Maneuverability modeling and trajectory tracking for fish robot

Suebsaiprom, Pichet; Lin, Chong

doi:10.1016/j.conengprac.2015.08.010

Cited by 51 publications

(23 citation statements)

References 25 publications

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“…(39) represents the left boundary when a 0 ¼ a 0min and Eq. (40) represents the arc at the top when a a ¼ a amax . To implement the projection scheme, the following cases are considered: For case (A), the point to be projected is inside or on the boundary of the convex set U and no projection is needed.…”

Section: Path-following Control Algorithmmentioning

confidence: 99%

“…There has been additional work done on model-based closedloop motion control to achieve maneuvering or trajectory tracking [35][36][37][38][39][40][41]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. [40], three simplified linearized models of the decoupled fish dynamics were used for the design of linear quadratic regulators to achieve speed and orientation control and to stabilize the pitch and roll. Furthermore, a line-of-sight guidance scheme was implemented for way-point tracking.…”

Section: Introductionmentioning

confidence: 99%

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

Castaño

Tan

2019

Journal of Dynamic Systems, Measurement, and Control

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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.

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Section: Path-following Control Algorithmmentioning

confidence: 99%

“…There has been additional work done on model-based closedloop motion control to achieve maneuvering or trajectory tracking [35][36][37][38][39][40][41]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

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

Castaño

Tan

2019

Journal of Dynamic Systems, Measurement, and Control

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“…Carangiform locomotion, the most common type of BCF, exhibits significant swimming actions such as undulatory swimming motions, high speed performance, low noise, fast start, rapid turning and high accelerations. These specifications of Carangiform locomotion provide an appropriate solution for biomimetic design of AUVs [32][33][34][35].…”

Section: Design Procedures Of the Robotic Fish Prototypementioning

confidence: 99%

Mechatronic Design and Manufacturing of the Intelligent Robotic Fish for Bio-Inspired Swimming Modes

Korkmaz

Koca

et al. 2018

Electronics

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This paper presents mechatronic design and manufacturing of a biomimetic Carangiform-type autonomous robotic fish prototype (i-RoF) with two-link propulsive tail mechanism. For the design procedure, a multi-link biomimetic approach, which uses the physical characteristics of a real carp fish as its size and structure, is adapted. Appropriate body rate is determined according to swimming modes and tail oscillations of the carp. The prototype is composed of three main parts: an anterior rigid body, two-link propulsive tail mechanism, and flexible caudal fin. Prototype parts are produced with 3D-printing technology. In order to mimic fish-like robust swimming gaits, a biomimetic locomotion control structure based on Central Pattern Generator (CPG) is proposed. The designed unidirectional chained CPG network is inspired by the neural spinal cord of Lamprey, and it generates stable rhythmic oscillatory patterns. Also, a Center of Gravity (CoG) control mechanism is designed and located in the anterior rigid body to ensure three-dimensional swimming ability. With the help of this design, the characteristics of the robotic fish are performed with forward, turning, up-down and autonomous swimming motions in the experimental pool. Maximum forward speed of the robotic fish can reach 0.8516 BLs -1 and excellent three-dimensional swimming performance is obtained.

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“…Zhou et al derived the mathematic model of robotic fish by using Lagrange method [8]. Suebsaiprom and Lin produced two joint robotic fish based on carangiform motion and three dimensional trajectory tracking was implemented [9]. Wang et al derived dynamic model of one joint robotic fish and compared with experimental studies [10].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Model and Simulation of One Active Joint Robotic Fish

Akpolat

Bingöl²,

Ay³

et al. 2017

NWSA

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This study considers the dynamic model of one active joint robotic fish by using Lagrange method and simulation of the robotic fish model in MATLAB/SimMechanics environment. Compared results of these two different models are given in the study. The mathematical model of the system is derived from Lagrange energy equations of the robotic fish inspired from a real carangiform fish. The Computer Aided Design (CAD) model of the robotic fish is designed by using SolidWorks and it is transferred to the SimMechanics environment. The hydrodynamic effects, which are linear and nonlinear drag force, are also adapted and head motion, one active joint, and one passive joint angles found by using MATLAB Simulink environment. Obtained results for joint angles from both dynamic and SimMechanics models are compared and proved with animation video of the robotic fish.

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Maneuverability modeling and trajectory tracking for fish robot

Cited by 51 publications

References 25 publications

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

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

Mechatronic Design and Manufacturing of the Intelligent Robotic Fish for Bio-Inspired Swimming Modes

Dynamic Model and Simulation of One Active Joint Robotic Fish

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