CN108983770A - Data processing method, device, electronic equipment and storage medium - Google Patents
Data processing method, device, electronic equipment and storage medium Download PDFInfo
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- CN108983770A CN108983770A CN201810708241.XA CN201810708241A CN108983770A CN 108983770 A CN108983770 A CN 108983770A CN 201810708241 A CN201810708241 A CN 201810708241A CN 108983770 A CN108983770 A CN 108983770A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0259—Control of position or course in two dimensions specially adapted to land vehicles using magnetic or electromagnetic means
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0285—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using signals transmitted via a public communication network, e.g. GSM network
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Abstract
The present invention proposes a kind of data processing method, device, electronic equipment and storage medium, it is related to robot control field, this method comprises: calculating when the respective first position fitness value of all problems solution particle in former generation particle populations, with the average optimal individual particles and the respective second position fitness value of all individual optimal particles of contemporary optimal particle population, and after in contemporary optimal particle population determining group's optimal particle, calculate separately acquisition when in former generation particle populations each when the corresponding next-generation solution particle of former generation solution particle；So that the number of iterations when former generation particle populations reaches preset maximum number of iterations, when either the position adaptive value of group's optimal particle is less than preset termination threshold value, group's optimal particle is exported, and then mobile robot is made to carry out state control according to group's optimal particle.A kind of data processing method, device, electronic equipment and storage medium provided by the invention, are able to ascend the control efficiency of mobile robot.
Description
Technical field
The present invention relates to robot control fields, in particular to a kind of data processing method, device, electronic equipment
And storage medium.
Background technique
Two wheel mobile robots possess the ability of relatively easy mechanical structure and autonomic balance, it is easier to it manipulates,
It is widely used in every field, can be used as exploration, rescue, the mobile platform in search mission.Advocating cleaning energy simultaneously
Source instantly, can also be transformed into the balanced bi-wheel vehicle welcome by user.Two wheel mobile robots are substantially one
Two-wheeled inverted pendulum, two coaxial wheels are separately mounted to body two sides, and whole mass center is located at the top of wheel shaft, and therefore, trolley must
Balance must be maintained by the movement of itself.Inverted pendulum is a kind of typical nonlinear system, highly unstable and vulnerable to interference, quilt
It is widely used as studying the display platform of various control strategies.
Summary of the invention
The purpose of the present invention is to provide a kind of data processing method, device, electronic equipment and storage mediums, are able to ascend
The control efficiency of mobile robot.
To achieve the goals above, technical solution used in the embodiment of the present invention is as follows:
In a first aspect, the state for mobile robot controls the embodiment of the invention provides a kind of data processing method,
The described method includes: calculating the average optimal individual grain for working as the corresponding present age optimal particle population P (t) of former generation particle populations X (t)
Sub- C (t), wherein described to work as in former generation particle populations X (t) comprising multiple as former generation solution particle Xi(t), each described to ask
Key to exercises particle Xi(t) include preset quantity parameter, the present age optimal particle population P (t) includes multiple individual optimal particle Pi
(t), each individual optimal particle PiIt (t) is corresponding described problem solution particle Xi(t) it is solved in the problem of all grey iterative generations
The smallest solution particle of fitness value in particle, t characterize current iteration number；It calculates described when in former generation particle populations X (t)
All problems solution particle Xi(t) respective first position fitness value F1i(t) institute and in the present age optimal particle population P (t)
There is individual optimal particle Pi(t) respective second position fitness value F2i(t), and in the present age optimal particle population P (t)
Determine group optimal particle G (t), wherein the group optimal particle G (t) is in the present age optimal particle population P (t)
Second position adaptive value F2i(t) the smallest individual optimal particle Pi(t)；According to all problems solution particle Xi(t) respective
First position fitness value F1i(t), all individual optimal particle Pi(t) respective second position fitness value F2i(t), institute
State contemporary optimal particle population P (t), preset contraction-coefficient of expansion α, the average optimal individual particles C (t), the group
Optimal particle G (t), preset particle diversity function gsi,j(t) and it is the multiple as former generation solution particle Xi(t), respectively
It calculates and obtains described work as in former generation particle populations X (t) each as former generation solution particle Xi(t) corresponding next-generation problem
Solve particle Xi(t+1)；Judgement is described when whether the number of iterations t of former generation particle populations X (t) reaches preset maximum number of iterations
Whether T or the position fitness value of the group optimal particle G (t) are less than preset termination threshold value；Work as former generation when described
The number of iterations t of particle populations X (t) reaches the preset maximum number of iterations T or the group optimal particle G (t)
Position adaptive value be less than the preset termination threshold value when, the group optimal particle G (t) is exported, so that the movement machine
People carries out state control according to the group optimal particle G (t).
Second aspect, the embodiment of the invention provides a kind of data processing equipment, the state for mobile robot is controlled,
Described device includes: average optimal individual particles computing module, works as former generation particle populations X (t) the corresponding present age most for calculating
The average optimal individual particles C (t) of excellent particle populations P (t), wherein described to work as in the former generation particle populations X (t) comprising multiple
Former generation solution particle Xi(t), each described problem solution particle Xi(t) include preset quantity parameter, it is described the present age optimal particle
Population P (t) includes multiple individual optimal particle Pi(t), each individual optimal particle PiIt (t) is corresponding described problem solution
Particle Xi(t) the smallest solution particle of fitness value in particle is solved in the problem of all grey iterative generations, t characterizes current iteration time
Number；Position fitness value calculation module, it is described as all problems solution particle X in former generation particle populations X (t) for calculatingi(t) each
From first position fitness value F1i(t) all individual optimal particle P and in the present age optimal particle population P (t)i(t) each
From second position fitness value F2i(t)；Group's optimal particle determining module, in the present age optimal particle population P (t)
In determine group optimal particle G (t), wherein the group optimal particle G (t) be the present age optimal particle population P (t)
Middle second position adaptive value F2i(t) the smallest individual optimal particle Pi(t)；Population particle iteration module, for according to the institute
Problematic solution particle Xi(t) respective first position fitness value F1i(t), all individual optimal particle Pi(t) respective
Second position fitness value F2i(t), the present age optimal particle population P (t), preset contraction-coefficient of expansion α, described average
Optimum individual particle C (t), the group optimal particle G (t), preset particle diversity function gsi,j(t) and the multiple work as
Former generation solution particle Xi(t), it calculates separately and works as in former generation particle populations X (t) described in acquisition each as former generation solution particle Xi
(t) corresponding next-generation solution particle Xi(t+1)；Judgment module, it is described as former generation particle populations X (t) for judging
The number of iterations t whether reach the position fitness of preset maximum number of iterations T or the group optimal particle G (t)
Whether value is less than preset termination threshold value；Parameter output module, for as the number of iterations t for working as former generation particle populations X (t)
Reach the position adaptive value of the preset maximum number of iterations T or the group optimal particle G (t) less than described pre-
If termination threshold value when, the group optimal particle G (t) is exported, so that the mobile robot is according to group's optimal particle G
(t) state control is carried out.
The third aspect, the embodiment of the invention provides a kind of electronic equipment, the electronic equipment includes memory, for depositing
Store up one or more programs；Processor.When one or more of programs are executed by the processor, above-mentioned data are realized
Processing method.
Fourth aspect, the embodiment of the invention provides a kind of computer readable storage mediums, are stored thereon with computer journey
Sequence, the computer program realize above-mentioned data processing method when being executed by processor.
Compared with the existing technology, it a kind of data processing method, device provided by the embodiment of the present invention, electronic equipment and deposits
Storage media works as the average optimal individual particles of the corresponding present age optimal particle population P (t) of former generation particle populations X (t) by calculating
C (t), and calculate as all problems solution particle X in former generation particle populations X (t)i(t) respective first position fitness value F1i(t)
And all individual optimal particle P in present age optimal particle population P (t)i(t) respective second position fitness value F2i(t), and
Acquisition is calculated separately as former generation particle populations X (t) after determining group optimal particle G (t) in contemporary optimal particle population P (t)
In each as former generation solution particle Xi(t) corresponding next-generation solution particle Xi(t+1), to complete to work as former generation problem
Solve particle Xi(t) after iteration, whether judgement reaches preset greatest iteration time as the number of iterations t of former generation particle populations X (t)
Whether the position fitness value of number T or group optimal particle G (t) is less than preset termination threshold value, so that when working as former generation grain
The number of iterations t of sub- population X (t) reaches preset maximum number of iterations T, or when the position of group optimal particle G (t) is suitable
When should be worth less than preset termination threshold value, export the group optimal particle G (t), so make mobile robot according to the group most
Excellent particle G (t) carries out state control, compared with the prior art, controls the state of mobile robot and is no longer dependent on average mark
With the mode that either trial-and-error method etc. artificially configures, but state control is carried out by the way of automatically generating, is able to ascend
The control efficiency of mobile robot.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 shows a kind of schematic diagram of a kind of electronic equipment provided by the embodiment of the present invention；
Fig. 2 shows a kind of a kind of schematic flow charts of data processing method provided by the embodiment of the present invention；
Fig. 3 is a kind of schematic flow chart of the sub-step of step S200 in Fig. 2；
Fig. 4 shows a kind of a kind of schematic diagram of data processing equipment provided by the embodiment of the present invention；
Fig. 5 shows one kind of group's optimal particle determining module of data processing equipment provided by the embodiment of the present invention
Schematic diagram.
In figure: 10- electronic equipment；20- data processing equipment；110- memory；120- processor；130- storage control
Device；140- Peripheral Interface；150- radio frequency unit；160- communication bus/signal wire；200- average optimal individual particles calculate mould
Block；The position 300- fitness value calculation module；400- group optimal particle determining module；Fitness value traversal in the position 410- is single
Member；420- group optimal particle determination unit；500- population particle iteration module；600- judgment module；700- parameter exports mould
Block；800- iterative cycles module.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.The present invention being usually described and illustrated herein in the accompanying drawings is implemented
The component of example can be arranged and be designed with a variety of different configurations.
Therefore, the detailed description of the embodiment of the present invention provided in the accompanying drawings is not intended to limit below claimed
The scope of the present invention, but be merely representative of selected embodiment of the invention.Based on the embodiments of the present invention, this field is common
Technical staff's every other embodiment obtained without creative efforts belongs to the model that the present invention protects
It encloses.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.Meanwhile of the invention
In description, term " first ", " second " etc. are only used for distinguishing description, are not understood to indicate or imply relative importance.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
With reference to the accompanying drawing, it elaborates to some embodiments of the present invention.In the absence of conflict, following
Feature in embodiment and embodiment can be combined with each other.
Fuzzy logic control presents brilliant performance in terms of processing uncertainty and interference, is widely used
In robot control.Type-2 fuzzy sets belong to the degree of some fuzzy set using a type fuzzy set description object, to have
It improves system to effect and handles probabilistic ability.In two Oriented Fuzzy Control Systems, traditional parameter setting is generallyd use
Membership function parameter in method (mean allocation or trial-and-error method) configuration control rule, i other words artificially configuring corresponding parameter
Carry out the control of robot.Although can satisfy demand for control under normal circumstances, it is corresponding to configure to need labor intensive
Parameter, and efficiency is lower.
Based on defect of the existing technology, inventor's a kind of settling mode provided by the embodiment of the present invention are as follows: adopt
The state control of mobile robot is carried out with the mode of the automatic allocation optimum of Fuzzy Controller Parameters.
Specifically, referring to Fig. 1, one kind that Fig. 1 shows a kind of electronic equipment 10 provided by the embodiment of the present invention is shown
Meaning property structure chart, it is smart phone, PC (personal that the electronic equipment 10, which may be, but not limited to,
Computer, PC), tablet computer, personal digital assistant (personal digital assistant, PDA) etc., the electricity
Sub- equipment 10 includes memory 110, storage control 130, one or more (one is only shown in figure) processors 120, peripheral hardware
Interface 140, radio frequency unit 150 etc..These components are mutually communicated by one or more communication bus/signal wire 160.
Memory 110 can be used for storing software program and mould group, the dress of the data processing as provided by the embodiment of the present invention
20 corresponding program instructions/mould group is set, processor 120 passes through the software program and mould group that operation is stored in memory 110,
Thereby executing various function application and data processing, the data processing method as provided by the embodiment of the present invention.
Wherein, the memory 110 may be, but not limited to, random access memory (Random Access
Memory, RAM), read-only memory (Read Only Memory, ROM), programmable read only memory (Programmable
Read-Only Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only
Memory, EPROM), electricallyerasable ROM (EEROM) (Electric Erasable Programmable Read-Only
Memory, EEPROM) etc..
Processor 120 can be a kind of IC chip, have signal handling capacity.Above-mentioned processor 120 can be with
It is general processor, including central processing unit (Central Processing Unit, CPU), network processing unit (Network
Processor, NP), speech processor and video processor etc.；Can also be digital signal processor, specific integrated circuit,
Field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components.
It may be implemented or execute disclosed each method, step and the logic diagram in the embodiment of the present invention.General processor can be
Microprocessor or the processor 120 are also possible to any conventional processor etc..
Various input/output devices are couple processor 120 and memory 110 by Peripheral Interface 140.In some implementations
In example, Peripheral Interface 140, processor 120 and storage control 130 can be realized in one single chip.The present invention other
Some embodiments in, they can also be realized by independent chip respectively.
Radio frequency unit 150 is used to receive and transmit electromagnetic wave, realizes the mutual conversion of electromagnetic wave and electric signal, thus with
Communication network or other equipment are communicated.
It is appreciated that structure shown in FIG. 1 is only to illustrate, electronic equipment 10 includes more or less than shown in Fig. 1
Component, or with the configuration different from shown in Fig. 1.Each component shown in Fig. 1 can use hardware, software, or its combination
It realizes.
Specifically, referring to Fig. 2, Fig. 2 shows a kind of one kind of data processing method provided by the embodiment of the present invention
Schematic flow chart, which can be used for the state control of mobile robot, such as two wheel mobile robots, at this
In inventive embodiments, the data processing method the following steps are included:
Step S100 calculates the average optimal individual particles when the corresponding contemporary optimal particle population of former generation particle populations.
In QPSQ algorithm (quanta particle swarm optimization), by a the problem of representing potential problems solution solution particle X of MiGroup granulating
Sub- population X, i.e. X={ X1,X2,…,XM, each solution particle XiThere are in the target search space of a N-dimensional.In QPSQ
In algorithm, by not stopping iteration, finally make some solution particle X in certain generation particle populations X (t)i(t), can make to move
The state control precision of mobile robot reaches preset range.
Specifically, particle populations X crosses Cheng Qian in iteration, it is necessary first to which the population X (0) for generating initialization, is iteration
The particle populations X that number is 0.Wherein, the population X (0) of the initialization each of solution particle Xi(0), exist
In the target search space of one N-dimensional, i other words, each solution particle Xi(0) include preset quantity parameter, i.e.,
It is N number of.And each parameter is generated at random in the range of user presets, and in the same solution particle Xi(0)
In any two parameter it is different.After the population X (0) for generating initialization, i.e., calculated using the population X (0) as QPSQ
The input of method, by ceaselessly iteration, it is assumed that after iteration t times, when former generation particle populations are expressed as X (t), and work as former generation particle
The all problems solution particle that population X (t) includes is X (t)={ X1(t),X2(t),…,XM(t)}。
Correspondingly, it after particle populations X iteration t times, in an iterative cycles calculating process, calculates work as former generation grain first
The average optimal individual particles C (t) of the corresponding present age optimal particle population P (t) of sub- population X (t).Wherein, with particle populations X
Continuous iteration, when iteration is to certain generation t, each solution particle XiAll grey iterative generations value in, i.e. Xi(0)、Xi
(1)、···Xi(t) in, the problem of uniquely determining solution particle X is certainly existedi(j), so that solution particle Xi(j) corresponding
Position adaptive value is less than or equal to Xi(0)、Xi(1)、···Xi(t) the position adaptive value of the other problems solution particle in, at this time
Xi(j) i.e. be known as iteration to t for when XiIndividual optimal particle Pi(t), it correspondingly, is asked when all in former generation particle populations X (t)
Key to exercises particle Xi(t) respective corresponding individual optimal particle Pi(t) set is present age optimal particle population P (t).
Correspondingly, with the continuous iteration for working as former generation particle populations X (t), contemporary optimal particle population P (t) is not also being stopped
Iteration update.Wherein, when iteration to t+1 for when, mode that contemporary optimal particle population P (t) iteration updates are as follows: more contemporary
The individual optimal particle P of each for including in optimal particle population P (t)i(t) corresponding to the particle populations X (t+1) in t+1 generation
The problem of position, solves particle Xi(t+1) respective positions adaptive value works as Pi(t) corresponding position adaptive value is corresponding less than X (t+1)
Position adaptive value when, take Pi(t) value is as Pi(t+1) value；Conversely, working as Pi(t) corresponding position adaptive value is greater than X (t+
1) when corresponding position adaptive value, take the value of X (t+1) as Pi(t+1) value.
Specifically, the calculation formula of the average optimal individual particles C (t) of contemporary optimal particle population P (t) are as follows:
I other words: it is flat
The value of each parameter of equal optimum individual particle C (t) is all optimal grains of individual that contemporary optimal particle population P (t) includes
Sub- Pi(t) arithmetic mean of instantaneous value in the parameter of corresponding position.
Step S200 is calculated when the respective first position fitness value of all problems solution particle in former generation particle populations and is worked as
For the respective second position fitness value of all individual optimal particles in optimal particle population, and in contemporary optimal particle population
Determine group's optimal particle.
Correspondingly, it after particle populations X iteration t times, in an iterative cycles calculating process, needs to calculate when former generation grain
All problems solution particle X in sub- population X (t)i(t) respective first position fitness value F1i(t) and contemporary optimal particle population P
(t) all individual optimal particle P ini(t) respective second position fitness value F2i(t).The process of calculating is to work as former generation particle
The problem solution particle X that some in population X (t) determinesk(t) it illustrates.
The status controlling packet of mobile robot includes balance control and position control, citing balance control explanation.For problem
Solve particle Xk(t), according to pre-set fuzzy control rule:
Wherein, θ indicates the pitch angle of robot, and Indicate rate of pitch, anduθIndicate the output of fuzzy controller, and uθ∈ [- 60,60], Rulei are indicated are as follows: as solution particle Xk
(t) parameter inWithRespectively equal to θ withWhen, the output u of fuzzy controllerθEqual to solution particle Xk(t) parameter inIt also is solution particle Xk(t) control of the robot is exported.
Also, the calculation formula of position fitness value are as follows:Wherein, λ indicates material calculation, tiIndicate i
Moment, θ (i) indicate the pitching angle error of i moment robot.By solution particle Xk(t) corresponding parameter is brought into above formula,
Solution particle X can be acquiredk(t) corresponding position fitness value.
It is appreciated that acquisition can be calculated when all problems solution in former generation particle populations X (t) according to same calculating process
Particle Xi(t) respective first position fitness value F1i(t) all individual optimal particles and in present age optimal particle population P (t)
Pi(t) respective second position fitness value F2i(t), it will not repeat them here.
Correspondingly, all individual optimal particle P in obtaining present age optimal particle population P (t)i(t) respective second
Set fitness value F2i(t) after, i.e., group optimal particle G (t) is determined in the present age optimal particle population P (t).
Specifically, referring to Fig. 3, Fig. 3 is a kind of schematic flow chart of the sub-step of step S200 in Fig. 2, in this hair
In bright embodiment, step S200 includes following sub-step:
Sub-step S210, all individual respective second positions of optimal particle traversed in contemporary optimal particle population adapt to
Angle value.
When determining group optimal particle G (t), all optimal grains of individual in present age optimal particle population P (t) are traversed first
Sub- Pi(t) respective second position fitness value F2i(t), all individual optimal particle P are obtainedi(t) the respective second position is suitable
Answer angle value F2i(t)。
Sub-step S220 determines the fitness value the smallest optimal grain of individual in the second position in contemporary optimal particle population
Son, and the individual optimal particle to determine is as group's optimal particle.
All individual optimal particle P are obtained in traversali(t) respective second position fitness value F2i(t) after, then according to every
Individual optimal particle Pi(t) respective second position fitness value F2i(t), second position fitness value F is determined2i(t) most
Small individual optimal particle Pi(t), the individual optimal particle P and then with this determinedi(t) it is used as group optimal particle G (t).
Please continue to refer to Fig. 2, step S300, according to the respective first position fitness value of all problems solution particle, own
It is the respective second position fitness value of individual optimal particle, contemporary optimal particle population, preset contraction-coefficient of expansion, average
Optimum individual particle, group's optimal particle, preset particle diversity function and it is multiple work as former generation solution particle, calculate separately
It obtains and works as in former generation particle populations each when the corresponding next-generation solution particle of former generation solution particle.
Correspondingly, it obtains according to step S200 as all problems solution particle X in former generation particle populations X (t)i(t) respectively
First position fitness value F1i(t) all individual optimal particle P and in present age optimal particle population P (t)i(t) respective
Two position fitness value F2i(t) after and determining group optimal particle G (t) in contemporary optimal particle population P (t), i.e. foundation
All problems solution particle Xi(t) respective first position fitness value F1i(t), all individual optimal particle Pi(t) respective
Two position fitness value F2i(t), present age optimal particle population P (t), preset contraction-coefficient of expansion α, average optimal individual grain
Sub- C (t), group optimal particle G (t), preset particle diversity function gsi,j(t) and it is multiple as former generation solution particle Xi
(t), acquisition is calculated separately to work as in former generation particle populations X (t) each as former generation solution particle Xi(t) corresponding next generation
Solution particle Xi(t+1), that is, iteration goes out each to work as former generation solution particle Xi(t) respective next-generation solution particle
Xi(t+1), the next-generation particle populations X (t+1) for working as former generation particle populations X (t) is generated.
Specifically, when in former generation particle populations X (t) each as former generation solution particle Xi(t) corresponding next generation
Solution particle Xi(t) calculation formula are as follows:
Xi,j(t+1)=pi,j(t)±α|C(t)-gsi,j(t)·Xi,j(t) ln (1/u) |,
Wherein, j indicates j-th of parameter,And
And
σP(t)=(σ (Pi,1(t)),σ(Pi,2(t)),…,σ(Pi,D(t))), σP(t) the contemporary optimal particle kind is characterized
The standard deviation of group P (t), σX(t)=(σ (Xi,1(t)),σ(Xi,2(t)),…,σ(Xi,D(t))), σX(t) characterization is described works as former generation grain
The standard deviation of sub- population X (t), μ indicate 0~1 between random number, and when μ be section (0,0.5] in value when, Xi,j(t+1)
=pi,j(t)+α|C(t)-gsi,j(t)·Xi,j(t)·ln(1/u)|；And when μ is the value in section (0.5,1), Xi,j(t+1)
=pi,j(t)-α|C(t)-gsi,j(t)·Xi,j(t)·ln(1/u)|。
Do step S400, judgement have reached preset maximum number of iterations when the iteration of former generation particle populations herein? or
Does is it that the position fitness value of optimal particle in group is less than preset termination threshold value? when to be, step S500 is executed；When for
When no, work as former generation particle populations using the next-generation particle populations when former generation particle populations as new, continue to execute step S100.
Correspondingly, go out to work as in former generation particle populations X (t) each as former generation solution particle X according to step S300 iterationi
(t) corresponding next-generation solution particle Xi(t+1) after, i.e., the number of iterations t of former generation particle populations X (t) is worked as in judgement is
It is preset whether the no position fitness value for reaching preset maximum number of iterations T or group optimal particle G (t) is less than
Threshold value is terminated, which is preset maximum position fitness value.Wherein, when former generation particle populations X's (t)
When the number of iterations t reaches preset maximum number of iterations T, iterative evolution is completed in characterization particle populations X, should terminate to change at this time
The process that generation evolves, avoids falling into endless loop, executes step S500 at this time；Either, when the position of group optimal particle G (t)
When fitness value is less than preset termination threshold value, the position fitness value of group optimal particle G (t) is characterized already less than preset
Maximum position fitness value can satisfy use using the state control that the group optimal particle G (t) carries out mobile robot at this time
The required precision at family executes step S500 at this time；Otherwise, when the number of iterations t as former generation particle populations X (t) is not up to default
Maximum number of iterations T, and the position adaptive value of group optimal particle G (t) be greater than or equal to preset termination threshold value when, at this time
Work as former generation particle populations using the next-generation particle populations X (t+1) as former generation particle populations X (t) as new, continues to execute step
Rapid S100 continues iterative cycles.
Step S500 exports group's optimal particle.
When the number of iterations t for working as former generation particle populations X (t) according to step S400 judgement reaches preset maximum number of iterations
When the position fitness value of T or group optimal particle G (t) is less than preset termination threshold value, particle populations X at this time is characterized
Iteration is completed, at this time i.e. output group optimal particle G (t) so that mobile robot according to the group optimal particle G (t) into
The control of row state.
Based on above-mentioned design, a kind of data processing method provided by the embodiment of the present invention works as former generation particle by calculating
The average optimal individual particles C (t) of the corresponding present age optimal particle population P (t) of population X (t), and calculate and work as former generation particle populations
All problems solution particle X in X (t)i(t) respective first position fitness value F1i(t) and in present age optimal particle population P (t)
All individual optimal particle Pi(t) respective second position fitness value F2i(t), and it is true in contemporary optimal particle population P (t)
Make calculate separately after group optimal particle G (t) acquisition when in former generation particle populations X (t) each as former generation solution particle Xi
(t) corresponding next-generation solution particle Xi(t+1), to complete to work as former generation solution particle Xi(t) after iteration, judgement
When whether the number of iterations t of former generation particle populations X (t) reaches preset maximum number of iterations T or group optimal particle G
(t) whether position fitness value is less than preset termination threshold value, so that when the number of iterations t as former generation particle populations X (t) reaches
To preset maximum number of iterations T, or when the position adaptive value of group optimal particle G (t) is less than preset termination threshold value
When, the group optimal particle G (t) is exported, and then mobile robot is made to carry out state control according to the group optimal particle G (t),
Compared with the prior art, it controls the state of mobile robot and is no longer dependent on the artificial configuration such as mean allocation either trial-and-error method
Mode, but carry out state control by the way of automatically generating, be able to ascend the control efficiency of mobile robot.
Referring to Fig. 4, Fig. 4 shows a kind of one kind of data processing equipment 20 provided by the embodiment of the present invention schematically
Structure chart, state of the data processing equipment 20 for mobile robot control, in embodiments of the present invention, data processing dress
20 are set to determine including average optimal individual particles computing module 200, position fitness value calculation module 300, group's optimal particle
Module 400, population particle iteration module 500, judgment module 600 and parameter output module 700.
Average optimal individual particles computing module 200 works as the corresponding present age optimal grain of former generation particle populations X (t) for calculating
The average optimal individual particles C (t) of sub- population P (t), wherein described to work as former generation comprising multiple in the former generation particle populations X (t)
Solution particle Xi(t), each described problem solution particle Xi(t) include preset quantity parameter, the present age optimal particle population
P (t) includes multiple individual optimal particle Pi(t), each individual optimal particle PiIt (t) is corresponding described problem solution particle
Xi(t) the smallest solution particle of fitness value in particle is solved in the problem of all grey iterative generations, t characterizes current iteration number.
Position fitness value calculation module 300 is described when all problems solution particle in former generation particle populations X (t) for calculating
Xi(t) respective first position fitness value F1i(t) all individual optimal particles and in the present age optimal particle population P (t)
Pi(t) respective second position fitness value F2i(t)。
Group's optimal particle determining module 400 is for determining that group is optimal in the present age optimal particle population P (t)
Particle G (t), wherein the group optimal particle G (t) is second position adaptive value in the present age optimal particle population P (t)
F2i(t) the smallest individual optimal particle Pi(t)。
Specifically, referring to Fig. 5, Fig. 5 shows the group of data processing equipment 20 provided by the embodiment of the present invention most
A kind of schematic diagram of excellent particle determining module 400, in embodiments of the present invention, optimal particle determining module includes position
Fitness value Traversal Unit 410 and group's optimal particle determination unit 420.
Position fitness value Traversal Unit 410 is used to traverse all individuals in the present age optimal particle population P (t) most
Excellent particle Pi(t) respective second position fitness value F2i(t)。
Group's optimal particle determination unit 420 is for determining that the second position is suitable in the present age optimal particle population P (t)
Answer the smallest individual optimal particle P of angle valuei(t), and using the individual optimal particle determined as group optimal particle G
(t)。
Please continue to refer to Fig. 4, population particle iteration module 500 is used for according to all problems solution particle Xi(t) respectively
First position fitness value F1i(t), all individual optimal particle Pi(t) respective second position fitness value F2i(t)、
The present age optimal particle population P (t), preset contraction-coefficient of expansion α, the average optimal individual particles C (t), the group
Body optimal particle G (t), preset particle diversity function gsi,j(t) and it is the multiple as former generation solution particle Xi(t), divide
Described work as in former generation particle populations X (t) each as former generation solution particle X Ji Suan not obtainedi(t) corresponding next generation asks
Key to exercises particle Xi(t+1)。
Judgment module 600 be used to judge it is described when the number of iterations t of former generation particle populations X (t) whether reach it is preset most
Whether big the number of iterations T or the position fitness value of the group optimal particle G (t) are less than preset termination threshold value.
Parameter output module 700 is used for when described when the number of iterations t of former generation particle populations X (t) reaches described preset
When the position adaptive value of maximum number of iterations T or the group optimal particle G (t) is less than the preset termination threshold value,
The group optimal particle G (t) is exported, so that the mobile robot carries out state control according to the group optimal particle G (t)
System.
Please continue to refer to Fig. 4, as an implementation, the data processing equipment 20 further include:
Iterative cycles module 800 is used for when described when the number of iterations t of former generation particle populations X (t) is not up to described preset
Maximum number of iterations T, and the position adaptive value of the group optimal particle G (t) be greater than or equal to the preset termination threshold
When value, with the next-generation particle populations X as former generation particle populations X (t)i(t+1) work as former generation particle populations as new, after
Continue the average optimal individual particles computing module 200 and executes calculating when the corresponding contemporary optimal particle of former generation particle populations X (t)
The average optimal individual particles C (t) of population P (t).
In embodiment provided herein, it should be understood that disclosed device and method, it can also be by other
Mode realize.The apparatus embodiments described above are merely exemplary, for example, the flow chart and block diagram in attached drawing are shown
The architecture, function and operation in the cards of device according to an embodiment of the present invention, method and computer program product.
In this regard, each box in flowchart or block diagram can represent a part of a module, section or code, the mould
A part of block, program segment or code includes one or more executable instructions for implementing the specified logical function.Also it answers
When note that function marked in the box can also be to be different from being marked in attached drawing in some implementations as replacement
The sequence of note occurs.For example, two continuous boxes can actually be basically executed in parallel, they sometimes can also be by opposite
Sequence execute, this depends on the function involved.It is also noted that each box in block diagram and or flow chart and
The combination of box in block diagram and or flow chart can use the dedicated hardware based system for executing defined function or movement
System is to realize, or can realize using a combination of dedicated hardware and computer instructions.
In addition, each functional module in embodiments of the present invention can integrate one independent part of formation together,
It can be modules individualism, an independent part can also be integrated to form with two or more modules.
It, can be with if the function is realized and when sold or used as an independent product in the form of software function module
It is stored in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, server or network equipment etc.) execute the method for the embodiment of the present invention all or part of the steps.And it is preceding
The storage medium stated includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory
The various media that can store program code such as (RAM, Random Access Memory), magnetic or disk.
In conclusion a kind of data processing method, device provided by the embodiment of the present invention, electronic equipment and storage are situated between
Matter works as the average optimal individual particles C of the corresponding present age optimal particle population P (t) of former generation particle populations X (t) by calculating
(t), it and calculates as all problems solution particle X in former generation particle populations X (t)i(t) respective first position fitness value F1i(t)
And all individual optimal particle P in present age optimal particle population P (t)i(t) respective second position fitness value F2i(t), and
Acquisition is calculated separately as former generation particle populations X (t) after determining group optimal particle G (t) in contemporary optimal particle population P (t)
In each as former generation solution particle Xi(t) corresponding next-generation solution particle Xi(t+1), to complete to work as former generation problem
Solve particle Xi(t) after iteration, whether judgement reaches preset greatest iteration time as the number of iterations t of former generation particle populations X (t)
Whether the position fitness value of number T or group optimal particle G (t) is less than preset termination threshold value, so that when working as former generation grain
The number of iterations t of sub- population X (t) reaches preset maximum number of iterations T, or when the position of group optimal particle G (t) is suitable
When should be worth less than preset termination threshold value, export the group optimal particle G (t), so make mobile robot according to the group most
Excellent particle G (t) carries out state control, compared with the prior art, controls the state of mobile robot and is no longer dependent on average mark
With the mode that either trial-and-error method etc. artificially configures, but state control is carried out by the way of automatically generating, is able to ascend
The control efficiency of mobile robot.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
Claims (10)
1. a kind of data processing method, which is characterized in that the state for mobile robot controls, which comprises
The average optimal individual particles C (t) for working as the corresponding present age optimal particle population P (t) of former generation particle populations X (t) is calculated,
In, it is described to work as in former generation particle populations X (t) comprising multiple as former generation solution particle Xi(t), each described problem solution particle Xi
(t) include preset quantity parameter, the present age optimal particle population P (t) includes multiple individual optimal particle Pi(t), each
The individual optimal particle PiIt (t) is corresponding described problem solution particle Xi(t) it solves in particle and fits in the problem of all grey iterative generations
The smallest solution particle of angle value is answered, t characterizes current iteration number；
It calculates described as all problems solution particle X in former generation particle populations X (t)i(t) respective first position fitness value F1i
(t) all individual optimal particle P and in the present age optimal particle population P (t)i(t) respective second position fitness value F2i
(t), and in the present age optimal particle population P (t) group optimal particle G (t) is determined, wherein the optimal grain of group
Sub- G (t) is second position adaptive value F in the present age optimal particle population P (t)2i(t) the smallest individual optimal particle Pi(t)；
According to all problems solution particle Xi(t) respective first position fitness value F1i(t), all optimal grains of individual
Sub- Pi(t) respective second position fitness value F2i(t), the present age optimal particle population P (t), preset contraction-expansion system
Number α, the average optimal individual particles C (t), the group optimal particle G (t), preset particle diversity function gsi,j(t)
And it is the multiple as former generation solution particle Xi(t), it is described when each current in former generation particle populations X (t) to calculate separately acquisition
For solution particle Xi(t) corresponding next-generation solution particle Xi(t+1)；
Judgement is described when whether the number of iterations t of former generation particle populations X (t) reaches preset maximum number of iterations T or institute
Whether the position fitness value for stating group optimal particle G (t) is less than preset termination threshold value；
When described when the number of iterations t of former generation particle populations X (t) reaches the preset maximum number of iterations T or described
When the position adaptive value of group optimal particle G (t) is less than the preset termination threshold value, group's optimal particle G is exported
(t), so that the mobile robot carries out state control according to the group optimal particle G (t).
2. the method as described in claim 1, which is characterized in that the average optimal individual of the present age optimal particle population P (t)
The calculation formula of particle C (t) are as follows:
3. the method as described in claim 1, which is characterized in that described to be determined in the present age optimal particle population P (t)
The step of group optimal particle G (t), comprising:
Traverse all individual optimal particle P in the present age optimal particle population P (t)i(t) respective second position fitness
Value F2i(t)；
Determine fitness value the smallest individual optimal particle P in the second position in the present age optimal particle population P (t)i(t), and
Using the individual optimal particle determined as group optimal particle G (t).
4. the method as described in claim 1, which is characterized in that the calculating obtains described when every in former generation particle populations X (t)
It is a to work as former generation solution particle Xi(t) corresponding next-generation solution particle Xi(t+1) calculation formula are as follows:
Xi,j(t+1)=pi,j(t)±α|C(t)-gsi,j(t)·Xi,j(t)·ln(1/u)|；
Wherein,And
And
σP(t)=(σ (Pi,1(t)),σ(Pi,2(t)),…,σ(Pi,D(t))), σP(t) the contemporary optimal particle population P is characterized
(t) standard deviation, σX(t)=(σ (Xi,1(t)),σ(Xi,2(t)),…,σ(Xi,D(t))), σX(t) characterization is described works as former generation particle
The standard deviation of population X (t), μ indicate 0~1 between random number, when μ be section (0,0.5] in value when, Xi,j(t+1)=pi,j
(t)+α|C(t)-gsi,j(t)·Xi,j(t)·ln(1/u)|；And when μ is the value in section (0.5,1), Xi,j(t+1)=pi,j
(t)-α|C(t)-gsi,j(t)·Xi,j(t)·ln(1/u)|。
5. the method as described in claim 1, which is characterized in that the method also includes:
When described when the number of iterations t of former generation particle populations X (t) is not up to the preset maximum number of iterations T, and the group
When the position adaptive value of body optimal particle G (t) is greater than or equal to the preset termination threshold value, work as former generation particle populations with described
The next-generation particle populations X of X (t)i(t+1) work as former generation particle populations as new, continue to execute the calculating when former generation particle
The step of average optimal individual particles C (t) of the corresponding present age optimal particle population P (t) of population X (t).
6. a kind of data processing equipment, which is characterized in that the state for mobile robot controls, and described device includes:
Average optimal individual particles computing module, for calculating when the corresponding contemporary optimal particle population of former generation particle populations X (t)
The average optimal individual particles C (t) of P (t), wherein described to work as in former generation particle populations X (t) comprising multiple when former generation solution
Particle Xi(t), each described problem solution particle Xi(t) include preset quantity parameter, the present age optimal particle population P (t) packet
Containing multiple individual optimal particle Pi(t), each individual optimal particle PiIt (t) is corresponding described problem solution particle Xi(t) exist
The problem of all grey iterative generations, solves the smallest solution particle of fitness value in particle, and t characterizes current iteration number；
Position fitness value calculation module, it is described as all problems solution particle X in former generation particle populations X (t) for calculatingi(t) each
From first position fitness value F1i(t) all individual optimal particle P and in the present age optimal particle population P (t)i(t) each
From second position fitness value F2i(t)；
Group's optimal particle determining module, for determining group optimal particle G in the present age optimal particle population P (t)
(t), wherein the group optimal particle G (t) is second position adaptive value F in the present age optimal particle population P (t)2i(t)
The smallest individual optimal particle Pi(t)；
Population particle iteration module, for according to all problems solution particle Xi(t) respective first position fitness value F1i
(t), all individual optimal particle Pi(t) respective second position fitness value F2i(t), the contemporary optimal particle population
P (t), it preset contraction-coefficient of expansion α, the average optimal individual particles C (t), the group optimal particle G (t), presets
Particle diversity function gsi,j(t) and it is the multiple as former generation solution particle Xi(t), it calculates separately and works as former generation described in acquisition
Each as former generation solution particle X in particle populations X (t)i(t) corresponding next-generation solution particle Xi(t+1)；
Judgment module, it is described when whether the number of iterations t of former generation particle populations X (t) reaches preset greatest iteration for judging
Whether number T or the position fitness value of the group optimal particle G (t) are less than preset termination threshold value；
Parameter output module, for changing when described when the number of iterations t of former generation particle populations X (t) reaches the preset maximum
When the position adaptive value of generation number T or the group optimal particle G (t) is less than the preset termination threshold value, institute is exported
Group optimal particle G (t) is stated, so that the mobile robot carries out state control according to the group optimal particle G (t).
7. device as claimed in claim 6, which is characterized in that group's optimal particle determining module includes:
Position fitness value Traversal Unit, for traversing all individual optimal particles in the present age optimal particle population P (t)
Pi(t) respective second position fitness value F2i(t)；
Group's optimal particle determination unit, for determining second position fitness value in the present age optimal particle population P (t)
The smallest individual optimal particle Pi(t), and using the individual optimal particle determined as group optimal particle G (t).
8. device as claimed in claim 6, which is characterized in that described device further include:
Iterative cycles module, for being not up to the preset maximum as the number of iterations t for working as former generation particle populations X (t)
The number of iterations T, and the position adaptive value of the group optimal particle G (t) be greater than or equal to the preset termination threshold value when, with
The next-generation particle populations X as former generation particle populations X (t)i(t+1) work as former generation particle populations as new, described in continuation
Average optimal individual particles computing module, which executes to calculate, works as the corresponding present age optimal particle population P (t) of former generation particle populations X (t)
Average optimal individual particles C (t).
9. a kind of electronic equipment characterized by comprising
Memory, for storing one or more programs；
Processor；
When one or more of programs are executed by the processor, side according to any one of claims 1 to 5 is realized
Method.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program quilt
Processor realizes method according to any one of claims 1 to 5 when executing.
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