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Robot fish bionic control method and system integrating Spiking neural network and CPG

A neural network and control method technology, applied in the field of motion control, can solve the problems of environmental adaptability of robotic fish, the difficulty of integrating model and environmental information, etc., to achieve the effect of improving autonomy and adaptability

Pending Publication Date: 2020-04-10
SHANDONG JIANZHU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, CPG-based motion controllers usually have a big defect, that is, the CPG model is designed to imitate biological motion control, and is mainly used to generate rhythmic motion signals. It is difficult to integrate the model with environmental information, and there is environmental adaptability of robotic fish. question,

Method used

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  • Robot fish bionic control method and system integrating Spiking neural network and CPG
  • Robot fish bionic control method and system integrating Spiking neural network and CPG
  • Robot fish bionic control method and system integrating Spiking neural network and CPG

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Embodiment 1

[0045]This embodiment discloses a bionic control method for robotic fish that integrates Spiking neural network and CPG, imitating the biological motion mechanism to design the motion control system of robotic fish, and the Spiking neural network is used as an upper-layer controller to process environmental information and generate decision-making commands; The saturation function enables the CPG model to have an input function; the CPG neuron acts as a lower-level controller to receive spiking excitation signals and output control commands. This disclosure proposes the possibility of a robotic fish underwater sensing environment and autonomous movement.

[0046] Step 1: Spiking neural network modeling

[0047] Neurons are the basic structural components of the brain and the most basic unit of the Spiking neural network, which mainly processes pulse signals. This disclosure uses the Izhikevich neuron model as the upper controller of the hierarchical control system, which has s...

Embodiment 2

[0087] The present disclosure provides a robotic fish bionic control system that integrates Spiking neural network and CPG, including:

[0088]CPG model building module, which is used for dynamic modeling of four-joint robot fish with pectoral fins, using nonlinear oscillator model as CPG neuron, to determine the left and right input excitation, downlink and uplink phase coupling coefficient, and uplink and downlink coupling The coefficient weight corresponds to the CPG frequency of each joint;

[0089] Spiking neural network model building module, which is used to determine the Izhikevich neuron model, set various parameters to simulate different discharge states, use the unsupervised algorithm based on Hebb learning rules to perform neural network training on different discharge states, and send the trained data To the CPG neuron, as the input signal of the CPG neuron, drive the CPG to output the control signal of the bionic robot fish;

[0090] Spiking neural network and C...

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Abstract

The invention discloses a robot fish bionic control method and a robot fish bionic control system integrating a Spiking neural network and a CPG. The robot fish bionic control method comprises the steps of: establishing a CPG model and a Spiking neural network model; establishing a Spiking neural network and CPG hierarchical control model; taking the Spiking neural network model as an upper-levelcontroller, and taking a CPG model serves as a lower-level controller; and designing a saturation function to be connected with the upper-level controller and the lower-level controller, wherein the CPG model receives excitation signals generated by the Spiking neural network model by means of the saturation function and outputs control signals to drive all joints of a bionic robot fish to move.

Description

technical field [0001] The present disclosure relates to the technical field of motion control, in particular to a bionic control method and system for a robotic fish that integrates a Spiking neural network and a CPG. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In recent years, as the scarcity of land resources has become increasingly serious, people have paid more and more attention to the rich marine resources. Since the original underwater detection, operation, and delivery devices are difficult to meet the needs of complex underwater operations, the research and development of underwater robots has been accelerated. As the combination of fish propulsion mechanism and robot technology, the bionic robotic fish provides a new idea for the development of new underwater vehicles, and has important research value and application prosp...

Claims

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Application Information

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IPC IPC(8): G05B17/02
CPCG05B17/02
Inventor 汪明常征卫正张宜阳
Owner SHANDONG JIANZHU UNIV
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