CN114460849B - Bionic robot fish motion control method and device and bionic robot fish - Google Patents
Bionic robot fish motion control method and device and bionic robot fish Download PDFInfo
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Abstract
The embodiment of the application provides a bionic robot fish motion control method and device and a bionic robot fish, and the method comprises the following steps: determining related control parameters of each joint of the bionic robot fish according to the expected forward speed and steering speed; inputting the relevant control parameters into a motion control model of the bionic robot fish to carry out parameter smooth transition pretreatment and angle closed-loop control operation so as to obtain a target rotation angle of each joint; the motion control model comprises a central mode generator network formed based on a preset oscillator, and the central mode generator network is used for executing the angle closed-loop control operation; and controlling each joint steering engine of the bionic robot fish according to the target rotation angle so as to enable the bionic robot fish to execute expected actions. The method can enable the input parameters of the oscillator in the motion control model to be switched rapidly and smoothly, thereby giving consideration to rapidity, smoothness and the like of network output.
Description
Technical Field
The application relates to the technical field of bionic robot fish, in particular to a motion control method and device of the bionic robot fish and the bionic robot fish.
Background
The traditional rod-type underwater bionic robot fish is usually controlled by a Central Pattern Generator (CPG) algorithm, and at the moment, a Hopff oscillator can be adopted for each joint of the robot fish to simulate the rhythmic motion of each joint, wherein the hopf oscillator has the advantages that the hopf oscillator has stable state output, the output can be started in any state, an external oscillation starting signal is not needed, and the final state output can be stabilized on a limit ring. The CPG network formed according to the hopf oscillator can enable the relevant parameters of the robotic fish to be in relatively smooth transition when the parameters are changed, however, in practical application, if the parameters of the current CPG network are changed violently, especially when the amplitude and frequency parameters are changed suddenly, the fast convergence and the smooth characteristics of the hopf oscillator cannot be considered at the same time, the service life of the joints of the robotic fish is influenced, the power consumption is increased, and the like.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and an apparatus for controlling motion of a biomimetic robotic fish, and a biomimetic robotic fish, which can enable input parameters of an oscillator of a CPG network in a motion control model to be switched rapidly and smoothly, so as to take account of rapidity and smoothness of CPG network output.
In a first aspect, an embodiment of the present application provides a method for controlling motion of a biomimetic robotic fish, where each joint of the biomimetic robotic fish performs rhythmic motion simulation using a preset oscillator, the method including:
determining related control parameters of each joint of the bionic robot fish according to the expected forward speed and steering speed;
inputting the relevant control parameters into a motion control model of the bionic robot fish to carry out parameter smooth transition pretreatment and angle closed-loop control operation so as to obtain a target rotation angle of each joint of the bionic robot fish; the motion control model comprises a central mode generator network formed based on the preset oscillator, and the central mode generator network is used for executing the angle closed-loop control operation;
and controlling each joint steering engine of the bionic robot fish according to the target rotation angle so as to enable the bionic robot fish to execute expected actions.
In some embodiments, the relevant control parameters include a swing amplitude and a swing frequency of the respective joint; the parameter smooth transition preprocessing comprises the following steps:
and respectively smoothing the swing frequency and the swing amplitude of each joint by utilizing a nonlinear transition function.
In some embodiments, the biomimetic robotic fish comprises three joints connected in a neuron chain manner, and the related control parameters further comprise phase coupling strength between two preset oscillators corresponding to every two connected joints and phase difference between every two connected joints; the angle closed-loop control operation comprises:
inputting the oscillation amplitude subjected to normalization processing, the oscillation frequency subjected to smoothing processing, the phase coupling strength and the phase difference into preset oscillators of corresponding joints in the central mode generator network to calculate and obtain intermediate state variables of the preset oscillators;
calculating according to the intermediate state variable and the smoothed swing amplitude to obtain state output variables of the preset oscillators;
and determining the target rotation angle of each joint according to the state output variable of each preset oscillator.
In some embodiments, in constructing the motion control model, further comprising:
locally decoupling the phase coupling strength between the preset oscillators and the swing frequency of a joint so as to improve the structure of a central mode generator network formed on the basis of the preset oscillators to obtain an improved central mode generator network; the improved neutral pattern generator network is used for executing the angle closed-loop control operation.
In some embodiments, the nonlinear transition function is constructed based on a sigmoid function;
the expression of the nonlinear transition function is as follows:
wherein,
in the formula, z1And zinOutput and input quantities, ż, respectively, of said non-linear transition function1And delta is a linear interval coefficient of the sigmoid function, and beta is a convergence gain.
In some embodiments, the preset oscillator is expressed as follows:
wherein x and y are two intermediate state variables of the preset oscillator, ẋ and ẏ are derivatives corresponding to the two intermediate state variables, k is a coefficient of convergence speed of a limit cycle of the preset oscillator,NL() Is representative of the non-linear transition function,fis the frequency of the oscillation of the joint,
and
respectively, a first convergence gain and a first linear interval coefficient, a is the swing amplitude of the joint,
and
respectively, a second convergence gain and a second linear interval coefficient, xoutAnd youtOutputting variables for the two states of the preset oscillator.
In some embodiments, the improved neutral pattern generator network is expressed as follows:
in the formula, xiAnd yiIntermediate state variables, x, for the ith preset oscillatoroutiAnd youtiOutputs variables for the state of the ith preset oscillator,
for the phase coupling strength between the two preset oscillators,
is the phase difference from the ith joint to the jth joint, θiIs the target rotation angle of the ith joint, AiThe amplitude of the oscillation of the ith joint.
In a second aspect, an embodiment of the present application further provides a bionic robot fish motion control apparatus, each joint of the bionic robot fish adopts a preset oscillator to perform rhythm motion simulation, and the apparatus includes:
the parameter determination module is used for determining related control parameters of all joints of the bionic robot fish according to the expected advancing speed and steering speed;
the angle calculation module is used for inputting the related control parameters into a motion control model of the bionic robot fish to carry out parameter smooth transition preprocessing and angle closed-loop control operation so as to obtain target rotation angles of all joints of the bionic robot fish; the motion control model comprises a central mode generator network formed based on the preset oscillator, and the central mode generator network is used for executing the angle closed-loop control operation;
and the joint control module is used for controlling each joint steering engine of the bionic robot fish according to the target rotation angle so as to enable the bionic robot fish to execute expected actions.
In a third aspect, an embodiment of the present application further provides a biomimetic robotic fish, including a processor and a memory, where the memory stores a computer program, and the processor is configured to execute the computer program to implement the above-mentioned method for controlling motion of a biomimetic robotic fish.
In a fourth aspect, an embodiment of the present application further provides a readable storage medium, which stores a computer program, and when the computer program is executed on a processor, the computer program implements the above-mentioned biomimetic robotic fish motion control method.
The embodiment of the application has the following beneficial effects:
the bionic robot fish motion control method provided by the embodiment of the application carries out structural improvement on the oscillator used for rhythm motion simulation and adopted by the joints of the bionic robot fish, carries out smooth transition pretreatment on input control parameters, and carries out angle closed-loop control operation on a CPG (compact peripheral component group) network formed based on the improved oscillator structure, so that the oscillator can be rapidly and smoothly transited to a new limit ring balance state, the rapid and smooth change of the output of a motion control model can be responded, the system power consumption is reduced, the service life of each joint of the robot fish is prolonged, and the like.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 shows a schematic structural diagram of a biomimetic robotic fish in an embodiment of the present application;
FIG. 2 shows a physical diagram of a biomimetic robotic fish in an embodiment of the present application;
FIG. 3 shows a CPG network structure constructed based on an oscillator and constructed by a bionic robot fish with a three-joint rod type;
FIG. 4 shows a graph of a hop simulation generated by a hopf oscillator when k is increased and a graph of a ringing simulation generated when the amplitude A is changed;
FIG. 5 shows a smooth contrast plot of a first order low pass filter with NL function proposed by an embodiment of the present application at different convergence gains;
FIG. 6 shows a simulation of the improved oscillator proposed by the embodiment of the present application at increasing K and changing A;
fig. 7 shows amplitude jump comparison simulation of a CPG network constructed based on a hopf oscillator and an improved CPG network proposed based on the present embodiment;
fig. 8 shows frequency mutation comparison simulation of a CPG network constructed based on a hopf oscillator and an improved CPG network proposed based on the present embodiment;
fig. 9 shows phase jump comparison simulation of a CPG network constructed based on a hopf oscillator and an improved CPG network proposed based on the present embodiment;
fig. 10 shows simulation results of simultaneous abrupt changes in amplitude and phase frequencies based on the improved CPG network;
FIG. 11 is a first flowchart of a method for controlling the movement of a biomimetic robotic fish according to an embodiment of the present application;
FIG. 12 is a second flowchart of a bionic robot fish motion control method according to the embodiment of the application;
fig. 13 is a schematic structural diagram illustrating a bionic robot fish motion control device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments.
The components of the embodiments of the present application, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
The bionic robot fish motion control method can be applied to rod-type robot fish such as carangid, carangid and the like. Referring to fig. 1 and fig. 2, a schematic structural diagram and a schematic physical diagram of a biomimetic robotic fish 100 provided in the embodiments of the present application are shown. Exemplary structures that make up the biomimetic robotic fish 100 may include, but are not limited to including: the device comprises a rigid fish head 101, a balancing weight 102, a fish fin steering engine 103, a main control panel 104, a plurality of fish body steering engines 105, corresponding connecting pieces 106, tail fin connecting pieces 107, tail fins 108 and the like, and sensing elements such as a power supply device 109, a camera 110 and the like. Wherein, for the motion control of control bionic machine fish 100, can regard these steering engines of connecting through the connecting piece as this bionic machine fish 100's corresponding joint, and then carry out motion state analysis to these joints through the main control board, and then control the turned angle of every joint to make this bionic machine fish 100 move about in aqueous like real fish, and carry out motion etc. according to anticipated movement track.
Based on the structure of the biomimetic robotic fish 100, the embodiment of the application provides a method for controlling the motion of the biomimetic robotic fish, which includes performing rhythm motion simulation on each joint of the biomimetic robotic fish 100 by using a pre-designed oscillator, wherein the pre-designed oscillator can be used for performing smooth transition on input parameters, and constructing a motion control model of the biomimetic robotic fish 100 based on an improved Central Pattern Generator (CPG) network obtained by the pre-designed oscillators, so as to realize angle closed-loop convergence control. And then, the rotation angle required by each joint of the robot fish is solved by using the motion control model, so that the expected motion attitude and the like are realized. It can be understood that the motion control model provided by the embodiment of the application can ensure that the input parameters input into the CPG network can be output quickly and smoothly even when jumping occurs, which is beneficial to improving the service life of the robot fish joint and reducing the power consumption and the like caused by the occurrence of jumping points.
For convenience of understanding, the biomimetic robotic fish 100 having a three joint structure will be exemplified below. It will be appreciated that the same is true for robotic fish having more bar-type joints.
For the biomimetic robotic fish 100 with three joint structures, when constructing the motion control model of the biomimetic robotic fish 100, each joint may use one preset oscillator to perform rhythm motion simulation, as shown in fig. 3, the three joints correspond to the three preset oscillators, and the preset oscillators are coupled in a neuron chain manner. It should be noted that the preset oscillator in the present embodiment is different from a typical hopf oscillator in structure, and specifically, is a novel oscillator obtained by improving the structure of the hopf oscillator. The novel oscillator can enable the input parameters of the CPG network to be switched rapidly and smoothly, so that rapidness and smoothness of CPG network output are considered.
Among them, the formula of a typical hopf oscillator is as follows:
where x and y are two intermediate state variables of the hopf oscillator, ẋ and ẏ are derivatives of the two intermediate state variables x and y, k is the limiting loop convergence speed coefficient of the hopf oscillator,fis the swing frequency of the joint, A is the swing amplitude of the joint,
is the circumference ratio. The initial values of the state variables x and y may be selected according to actual requirements, and are not limited herein.
In the prior art, when the hopf oscillator is applied to the motion control of the biomimetic robotic fish 100, simulation can be performed to find that the hopf oscillator has two problems, one of which is that when a limit loop is converged, a parameter k is increased to ensure rapid convergence, and then jump occurs in the convergence process, as shown in (a) of fig. 4 below, it can be seen that there is an obvious jump point, which indicates that the rapidity and smoothness of the hopf oscillator at this time cannot be considered at the same time. Two, when the other parameters are unchanged (where, k =20,f= 0.5), the smoothing of the limit cycle convergence changes by changing the amplitude a of the limit cycle, the convergence is slow and smooth when a =1, the limit cycle convergence speed increases when the amplitude a =2 is changed, and a small-range oscillation occurs, as shown in (b) of fig. 4. Obviously, the occurrence of these phenomena is not friendly to the life of the joint and the power consumption, so the embodiment of the present application improves on the basis of the hopf oscillator, that is, adds a nonlinear transition structure for performing smooth transition processing on the input parameters of the CPG network, so as to obtain the above-mentioned novel oscillator, and further, is used for constructing the CPG network and the motion control model, thereby realizing the fast response, the smooth motion control, and the like of the biomimetic robotic fish 100.
In this embodiment, the nonlinear transition structure is implemented by a nonlinear transition function (abbreviated as NL function), and in an implementation, the NL function can be constructed based on a sigmoid function, specifically, an expression of the NL function is as follows:
wherein,
in the formula, z1And zinOutput and input quantities of the NL function, ż, respectively1Is an output quantity Z1Delta is a linear interval coefficient of the sigmoid function, and beta is a convergence gain.
It will be appreciated that for the above NL function, the tracking input signal is divided into three sections, roughly a linear tracking section, a slope convergence section and a smooth transition section, according to the tracking error. According to the improved sigmod function, when the tracking error of the NL function is smaller than +/-delta, the NL function is in a linear tracking interval, the performance of the NL function in the linear tracking interval is equivalent to a first-order low-pass filter, and the output quantity Z can be enabled to be1Quickly tracing input quantity Z with certain bandwidthin. When the NL function has a tracking error larger than + -2 delta, it is in the slope convergence region, Z1The convergence is performed at a constant speed, and compared with the smoothing of filtering, the change rate of the curve under large error can be greatly reduced. When the tracking error of the NL function is between the two, a smooth transition region is entered, and the region serves as a smooth transition. Compared with a common smoothing means, the NL function constructed based on the improved sigmod function can well achieve rapidness and smoothness.
To better verify the effect of the above NL function, a smooth comparison graph of the first order low pass filter with the above NL function at different convergence gains is shown, as shown in fig. 5 (a) and 5 (b), respectively. The curve S1 is the target input, the curve S2 is the convergence curve of the first-order smoothing, and the curve S3 is the curve of the NL function smoothing convergence. It can be seen that, under the same convergence speed, the maximum rate of change of NL function convergence is significantly lower than that of first-order filtering smoothing, and the convergence effect is better.
Based on the NL function, the present embodiment proposes to modify the hopf oscillator to obtain the preset oscillator, which is exemplarily expressed as follows:
where x and y are two intermediate state variables of the preset oscillator, ẋ and ẏ are derivatives corresponding to the two intermediate state variables, k is a coefficient of convergence speed of the limit cycle,NL() The function NL is represented as a function of,
and
respectively a first convergence gain and a first linear interval coefficient,
and
respectively a second convergence gain and a second linear interval coefficient, xoutAnd youtTwo state output variables for the preset oscillator.
It can be understood that the above expression represents the swing amplitude A and swing frequency of the joint of the robot fish by using the nonlinear transition functionfThe smoothing process is respectively carried out, and meanwhile, the normalization process is also carried out on the swing amplitude A. The normalization processing means that the swing amplitude A is input into a preset oscillator in a constant 1 mode for operation, so that the amplitude variable is decoupled from the state variable of the oscillator.
In order to verify the effect of the improved oscillator, the same parameters as those used in the above fig. 4 are selected for simulation comparison, and the simulation results are shown in fig. 6 (a) and fig. 6 (b), so that it can be seen that the new oscillator obtained by improving the hopf oscillator can rapidly and smoothly transition to a new limit cycle equilibrium state even when the input parameters are suddenly changed, and the output can rapidly and smoothly change through reasonable parameter configuration.
Therefore, the CPG network of fig. 3 can be constructed based on the improved oscillator, and then the CPG network serves as a motion control model of the biomimetic robotic fish 100 to perform closed-loop convergence control of the rotation angle of the robotic fish joint. In one embodiment, the expression of the CPG network is as follows:
in the formula,
for the phase coupling strength between the two preset oscillators,
is the phase difference from the ith joint to the jth joint, θiIs the target rotation angle of the ith joint, AiThe amplitude of the oscillation of the ith joint. Taking three joints as an example, the value of i can be 1, 2, 3, and the value of j can be 2, 3, etc. It will be appreciated that the state of the oscillator is output by a variable x hereoutAs the target rotation angle of the joint to be solved. And performing rotation angle control on the motors of the corresponding joints according to the target rotation angle so as to realize the expected movement.
As a preferred scheme, the above-mentioned CPG network can also be optimized to obtain an improved CPG network. Because the phase coupling strength is within a certain input frequency range in actual measurement
And the frequency of the oscillationfCan be approximated by a linear coupling relationship and, therefore, can be approximated by a pair of wobble frequenciesfStrength of coupling with phase
The coupling relationship of (a) is partially decoupled, and the expression of the improved CPG network at this time is as follows:
it can be understood that the motion control model constructed by the improved CPG network can realize the input parameters
，Ai，f，
The mutual decoupling between the two is realized, and the output joint rotation angle response is rapid and smooth.
To verify the effectiveness of the above-mentioned CPG network, taking the biomimetic robotic fish 100 shown in fig. 3 as an example, by setting corresponding control parameters in the above-mentioned modified CPG network, (1) for the case of an abrupt amplitude change: suppose the initial conditions of the simulation aref =1.5，
，Ai=50，
=0.01, the swing amplitude of the joint is suddenly changed from 50 to 10, and fig. 7 (a) and 7 (b) respectively show the amplitude sudden change comparison simulation of the CPG network constructed based on the hopf oscillator and the improved CPG network proposed based on the present embodiment, and it can be seen that there is a distinct jump point in the method before the improvement, and the jump point of the method after the improvement disappears, and the response is rapid and the lag is not distinct.
(2) For the case of frequency mutations: suppose the initial conditions of the simulation aref =1.5，
，Ai=50，
=0.01, the swing frequency of the joint is abruptly changed from 1.5 to 0.2, and fig. 8 (a) and 8 (b) respectively show frequency abrupt change comparison simulations of a CPG network constructed based on a hopf oscillator and an improved CPG network proposed based on the present embodiment, and it can be seen thatOut, improved post-handover smoothing.
(3) For the case of a phase jump: in the parameter
Under the condition of no change, the swing frequency is changed to carry out simulation, and the initial simulation condition is assumed to bef =1，
，Ai=10，
=0.002, the frequency was changed from 1.0 to 0.2, and phase jump comparison simulations of the CPG network configured based on the hopf oscillator and the improved CPG network proposed based on the present embodiment are shown in (a) of fig. 9 and (b) of fig. 9, respectively, and it can be seen that the parameters before the improvement
Not all frequencies are used, the waveform has been distorted, and the improvement solves the problem.
(4) For the case of simultaneous sudden changes in amplitude and phase frequencies: suppose the initial conditions of the simulation aref =0.5，
，Ai=10，
=0.01 and the mutation parameter isf =0.5，
，A1=8，A2=12，
Fig. 10 shows simulation results of the improved CPG network, and it can be seen that, when the amplitude and phase frequencies are all abrupt, the improved CPG algorithm can still smoothly and quickly track, and the performance is superior.
Based on the motion control model constructed by using the CPG network or the improved CPG network, referring to fig. 11, the present embodiment provides a method for controlling motion of a biomimetic robotic fish, exemplarily including the following steps S100 to S300:
and S100, determining relevant control parameters of all joints of the bionic robot fish 100 according to the expected advancing speed and steering speed.
The related control parameters may include, but are not limited to, a swing amplitude, a swing frequency, and a phase of the corresponding joint, a phase coupling strength between two preset oscillators corresponding to two connected joints, a phase difference between two connected joints, and the like, and may be specifically selected according to actual requirements.
For the biomimetic robotic fish 100, the moving speed may include a forward speed (also linear speed) in a forward direction and a turning speed (also angular speed) in any rotating direction, wherein the two speeds can be set by a desired moving track or moving speed, and the like. For each speed, a corresponding relation table stored in advance can be inquired or corresponding throttle control quantity can be obtained by calculation according to a preset formula, and further, different throttle control quantities correspond to the respective swing amplitude, swing frequency and the like of each joint.
S200, inputting the relevant control parameters into the motion control model of the bionic robot fish 100 to perform parameter smooth transition preprocessing and angle closed-loop control operation so as to obtain the target rotation angles of all joints of the bionic robot fish 100. The motion control model comprises a central mode generator network formed based on the preset oscillator, and the central mode generator network is used for executing angle closed-loop control operation so as to control the motion of the bionic robot fish 100.
In one embodiment, the motion control model may include a CPG network formed based on the improved oscillator, and when performing the angle closed-loop control operation, as shown in fig. 12, the step S200 includes the following sub-steps S210 to S230:
s210, the swing amplitude after the normalization processing,Inputting the smoothed swing frequency, phase coupling strength and phase difference into preset oscillators of corresponding joints in the central pattern generator network to calculate intermediate state variables of the preset oscillators, namelyx i Andy i 。
s220, calculating according to the intermediate state variable and the swing amplitude after smoothing to obtain the state output variable of each preset oscillator, namely xoutiAnd youti。
S230, determining the target rotation angle of each joint according to the state output variable of each preset oscillator, namely enabling
. Thereby, the target rotation angles of all the joints can be calculated.
Then, after the target rotation angles of all the joints are obtained, step S300 is performed.
And S300, controlling each joint steering engine of the bionic robot fish 100 according to the target rotation angle so as to enable the bionic robot fish 100 to execute expected actions.
For example, each joint steering engine may be implemented by a small servo motor, so that the target rotation angle required by each joint may be transmitted to the motor of the corresponding joint to drive the motor to rotate by the corresponding angle, thereby enabling the biomimetic robotic fish 100 to perform the desired motion.
The bionic robot fish motion control method provided by the embodiment of the application carries out structural improvement on the oscillator used for rhythm motion simulation and adopted by the joints of the bionic robot fish 100, carries out smooth transition pretreatment on input control parameters, and carries out angle closed-loop control operation on a CPG (compact peripheral component network) network formed based on the improved oscillator structure, so that the oscillator can be rapidly and smoothly transited to a new limit ring balance state, in addition, local decoupling can be carried out on the coupling relation between the swing frequency and the phase coupling strength so as to facilitate parameter calculation, quick and smooth change of the output of the motion control model can be responded, the system power consumption is reduced, the service life of each joint of the robot fish is prolonged, and the like.
Referring to fig. 13, based on the above-mentioned embodiment of the method for controlling the motion of the biomimetic robotic fish, this embodiment provides a biomimetic robotic fish motion control apparatus 200, wherein each joint of the biomimetic robotic fish 100 uses the above-mentioned preset oscillator to perform rhythm motion simulation, and exemplarily, the biomimetic robotic fish motion control apparatus 200 includes:
and the parameter determining module 210 is used for determining relevant control parameters of all joints of the bionic robot fish 100 according to the expected advancing speed and the steering speed.
The angle calculation module 220 is configured to input the relevant control parameters into the motion control model of the biomimetic robotic fish 100 to perform parameter smooth transition preprocessing and angle closed-loop control operation, so as to obtain target rotation angles of each joint of the biomimetic robotic fish 100; the motion control model comprises a central mode generator network formed based on the preset oscillator, and the central mode generator network is used for executing the angle closed-loop control operation;
and the joint control module 230 is configured to control each joint steering engine of the biomimetic robotic fish 100 according to the target rotation angle, so that the biomimetic robotic fish 100 executes a desired action.
It is to be understood that the apparatus of the present embodiment corresponds to the method of the above embodiment, and the alternatives in the above embodiment are also applicable to the present embodiment, so that the description is not repeated here.
The application further provides a biomimetic robotic fish 100, exemplarily, the biomimetic robotic fish 100 includes a processor and a memory, where the memory stores a computer program, and the processor executes the computer program, so as to enable the terminal device to execute the functions of each module in the above-mentioned biomimetic robotic fish motion control method or the above-mentioned biomimetic robotic fish motion control apparatus.
The present application also provides a readable storage medium for storing the computer program used in the biomimetic robotic fish 100 described above.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, each functional module or unit in each embodiment of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a smart phone, a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application.
Claims (8)
1. A bionic robot fish motion control method is characterized in that each joint of the bionic robot fish adopts a preset oscillator to perform rhythm motion simulation, and the method comprises the following steps:
determining relevant control parameters of each joint of the bionic robot fish according to the expected advancing speed and the steering speed; the relevant control parameters comprise the swing amplitude and the swing frequency of the corresponding joint;
inputting the relevant control parameters into a motion control model of the bionic robot fish to carry out parameter smooth transition pretreatment and angle closed-loop control operation so as to obtain a target rotation angle of each joint of the bionic robot fish;
the motion control model comprises a central mode generator network formed based on the preset oscillator, and the central mode generator network is used for executing the angle closed-loop control operation; the preset oscillator is obtained by respectively smoothing the joint swing amplitude and the swing frequency in the hopf oscillator by utilizing a nonlinear transition function and normalizing the swing amplitude; the nonlinear transition function is constructed on the basis of a sigmoid function, and the expression is as follows:
wherein,
in the formula, z1And zinOutput and input quantities, ż, respectively, of the nonlinear transition function1Is a derivative of said output quantity and is,δis the linear interval coefficient of the sigmoid function,βis the convergence gain;
and controlling each joint steering engine of the bionic robot fish according to the target rotation angle so as to enable the bionic robot fish to execute expected actions.
2. The method for controlling the motion of the biomimetic robotic fish according to claim 1, wherein the biomimetic robotic fish comprises three joints connected in a neuron chain manner, and the related control parameters further comprise phase coupling strength between two preset oscillators corresponding to every two connected joints and phase difference between every two joints; the angle closed-loop control operation comprises:
inputting the oscillation amplitude subjected to normalization processing, the oscillation frequency subjected to smoothing processing, the phase coupling strength and the phase difference into preset oscillators of corresponding joints in the central mode generator network to calculate and obtain intermediate state variables of the preset oscillators;
calculating according to the intermediate state variable and the smoothed swing amplitude to obtain state output variables of the preset oscillators;
and determining the target rotation angle of each joint according to the state output variable of each preset oscillator.
3. The method for controlling the motion of the biomimetic robotic fish according to claim 2, wherein in the process of constructing the motion control model, further comprising:
locally decoupling the phase coupling strength between the preset oscillators and the swing frequency of a joint so as to improve the structure of a central mode generator network formed on the basis of the preset oscillators to obtain an improved central mode generator network; the improved neutral pattern generator network is used for executing the angle closed-loop control operation.
4. The biomimetic robotic fish motion control method of any of claims 1-3, wherein the preset oscillator is expressed as follows:
where x and y are two intermediate state variables of the preset oscillator, ẋ and ẏ are derivatives of the two intermediate state variables, k is a limit cycle convergence rate coefficient of the preset oscillator,NL() Representing the non-linear transition function in question,fis the frequency of the oscillation of the joint,
and
respectively, a first convergence gain and a first linear interval coefficient, a is the swing amplitude of the joint,
and
respectively a second convergence gain and a second linear interval coefficient, xoutAnd youtOutputting variables for the two states of the preset oscillator.
5. The biomimetic robotic fish motion control method of claim 3, wherein the modified central pattern generator network is expressed as follows:
in the formula, xiAnd yiIs the intermediate state variable of the ith preset oscillator,ẋiand ẏiAre each xiAnd yiK is a limit cycle convergence rate coefficient of the preset oscillator,NL() Representing the non-linear transition function in question,fis the frequency of the oscillation of the joint,
and
respectively a first convergence gain and a first linear interval coefficient,
and
respectively, a second convergence gain and a second linear interval coefficient, xoutiAnd youtiFor the state output variable, x, of the ith preset oscillatori-1And yi-1For the intermediate state variable of the (i-1) th preset oscillator,
for the phase coupling strength between the two preset oscillators,
is the phase difference from the ith joint to the jth joint, θiIs the target rotation angle of the ith joint, AiThe amplitude of the oscillation of the ith joint.
6. The utility model provides a bionical machine fish motion control device which characterized in that, every joint of bionical machine fish all adopts and predetermines the oscillator and carries out rhythm motion simulation, the device includes:
the parameter determination module is used for determining related control parameters of all joints of the bionic robot fish according to the expected advancing speed and steering speed; the relevant control parameters comprise the swing amplitude and the swing frequency of the corresponding joint;
the angle calculation module is used for inputting the related control parameters into a motion control model of the bionic robot fish to carry out parameter smooth transition preprocessing and angle closed-loop control operation so as to obtain target rotation angles of all joints of the bionic robot fish;
the motion control model comprises a central mode generator network formed based on the preset oscillator, and the central mode generator network is used for executing the angle closed-loop control operation; the preset oscillator is obtained by respectively smoothing the joint swing amplitude and the swing frequency in the hopf oscillator by utilizing a nonlinear transition function and normalizing the swing amplitude; the nonlinear transition function is constructed on the basis of a sigmoid function, and the expression is as follows:
wherein,
in the formula, z1And zinOutput and input quantities, ż, respectively, of said non-linear transition function1Is the derivative of said output quantity and,δis the linear interval coefficient of the sigmoid function,βis the convergence gain;
and the joint control module is used for controlling each joint steering engine of the bionic robot fish according to the target rotation angle so as to enable the bionic robot fish to execute the expected action.
7. A biomimetic robotic fish, comprising a processor and a memory, the memory storing a computer program, the processor being configured to execute the computer program to implement the biomimetic robotic fish motion control method of any of claims 1-5.
8. A readable storage medium, characterized in that it stores a computer program which, when executed on a processor, implements the biomimetic robotic fish motion control method according to any of claims 1-5.
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