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

Wang, Ming; Dong, Huifang; Li, Xu; Zhang, Yanlu; Yu, Junzhi

doi:10.1016/j.neucom.2019.01.062

Cited by 49 publications

(19 citation statements)

References 19 publications

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“…Zhong et al ( 2017 ) transformed phase signal from CPG output into a trajectory signal for the legs of a six-legged robot by adjusting it. Wang et al ( 2019 ) proposed a gait planning method based on a reactive neuromuscular controller and CPG to achieve a power-saving human-like large walk, and the controller parameters were optimized based on an optimization algorithm in the paper. Common optimization algorithms such as central force optimization (CFO) and genetic algorithm (GA) have also been successfully used for the gait planning of humanoid robots.…”

Section: Introductionmentioning

confidence: 99%

Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm

et al. 2021

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To improve the fast and stable walking ability of a humanoid robot, this paper proposes a gait optimization method based on a parallel comprehensive learning particle swarm optimizer (PCLPSO). Firstly, the key parameters affecting the walking gait of the humanoid robot are selected based on the natural zero-moment point trajectory planning method. Secondly, by changing the slave group structure of the PCLPSO algorithm, the gait training task is decomposed, and a parallel distributed multi-robot gait training environment based on RoboCup3D is built to automatically optimize the speed and stability of bipedal robot walking. Finally, a layered learning approach is used to optimize the turning ability of the humanoid robot. The experimental results show that the PCLPSO algorithm achieves a quickly optimal solution, and the humanoid robot optimized possesses a fast and steady gait and flexible steering ability.

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Section: Introductionmentioning

confidence: 99%

Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm

et al. 2021

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“…Wang and Tan [ 6 ] studied the development of the mechanical structure of robotic fish, while Wang et al [ 7 ] examined their propulsion using a drag coefficient. Vo et al [ 8 ] studied the coefficient of propulsion of robotic fish with towing; and Wang et al [ 9 ] optimized a control strategy to study the advancement of robotic fish. Further, researchers have drawn upon the advantages of dolphins to improve the performance of underwater robots.…”

Section: Introductionmentioning

confidence: 99%

The Research of Maneuverability Modeling and Environmental Monitoring Based on a Robotic Dolphin

Xue

Song³

2021

Applied Bionics and Biomechanics

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In this study, the C-turning, pitching, and flapping propulsion of a robotic dolphin during locomotion were explored. Considering the swimming action required of a three-dimensional (3D) robotic dolphin in the ocean, we propose a maneuverability model that can be applied to the flapping motion to provide precise and stable movements and function as the driving role in locomotion. Additionally, an added tail joint allows for the turning movement with efficient parameters obtained by a fluid-structure coupling method. To obtain a mathematical model, several disturbance signals were considered, including systematic uncertainties of the parameters, the perpetually changing environment, the interference from obstacles with effective fuzzy rules, and a sliding mode of control. Furthermore, a combined strategy of environment recognition was used for the positional control of the robotic dolphin, incorporating sonar, path planning with an artificial potential field, and trajectory tracking. The simulation results show satisfactory performance of the 3D robotic dolphin with respect to flexible movement and trajectory tracking under the observed interference factors.

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“…Consequently, nonlinear feedback controllers are proposed to imitate the natural oscillation of the body, which results in a prescribed average velocity. The work in [18] introduced a particle swarm optimization (PSO) algorithm to optimize the characteristic parameters of the CPG model to obtain the maximum average speed and improve efficiency. In addition, research [19] has been conducted on the control of pectoral fins to improve propulsion efficiency.…”

Section: Introductionmentioning

confidence: 99%

High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology

et al. 2021

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Improving propulsion efficiency holds the promise of enabling the robotic fish to work for a long time with a limited battery in its small body. In this paper, for the swimming of a bionic robotic fish, we present a virtual musculoskeletal control method from the bionic model of the joint driven by agonist muscle and antagonist muscle. A closed-loop method composed of two loops is proposed as a rule of thumb for the speed control of the robotic fish. The outer loop adjusts the swimming speed using the speed deviation; the inner loop regulates the stiffness according to the virtual muscle spindle feedback to fit the water environment. Compared with the proportion control, the evaluation results show that the virtual musculoskeletal methodology increases the efficiency by 3.4% in the steady flow and 7% in the Karman-vortex flow. This algorithm provides a new idea for the joint-space control of the bionic robots that need to reduce the energy consumption of movements.

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

Cited by 49 publications

References 19 publications

Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm

Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm

The Research of Maneuverability Modeling and Environmental Monitoring Based on a Robotic Dolphin

High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology

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