency, as the efficiency is maximised at zero amplitude. Next, the authors optimise flapping amplitude and trajectory profile without fin rotation. Results show that smaller flapping amplitude results in higher efficiency and linear flapping trajectories are preferred over sinusoidal ones. Fins that have the highest flexibility are more efficient in thrust generation although they generate less thrust, while an inclination angle of 30° yields the most efficient fin shape.

Control and Optimization of a Bionic Robotic Fish Through a Combination of CPG model and PSO

Swimming speed and propulsive efficiency are critical indicators of a self-propelled bionic robotic fish in terms of swimming performance. This paper is devoted to achieving a maximum swimming speed and a higher propulsive efficiency for a four-joint robotic fish. A Newton–Euler method based dynamic model in conjunction with a central pattern generator (CPG) network serves to theoretically estimate fish-like swimming performance. In particular, each CPG unit based on limit cycles serves to drive one joint of the robotic fish. The overall CPG model is able to generate rhythmic signals for multimode swimming. To obtain the maximum average speed, a particle swarm optimization (PSO) algorithm is further utilized to optimize the feature parameters of the CPG model. Furthermore, the higher propulsive efficiency is sought within the same control framework. Simulations and experiments on the actual robotic fish demonstrate the improved propulsive performance and the effectiveness of the proposed control framework.

Development of a numerical approach for the prediction of thrust generated by a bio-mimetic propulsion system

ABSTRACTThis paper presents a numerical approach for the prediction of thrust generated by a fish-type biomimetic propulsion system. We model the fish as a non-uniform Timoshenko Beam (TB) with varying Young’s modulus and moment of inertia along its length from head to tail. The non-uniform Timoshenko beam is assumed to be a cantilever with the fixed end at the head and free end at the tail and the corresponding mode shapes are determined. Based on the mode shapes, the thrust is calculated with respect to the input parameters such as wave velocity, propulsive velocity and wavelength. The method developed here is shown to be useful for the design of bio-inspired propulsive systems for marine vehicles and also for gaining a better understanding propulsive mechanics of a robotic fish. In results, the thrust delivered by the simple flapping beam is identified and the relationship between the non-dimensional frequencies is determined.

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

To achieve fast and accurate adjustment of robotic fish, this paper proposes state prediction model based on the extreme learning machine optimized by particle swarm algorithm. The proposed model can select desirable actions for robotic fish according to precisely predicted states, “adjusting position” or “pushing ball” defined herein. Specifically, the extreme learning machine (ELM) is leveraged to predict the state of robotic fish, from the observations of current surrounding environment. As the outputs in ELM are varying with the randomly initialized parameters, particle swarm optimization (PSO) algorithm further improves the accuracy and robustness of the ELM by optimizing initial parameters. The empirical results on URWPGSim2D simulation platform indicate that the robotic fish tends to carry out appropriate actions using the state prediction model so that we can complete the game efficiently. It proves that the proposed model can make best use of the real‐time information of robotic fish and water polo and derive fulfilling action strategy in various scenarios, which meet the requirements of motion control for robotic fish.

Small Robotic Fish with Two Magnetic Actuators for Autonomous Tracking of a Goldfish

Small Robotic Fish with Two Magnetic Actuators for Autonomous Tracking of a Goldfish

Developmen of The Tuna Modeled Oscillation Wing Propulsion Type Fish Robot

Developmen of The Tuna Modeled Oscillation Wing Propulsion Type Fish Robot

Design and Fabrication and Hydrodynamic Analysis of a Fish Robot for Underwater Surveillance

Design and Fabrication and Hydrodynamic Analysis of a Fish Robot for Underwater Surveillance

Multi-objective optimization of the kinematic parameters of fish-like swimming using a genetic algorithm method

This paper investigates the kinematic optimization of fish-like swimming. First, an experiment was performed to detect the motion of the fish tail foil of a fish robot. Next, the kinematic swimming model was verified experimentally using an image processing method. The model includes two rotational motions: caudal foil motion and foil-pitching motion. The kinematic model allows us to evaluate the influence of motion trajectory in the optimization process. To optimize the propulsive efficiency and thrust, a multi-objective genetic algorithm was employed to handle with kinematic, hydrodynamic, and propulsion models. The results show that the caudal length has a significant effect on the performance of the flapping foil in fish-like swimming, and its influence on the motion trajectory may increase the propulsive efficiency to as high as 98% in ideal conditions. The maximum thrust coefficient can also reach approximately 3 in ideal conditions.

Design and motion testing of a Multiple SMA fin driven BIUV

Fishes interact with the fluid environment using various surfaces. These multiple control surfaces work in combination to produce the thrust and the balance torques in a steady swimming, to maneuver and to position themselves accurately even in turbulent flows. These motivate us to embark on a research program designed to develop an agile biologically inspired robotic fish based on the performance of multiple fins. To accomplish this goal, a mechanical ray-like fin actuated by the shape memory alloy (SMA) is developed, which can realize the oscillating motion, the undulating motion or even the complex three dimensional motion. The basic unit is the two opposite side SMA-driven plate, namely the fin ray. As a result, a lightweight bio-inspired fin is constructed by placing radially multiple SMA fin rays. A biologically inspired underwater vehicle (BIUV) is later built using the above multiple lightweight bio-inspired fins. Two common arrangement styles of multiple fins on the BIUV are considered here: one is the posterior fin (for the oscillating motion) that is parallel to the anterior fins (for the undulating motion), another one is the posterior fin that perpendicular to the anterior fins. The kinematic modeling, the deformation modeling and the detecting of the SMA fin are made. The thrust generation is also established. Finally, an experiment is conducted to test the performance of the proposed two arrangement styles, including the comparison of the averaged propulsion velocity and the averaged thrust under certain kinematic parameters. Meanwhile, the influence of the frequency and the amplitude of the SMA fin ray on the propulsion performance is also investigated.

Trajectory Planning Strategy for Robotic Water Polo Matches

An area partition-based ball-pushing control strategy is proposed for the real robot water polo events since the multi-joint robot fishes often play the edge ball or are unable to touch the ball in their attempt of pushing the ball. As a first step, the venue image acquired is partitioned into several areas for separate implementation of different control options regarding the position judgment of ball and fishes and the ball-pushing strategy. According to the line connecting the ball to the goal midpoint, the area around the ball is divided into the ball-pushing area and the non-ball-pushing area, and a temporary point is designed for the ball-pushing area. The robot fishes are set to hit the ball when they are in the ball-pushing area; and when they are in the non-ball-pushing area, the fishes are driven into the ball-pushing area via the temporary point to hit the ball. The experimental results show that the use of this method allows the robot fishes in the water to push the target ball to the goal more quickly and accurately. Aside from the 2V2 water polo competition, the proposed method is also applicable to the N V N water polo competition, and even robot path planning.