CN118244805B - A control method, device and equipment for a bionic robot dolphin - Google Patents

Abstract

The present invention provides a control method, device and equipment for a bionic robot dolphin, which belongs to the field of bionic robot control technology. The control method for a bionic robot dolphin of the present invention includes: obtaining the movement speed information and coordinate information of the bionic robot dolphin; obtaining the control parameters of the bionic robot dolphin according to the movement speed information and the coordinate information; the control parameters include: target speed increment and target turning angle; inputting the control parameters into a control model for processing to obtain an output signal; the control model processes the control parameters through a neuron function to obtain an intermediate result, corrects the intermediate result and obtains an output signal; and controls the operation state of the bionic robot dolphin according to the output signal. The technical solution of the present invention enables the bionic robot dolphin to respond to control instructions in a timely manner when in motion, thereby improving the motion performance of the bionic robot dolphin.

Description

A control method, device and equipment for a bionic robot dolphin

Technical Field

The present invention relates to the technical field of bionic robot control, and in particular to a control method, device and equipment for a bionic robot dolphin.

Background technique

Traditional ships and submarines usually use propellers as propulsion mechanisms, which not only make a lot of noise, but also consume part of the energy of mechanical work in the process of transmission and overcoming resistance, and the rotation of traditional blades will harm aquatic organisms. At present, most bionic underwater robots are based on fish, and the shell and propulsion structure are designed according to the appearance and propulsion mode of different aquatic organisms. The propulsion mode can be divided into two categories: central fin/opposite fin propulsion mode and body/tail fin propulsion mode. Among them, the central fin/opposite fin propulsion mode has higher navigation stability, while the body/tail fin propulsion mode can achieve a higher navigation speed, with higher propulsion performance and energy utilization efficiency. At the same time, the body/tail fin propulsion mode can also eliminate the huge noise generated by the propeller and has a good concealment effect. Dolphins rely on the swing of their tails and tail fins to provide the main power. Their propulsion mode is a typical body/tail fin propulsion mode. The tail fin swings through the bending movement of the tail. During the swimming process, the trajectory of the dolphin's tail fin can be approximately regarded as a curve that changes according to the sine law.

In order to truly restore the movement posture of the dolphin, most of the existing bionic robot dolphins use a multi-joint module drive method. Each joint module is equipped with a power device, which can adjust the movement state of the bionic machine in real time according to the control instructions and the current environment. In this process, since it is necessary to control the multiple power devices of the bionic robot dolphin to work together at the same time, the traditional control method cannot respond to the control instructions in time, resulting in low movement performance of the bionic robot dolphin.

Summary of the invention

The present invention provides a control method, device and equipment for a bionic robot dolphin, which improve the motion performance of the bionic robot dolphin.

In order to solve the above technical problems, the technical solution of the present invention is as follows:

A control method for a bionic robot dolphin, comprising:

Obtain the movement speed information and coordinate information of the bionic robot dolphin;

According to the motion speed information and the coordinate information, control parameters of the bionic robot dolphin are obtained; the control parameters include: target speed increment and target rotation angle;

The control parameters are input into the control model for processing to obtain an output signal; the control model processes the control parameters through a neuron function to obtain an intermediate result, and the intermediate result is corrected to obtain an output signal;

The operating state of the bionic robot dolphin is controlled according to the output signal.

Optionally, obtaining control parameters of the bionic robot dolphin according to the motion speed information and the coordinate information includes:

According to the motion speed information and the coordinate information, by the formula:

, get the target speed increment;

in, is the target speed increment, x ₂ , y ₂ , z ₂ are the target position coordinate data, x ₁ , y ₁ , z ₁ are the current position coordinate data of the bionic robot dolphin, t is the preset time data, is the current movement speed of the bionic robot dolphin;

By formula:

, get the target turning angle;

in, is the target corner, is the current motion speed vector of the bionic robot dolphin, is the target position vector of the bionic robot dolphin.

Optionally, the control model processes the control parameter by a neuron function to obtain an intermediate result, and corrects the intermediate result to obtain an output signal, including:

The control parameters are input into the control model for weighted processing through a neuron function to obtain an intermediate result;

The intermediate result is corrected to obtain a current intensity control signal and a power-on time interval control signal; the current intensity control signal and the power-on time interval control signal are used to control the movement of the posture adjustment device and the driving device of the bionic robot dolphin.

Optionally, the control parameters are input into the control model and weighted by a neuron function to obtain an intermediate result, including:

The control model is implemented by neuron function:

Process the control parameters to obtain intermediate results;

Where f ( _ni ) is the intermediate result; _ni = _Ai ∙x+ _d1i , 1≤i≤j, j is the number of neurons in the first hidden layer; _Ai =[ ], is the weight from the input layer to the first hidden layer; x=[x ₁ ,x ₂ ] is the input vector, x ₁ = , x ₂ = ; d _1i is the weight of the neuron bias value in the first hidden layer; a is a parameter, 0<a<1.

Optionally, the intermediate result is corrected to obtain a current intensity control signal and a power-on time interval control signal, including:

According to the intermediate results, by formula:

Determine deviation data, where E is the deviation data; For the expected results;

The intermediate result is corrected according to the deviation data to obtain a current intensity control signal and a power-on time interval control signal.

Optionally, the intermediate result is corrected according to the deviation data to obtain a current intensity control signal and a power-on time interval control signal, including:

According to the deviation data, by the formula:

Determine the weight from the second hidden layer to the output layer and the weight of the neuron bias value in the second hidden layer, where, is the weight from the second hidden layer to the output layer, m=1, 2; is the weight of the neuron bias value in the second hidden layer; is the learning rate;

According to the weight from the second hidden layer to the output layer and the weight of the neuron bias value in the second hidden layer, a current intensity control signal and a power-on time interval control signal are obtained through the neuron function.

Optionally, controlling the operating state of the bionic robot dolphin according to the output signal includes:

generating a control pulse signal according to the current intensity control signal and the power-on time interval control signal;

The operating state of the bionic robot dolphin is controlled according to the control pulse signal.

An embodiment of the present invention further provides a control device for a bionic robot dolphin, the device comprising:

An acquisition module is used to acquire the movement speed information and coordinate information of the bionic robot dolphin;

A production module, used for obtaining control parameters of the bionic robot dolphin according to the motion speed information and the coordinate information; the control parameters include: target speed increment and target rotation angle;

A determination module is used to input the control parameter into a control model for processing to obtain an output signal; the control model processes the control parameter through a neuron function to obtain an intermediate result, and corrects the intermediate result to obtain an output signal;

A control module is used to control the operating state of the bionic robot dolphin according to the output signal.

Optionally, obtaining control parameters of the bionic robot dolphin according to the motion speed information and the coordinate information includes:

According to the motion speed information and the coordinate information, by the formula:

, get the target speed increment;

in, is the target speed increment, x ₂ , y ₂ , z ₂ are the target position coordinate data, x ₁ , y ₁ , z ₁ are the current position coordinate data of the bionic robot dolphin, t is the preset time data, is the current movement speed of the bionic robot dolphin;

By formula:

, get the target turning angle;

in, is the target corner, is the current motion speed vector of the bionic robot dolphin, is the target position vector of the bionic robot dolphin.

Optionally, the control model processes the control parameter by a neuron function to obtain an intermediate result, and corrects the intermediate result to obtain an output signal, including:

The control parameters are input into the control model for weighted processing through a neuron function to obtain an intermediate result;

The intermediate result is corrected to obtain a current intensity control signal and a power-on time interval control signal; the current intensity control signal and the power-on time interval control signal are used to control the movement of the posture adjustment device and the driving device of the bionic robot dolphin.

Optionally, the control parameters are input into the control model and weighted by a neuron function to obtain an intermediate result, including:

The control model is implemented by neuron function:

Process the control parameters to obtain intermediate results;

Where f ( _ni ) is the intermediate result; _ni = _Ai ∙x+ _d1i , 1≤i≤j, j is the number of neurons in the first hidden layer; _Ai =[ ], is the weight from the input layer to the first hidden layer; x=[x ₁ ,x ₂ ] is the input vector, x ₁ = , x ₂ = ; d _1i is the weight of the neuron bias value in the first hidden layer; a is a parameter, 0<a<1.

Optionally, the intermediate result is corrected to obtain a current intensity control signal and a power-on time interval control signal, including:

According to the intermediate results, by formula:

Determine deviation data, where E is the deviation data; For the expected results;

The intermediate result is corrected according to the deviation data to obtain a current intensity control signal and a power-on time interval control signal.

Optionally, the intermediate result is corrected according to the deviation data to obtain a current intensity control signal and a power-on time interval control signal, including:

According to the deviation data, by the formula:

Determine the weight from the second hidden layer to the output layer and the weight of the neuron bias value in the second hidden layer, where, is the weight from the second hidden layer to the output layer, m=1, 2; is the weight of the neuron bias value in the second hidden layer; is the learning rate;

According to the weight from the second hidden layer to the output layer and the weight of the neuron bias value in the second hidden layer, a current intensity control signal and a power-on time interval control signal are obtained through the neuron function.

Optionally, controlling the operating state of the bionic robot dolphin according to the output signal includes:

generating a control pulse signal according to the current intensity control signal and the power-on time interval control signal;

The operating state of the bionic robot dolphin is controlled according to the control pulse signal.

An embodiment of the present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program executes the above method when executed by the processor.

An embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the above method.

The above solution of the present invention includes at least the following beneficial effects:

The scheme of the present invention obtains the movement speed information and coordinate information of the bionic robot dolphin; obtains the control parameters of the bionic robot dolphin according to the movement speed information and the coordinate information; the control parameters include: a target speed increment and a target rotation angle; the control parameters are input into a control model for processing to obtain an output signal; the control model processes the control parameters through a neuron function to obtain an intermediate result, corrects the intermediate result, and obtains an output signal; controls the running state of the bionic robot dolphin according to the output signal, so that the bionic robot dolphin can respond to the control instruction in time when moving, improves the movement performance of the bionic robot dolphin, and makes the movement of the bionic robot dolphin more natural, efficient and flexible.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for controlling a bionic robot dolphin provided by an embodiment of the present invention;

FIG2 is a schematic diagram of the architecture of a control model of a bionic robot dolphin provided in an embodiment of the present invention;

FIG3 is a structural diagram of a bionic robot dolphin provided in an embodiment of the present invention;

FIG4 is a block diagram of a control system of a bionic robot dolphin provided in an embodiment of the present invention;

FIG5 is a structural diagram of a control device for a bionic robot dolphin provided in an embodiment of the present invention;

FIG6 is a schematic diagram of the structure of a computing device provided in an embodiment of the present invention;

Among them, 1. horizontal tail fin drive device; 2. vertical tail fin drive device; 3. pectoral fin drive device; 50. control device; 51. acquisition module; 52. production module; 53. determination module; 54. control module; 60. computing device; 61. processor; 62. memory.

Detailed ways

The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

As shown in FIG1 , an embodiment of the present invention provides a control method for a bionic robot dolphin, comprising:

Step 11, obtaining the movement speed information and coordinate information of the bionic robot dolphin;

In a specific implementation, the obtaining of the movement speed information and coordinate information of the bionic robot dolphin includes: obtaining the movement speed information of the bionic robot dolphin through a sensor installed at the head position of the bionic robot dolphin; and obtaining the coordinate information of the bionic robot dolphin through a GPS locator installed at the head position of the bionic robot dolphin;

Step 12, obtaining control parameters of the bionic robot dolphin according to the motion speed information and the coordinate information; the control parameters include: target speed increment and target rotation angle;

When it is implemented specifically, the formula is:

, get the target speed increment;

By formula:

, get the target turning angle;

in, is the target speed increment, x ₂ , y ₂ , z ₂ are the target position coordinate data, x ₁ , y ₁ , z ₁ are the current position coordinate data of the bionic robot dolphin, t is the preset time data, is the current movement speed of the bionic robot dolphin, is the target corner, is the current motion speed vector of the bionic robot dolphin, is the target position vector of the bionic robot dolphin.

Step 13, inputting the control parameters into the control model for processing to obtain an output signal; the control model processes the control parameters through a neuron function to obtain an intermediate result, and corrects the intermediate result to obtain an output signal;

Step 14, controlling the operating state of the bionic robot dolphin according to the output signal.

In this embodiment, the motion speed information and coordinate information of the bionic robot dolphin are obtained by the positioner and the sensor, and the control parameters of the bionic robot dolphin are obtained according to the motion speed information and coordinate information of the bionic robot dolphin. The control parameters include the target speed increment. and target angle , increase the target speed by and target angle Input control model, the target speed increment is calculated through the neuron function and target angle Processing is performed to obtain intermediate results, the intermediate results are corrected to obtain output signals, and the operating state of the bionic robot dolphin is controlled according to the output signals. This technical solution enables the bionic robot dolphin to respond to control instructions in a timely manner during movement, thereby improving the movement performance of the bionic robot dolphin and making the movement of the bionic robot dolphin more natural, efficient and flexible.

In an optional embodiment of the present invention, step 13 includes:

Step 131, input the control parameters into the control model and perform weighted processing through the neuron function to obtain an intermediate result;

Step 132, correcting the intermediate result to obtain a current intensity control signal and a power-on time interval control signal; the current intensity control signal and the power-on time interval control signal are used to control the movement of the posture adjustment device and the driving device of the bionic robot dolphin.

In this embodiment, the control parameters are input into the control model for weighted processing through the neuron function to obtain an intermediate result; in specific implementation, the control model uses the neuron function:

Process the control parameters to obtain intermediate results;

Where f ( _ni ) is the intermediate result; _ni = _Ai ∙x+ _d1i , 1≤i≤j, j is the number of neurons in the first hidden layer; _Ai =[ ], is the weight from the input layer to the first hidden layer; x=[x ₁ ,x ₂ ] is the input vector, x ₁ = , x ₂ = ; d _1i is the weight of the neuron bias value in the first hidden layer; a is a parameter, 0<a<1;

The intermediate result is corrected to obtain a current intensity control signal and a power-on time interval control signal; the current intensity control signal and the power-on time interval control signal are used to control the movement of the posture adjustment device and the driving device of the bionic robot dolphin; in specific implementation, according to the intermediate result, through the formula:

Determine deviation data, where E is the deviation data; For the expected results;

By formula:

Determine the weight from the second hidden layer to the output layer and the weight of the neuron bias value in the second hidden layer, where, is the weight from the second hidden layer to the output layer, m=1, 2; is the weight of the neuron bias value in the second hidden layer; is the learning rate; according to the weight from the second hidden layer to the output layer and the weight of the neuron bias value in the second hidden layer, the current intensity control signal and the power-on time interval control signal are obtained through the neuron function.

As shown in Figure 2, a target control model contains 2 input vectors, 2 hidden layers and 2 output vectors. The training process includes:

Step 31, obtaining training set data including operating status parameters of the historical bionic machine dolphin;

Step 32, performing feature extraction processing on the training set data to obtain multiple input vectors;

Step 33, inputting the input vector into an input layer of a control model, and the input layer outputs a first intermediate result;

Step 34, inputting the first intermediate result output by the input layer into the first hidden layer of the control model for local response processing, and outputting a second intermediate result;

Step 35, inputting the second intermediate result into the second hidden layer of the control model for correction processing, and outputting a third intermediate result;

Step 36, inputting the third intermediate result into the output layer of the control model, and the output layer outputs the network result;

Step 37, obtaining the error between the network result and each sample in the training set;

Step 37, weighting the neural network function in the hidden layer according to the error and performing input and output calculations again until the error is controlled within the allowable accuracy range, thereby obtaining the control model.

In an optional embodiment of the present invention, step 14 includes:

Step 141, generating a control pulse signal according to the current intensity control signal and the power-on time interval control signal;

Step 142, controlling the operating state of the bionic robot dolphin according to the control pulse signal.

In this embodiment, the target speed increment of the bionic robot dolphin is and target angle As input parameters of the target control model, the current intensity and the power-on time interval are obtained; the obtained current intensity and the power-on time interval are used as input parameters of the pulse width modulation circuit to obtain a pulse square wave signal for controlling the operation of the bionic robot dolphin; the obtained pulse square wave signal is used as an input parameter of the power amplification device to control the operation of multiple driving devices of the bionic robot dolphin, thereby achieving high-precision speed control of the bionic robot dolphin and improving the movement performance of the bionic robot dolphin.

A specific embodiment of the control method of the bionic robot dolphin provided by the embodiment of the present invention is:

As shown in Figures 3 and 4, the driving device of the bionic robot dolphin includes a pectoral fin driving device 3, a tail fin horizontal driving device 1, and a tail fin vertical driving device 2, wherein the pectoral fin driving device 3 is used to balance the posture of the bionic robot dolphin, which helps the bionic robot dolphin to complete various flexible movements; the tail fin horizontal driving device 1 is used to adjust the movement direction of the bionic robot dolphin; the tail fin vertical driving device 2 is composed of a plurality of sub-driving devices connected in series, which can generate a sine wave-like swing to drive the bionic robot dolphin forward; the sensor installed on the head of the bionic robot dolphin is used to collect the current motion state information of the bionic robot dolphin, including motion speed information and coordinate information, and the controller calculates the target speed increment according to the target position. and target angle , increase the target speed by and target angle As control parameters, the controller is input, and the controller generates an output signal including current intensity and power-on time interval through a neural network function. The obtained current intensity and power-on time interval are used as input parameters of a pulse width modulation circuit to obtain a pulse square wave signal for controlling the operation of a bionic robot dolphin drive device; the obtained pulse square wave signal is used as an input parameter of a power amplification device to control the movement of multiple drive devices of the bionic robot dolphin, so that the bionic robot dolphin moves to a target position.

As shown in FIG5 , an embodiment of the present invention provides a control device 50 for a bionic robot dolphin, and the control device 50 includes:

An acquisition module 51 is used to acquire the movement speed information and coordinate information of the bionic robot dolphin;

A production module 52 is used to obtain control parameters of the bionic robot dolphin according to the motion speed information and the coordinate information; the control parameters include: a target speed increment and a target rotation angle;

The determination module 53 is used to input the control parameter into the control model for processing to obtain an output signal; the control model processes the control parameter through a neuron function to obtain an intermediate result, and corrects the intermediate result to obtain an output signal;

The control module 54 is used to control the operating state of the bionic robot dolphin according to the output signal.

Optionally, obtaining control parameters of the bionic robot dolphin according to the motion speed information and the coordinate information includes:

According to the motion speed information and the coordinate information, by the formula:

, get the target speed increment;

in, is the target speed increment, x ₂ , y ₂ , z ₂ are the target position coordinate data, x ₁ , y ₁ , z ₁ are the current position coordinate data of the bionic robot dolphin, t is the preset time data, is the current movement speed of the bionic robot dolphin;

By formula:

, get the target turning angle;

in, is the target corner, is the current motion speed vector of the bionic robot dolphin, is the target position vector of the bionic robot dolphin.

Optionally, the control model processes the control parameter by a neuron function to obtain an intermediate result, and corrects the intermediate result to obtain an output signal, including:

The control parameters are input into the control model for weighted processing through a neuron function to obtain an intermediate result;

The intermediate result is corrected to obtain a current intensity control signal and a power-on time interval control signal; the current intensity control signal and the power-on time interval control signal are used to control the movement of the posture adjustment device and the driving device of the bionic robot dolphin.

Optionally, the control parameters are input into the control model and weighted by a neuron function to obtain an intermediate result, including:

The control model is implemented by neuron function:

Process the control parameters to obtain intermediate results;

Where f ( _ni ) is the intermediate result; _ni = _Ai ∙x+ _d1i , 1≤i≤j, j is the number of neurons in the first hidden layer; _Ai =[ ], is the weight from the input layer to the first hidden layer; x=[x ₁ ,x ₂ ] is the input vector, x ₁ = , x ₂ = ; d _1i is the weight of the neuron bias value in the first hidden layer; a is a parameter, 0<a<1.

Optionally, the intermediate result is corrected to obtain a current intensity control signal and a power-on time interval control signal, including:

According to the intermediate results, by formula:

Determine deviation data, where E is the deviation data; For the expected results;

The intermediate result is corrected according to the deviation data to obtain a current intensity control signal and a power-on time interval control signal.

Optionally, the intermediate result is corrected according to the deviation data to obtain a current intensity control signal and a power-on time interval control signal, including:

According to the deviation data, by the formula:

Determine the weight from the second hidden layer to the output layer and the weight of the neuron bias value in the second hidden layer, where, is the weight from the second hidden layer to the output layer, m=1, 2; is the weight of the neuron bias value in the second hidden layer; is the learning rate;

According to the weight from the second hidden layer to the output layer and the weight of the neuron bias value in the second hidden layer, a current intensity control signal and a power-on time interval control signal are obtained through the neuron function.

Optionally, controlling the operating state of the bionic robot dolphin according to the output signal includes:

Generate a control pulse signal according to the current intensity control signal and the power-on time interval control signal;

The operating state of the bionic robot dolphin is controlled according to the control pulse signal.

It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

As shown in FIG6 , an embodiment of the present invention further provides a computing device 60, including a processor 61, a memory 62, and a program or instruction stored in the memory 62 and executable on the processor 61. When the program or instruction is executed by the processor 61, each process of the embodiment of the synchronization information processing method of the virtual object described above is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here. It should be noted that the computing device in the embodiment of the present invention includes the mobile electronic device and the non-mobile electronic device described above.

Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

Stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or optical disk, etc., various media that can store program codes.

In addition, it should be pointed out that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and/or recombined. These decompositions and/or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understandable that all or any steps or components of the method and apparatus of the present invention can be implemented in hardware, firmware, software or a combination thereof in any computing device (including processors, storage media, etc.) or a network of computing devices, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code for implementing a method or device. That is to say, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and/or recombined. These decompositions and/or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims (8)

1. A control method of a biomimetic robotic dolphin, comprising:

Acquiring motion speed information and coordinate information of a bionic robot dolphin;

Obtaining control parameters of the bionic robotic dolphin according to the movement speed information and the coordinate information; the control parameters include: target speed increment and target rotation angle;

Inputting the control parameters into a control model for processing to obtain an output signal; the control model processes the control parameters through a neuron function to obtain an intermediate result, and corrects the intermediate result to obtain an output signal;

controlling the running state of the bionic robotic dolphin according to the output signal;

the control model processes control parameters through a neuron function to obtain an intermediate result, corrects the intermediate result to obtain an output signal, and comprises the following steps:

Inputting the control parameters into a control model to carry out weighting treatment through a neuron function to obtain an intermediate result;

Correcting the intermediate result to obtain a current intensity control signal and an electrifying time interval control signal; the current intensity control signal and the power-on time interval control signal are used for controlling the gesture adjusting device and the driving device of the bionic robot dolphin to move;

The control parameters are input into a control model to be weighted through a neuron function, and an intermediate result is obtained, wherein the method comprises the following steps:

the control model is generated by a neuron function:

Processing the control parameters to obtain an intermediate result;

Wherein f (n _i) is an intermediate result; I is more than or equal to 1 and less than or equal to j, wherein j is the number of neurons in the first hidden layer; a _i=[a_i1,a_i2],a_i1、a_i2 is the weight from the input layer to the first hidden layer; x= [ x ₁,x₂ ] is an input vector, and x ₁=Δv_T,x₂=θ_T;d_1i is the weight of the neuron bias value in the first hidden layer; a is a parameter, 0< a <1.

2. The control method of a biomimetic robotic dolphin according to claim 1, wherein obtaining control parameters of the biomimetic robotic dolphin according to the movement speed information and the coordinate information comprises:

According to the movement speed information and the coordinate information, the following formula is adopted:

Obtaining a target speed increment;

Wherein Deltav _T is a target speed increment, x ₂、y₂、z₂ is target position coordinate data, x ₁、y₁、z₁ is current position coordinate data of the bionic robot dolphin, t is preset time data, and v ₀ is current movement speed of the bionic robot dolphin;

By the formula:

obtaining a target rotation angle;

Wherein, theta _T is the target rotation angle, For the current motion velocity vector of the biomimetic robotic dolphin,And (3) a target position vector of the bionic robotic dolphin.

3. The control method of a biomimetic robotic dolphin according to claim 1, wherein correcting the intermediate result to obtain a current intensity control signal and an energizing time interval control signal comprises:

According to the intermediate result, the following formula is adopted:

Determining deviation data, wherein E is the deviation data; Is the expected result;

And correcting the intermediate result according to the deviation data to obtain a current intensity control signal and an energizing time interval control signal.

4. The control method of a biomimetic robotic dolphin according to claim 3, wherein correcting the intermediate result according to the deviation data, a current intensity control signal and an energizing time interval control signal are obtained, comprising:

according to the deviation data, the method comprises the following steps of:

determining weights from the second hidden layer to the output layer and weights of neuron bias values in the second hidden layer, wherein u _im is the weight from the second hidden layer to the output layer, and m=1, 2; d _2i is the weight of the neuron bias value in the second hidden layer; η is the learning rate;

and obtaining a current intensity control signal and a power-on time interval control signal through the neuron function according to the weight from the second hidden layer to the output layer and the weight of the neuron bias value in the second hidden layer.

5. The control method of a biomimetic robotic dolphin according to any one of claims 1 to 4, wherein controlling the operational state of the biomimetic robotic dolphin according to the output signal comprises:

Generating a control pulse signal according to the current intensity control signal and the power-on time interval control signal;

and controlling the running state of the bionic robot dolphin according to the control pulse signal.

6. A control device for a biomimetic robotic dolphin, the device comprising:

The acquisition module is used for acquiring motion speed information and coordinate information of the bionic robot dolphin;

The production module is used for obtaining control parameters of the bionic robotic dolphin according to the movement speed information and the coordinate information; the control parameters include: target speed increment and target rotation angle;

The determining module is used for inputting the control parameters into a control model for processing to obtain an output signal; the control model processes the control parameters through a neuron function to obtain an intermediate result, and corrects the intermediate result to obtain an output signal;

the control module is used for controlling the running state of the bionic robot dolphin according to the output signal;

the control model processes control parameters through a neuron function to obtain an intermediate result, corrects the intermediate result to obtain an output signal, and comprises the following steps:

Inputting the control parameters into a control model to carry out weighting treatment through a neuron function to obtain an intermediate result;

Correcting the intermediate result to obtain a current intensity control signal and an electrifying time interval control signal; the current intensity control signal and the power-on time interval control signal are used for controlling the gesture adjusting device and the driving device of the bionic robot dolphin to move;

The control parameters are input into a control model to be weighted through a neuron function, and an intermediate result is obtained, wherein the method comprises the following steps:

the control model is generated by a neuron function:

Processing the control parameters to obtain an intermediate result;

Wherein f (n _i) is an intermediate result; I is more than or equal to 1 and less than or equal to j, wherein j is the number of neurons in the first hidden layer; a _i=[a_i1,a_i2],a_i1、a_i2 is the weight from the input layer to the first hidden layer; x= [ x ₁,x₂ ] is an input vector, and x ₁=Δv_T,x₂=θ_T;d_1i is the weight of the neuron bias value in the first hidden layer; a is a parameter, 0< a <1.

7. A computing device, comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method of any one of claims 1 to 5.

8. A computer readable storage medium storing instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 5.