CN109872010A - Intelligent Mobile Robot method for allocating tasks - Google Patents
Intelligent Mobile Robot method for allocating tasks Download PDFInfo
- Publication number
- CN109872010A CN109872010A CN201910198217.0A CN201910198217A CN109872010A CN 109872010 A CN109872010 A CN 109872010A CN 201910198217 A CN201910198217 A CN 201910198217A CN 109872010 A CN109872010 A CN 109872010A
- Authority
- CN
- China
- Prior art keywords
- point
- task
- guarantee
- intelligent mobile
- mobile robot
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Feedback Control In General (AREA)
Abstract
This application involves a kind of Intelligent Mobile Robot method for allocating tasks.The Intelligent Mobile Robot method for allocating tasks includes: building task point set, and the task point set includes task point position and task point patrol task amount.The guarantee point quantity of crusing robot is set.The location optimization model for ensureing point is constructed according to the task point patrol task amount.According to the guarantee point quantity, the location optimization model is solved in conjunction with particle swarm algorithm and weighted Voronoi diagrams nomography, be protected a position and guarantee point task distribution.The Intelligent Mobile Robot method for allocating tasks is in conjunction with the location optimization model for ensureing point and the preset guarantee point quantity, the guarantee point position and guarantee point task distribution are optimized by particle swarm algorithm and weighted Voronoi diagrams nomography, optimal location and task allocation plan are obtained, to improve the routing inspection efficiency of Intelligent Mobile Robot.
Description
Technical field
This application involves Intelligent Mobile Robot technical fields, more particularly to a kind of Intelligent Mobile Robot task
Distribution method.
Background technique
When substation equipment is chronically at operating status, in order to ensure electrical equipment safe and stable operation and find in time
The defect or hidden danger of equipment need operations staff to carry out inspection to field device.But heavy workload, the low efficiency of manual inspection, and
Testing result often falls flat.Intelligent Mobile Robot can realize that the intelligence of substation is patrolled to a certain extent
Inspection, but scene big for region area, more than patrol task, exist in the related technology the distribution of crusing robot task it is uneven and
The low problem of routing inspection efficiency.
Summary of the invention
Based on this, it is necessary to which and routing inspection efficiency uneven for the distribution of crusing robot task in the related technology is low to ask
Topic, provides a kind of Intelligent Mobile Robot method for allocating tasks.
A kind of Intelligent Mobile Robot method for allocating tasks, comprising:
Building task point set, the task point set include task point position and task point patrol task amount；
The guarantee point quantity of crusing robot is set；
The location optimization model for ensureing point is constructed according to the task point patrol task amount；
According to the guarantee point quantity, mould is optimized to the addressing in conjunction with particle swarm algorithm and weighted Voronoi diagrams nomography
Type is solved, and be protected a position and guarantee point task distribution.
Above-mentioned Intelligent Mobile Robot method for allocating tasks, in conjunction with the location optimization model and preset for ensureing point
The guarantee point quantity, by particle swarm algorithm and weighted Voronoi diagrams nomography to the guarantee point position and guarantee point
Task distribution optimizes, and the optimal guarantee point task allocation plan is obtained, to improve Intelligent Mobile Robot
Routing inspection efficiency.
The addressing for constructing the guarantee point according to the task point patrol task amount is excellent in one of the embodiments,
Change model the step of include:
Calculate it is different ensure point between inspection machine for each person patrol task amount maximum difference normalized value as appoint
Be engaged in balance parameters Qcost；
The communication distance for calculating crusing robot limits the affecting parameters of the location optimization model as communication distance
Parameter Dcost；
According to the task balance parameter QcostParameter D is limited with the communication distancecostConstruct the location optimization model
MinZ=α Qcost+(1-α)Dcost, α is weight.
The communication distance limits parameter D in one of the embodiments,costAre as follows:
Wherein, DcommFor the practical maximum communication distance of crusing robot, D be crusing robot specified maximum communication away from
From.
It is described according to the guarantee point quantity in one of the embodiments, in conjunction with particle swarm algorithm and weighted Voronoi diagrams
The step of nomography solves the location optimization model, and a position of being protected is with point task distribution is ensured, comprising:
Initialization population, and using the particle position in the population as the guarantee point position；
The guarantee point task distribution is adjusted according to weighted Voronoi diagrams nomography；
Each adaptive value for ensureing point is calculated according to the location optimization model, and records each guarantee respectively
The history optimal solution of point and the globally optimal solution of the population；
Each guarantee point position and speed is updated, and returns and adjusts the guarantee according to weighted Voronoi diagrams nomography
The step of point task distribution, until meeting stopping criterion for iteration and exporting the guarantee point position and the guarantee point task minute
Match.
The step of adjusting guarantee point task distribution according to weighted Voronoi diagrams nomography in one of the embodiments,
Include:
Task point is distributed to the guarantee point according to the task point method of salary distribution；
According to the task point patrol task amount and the maximum ensured between point and the task point distributed
Communication distance distributes the guarantee point task and carries out adaptive weighting adjusting.
The adjustment criteria that the adaptive weighting is adjusted in one of the embodiments, are as follows:
WhenWhen, the weight for ensureing point reduces according to Adaptive Adjustment of Step Length；
WhenAndWhen, the weight for ensureing point increases according to Adaptive Adjustment of Step Length
Add；
WhenAndWhen, the guarantee point position cannot meet inspection requirement, update every
A guarantee point position and speed, and return and adjust the step for ensureing point task distribution according to weighted Voronoi diagrams nomography
Suddenly；
Wherein, QiFor the task amount that the guarantee point numbered by i distributes, QstdFor the specified inspection of single crusing robot
Task amount, diTo number the maximum communication distance between the guarantee point and distribution task point by i, D is the volume of crusing robot
Fixed maximum communication distance, t-1 is the number of iterations.
The range of Adaptive Adjustment of Step Length is 0.05~0.15 in one of the embodiments,.
The task point method of salary distribution in one of the embodiments, are as follows:
Wherein, x is any task point, pi、pjRespectively number be i, j guarantee point, λi、λjRespectively ensure point pi、pj
Weight, d (x, pi)、d(x,pj) it is respectively any task point and guarantee point pi、pjBetween Euclidean distance.
The stopping criterion for iteration in one of the embodiments, are as follows: current iteration number k and default maximum number of iterations
kmaxIt is equal.
The update mode for ensureing point position and speed in one of the embodiments, are as follows:
ω=ωmax-0.1*(ωmax-ωmin)*exp(k/kmax)
Wherein,To number the speed for ensureing point kth time iteration for being i,Guarantee point kth+1 time for being i for number
The speed of iteration, ω are inertial factor, and r (1) and r (2) are the random number between [0,1], c1、c2For Studying factors,WithThe history optimal solution of the guarantee point and the population that are i is respectively numbered,For the guarantor of kth time iteration
The set of barrier point position,For the set of the guarantee point position of+1 iteration of kth.
The task point is set to the position of examined in determination, the task point patrol task in one of the embodiments,
Amount is the activity duration of task point described in crusing robot inspection.
The step of guarantee point quantity of the setting crusing robot in one of the embodiments, comprising:
According to patrol task region area, patrol task amount, staffing and crusing robot quantity, inspection machine is set
The guarantee point quantity of people.
A kind of computer equipment, including memory and processor are stored with and can be run on a processor on the memory
Computer program, the processor realizes power transformation described in any one of claims 1 to 12 when executing the computer program
Stand crusing robot method for allocating tasks the step of.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
The step of Intelligent Mobile Robot method for allocating tasks described in any one of claims 1 to 12 is realized when row.
Detailed description of the invention
Fig. 1 is a kind of Intelligent Mobile Robot method for allocating tasks flow chart provided by the embodiments of the present application；
Fig. 2 is a kind of choosing that the guarantee point is constructed according to the task point patrol task amount provided by the embodiments of the present application
The flow chart of location Optimized model；
Fig. 3 is a kind of customized D provided by the embodiments of the present applicationcostWith standard DcostCurve comparison figure；
Fig. 4 is a kind of foundation particle swarm algorithm and weighted Voronoi diagrams nomography provided by the embodiments of the present application to the choosing
The flow chart that location Optimized model is solved；
Fig. 5 is provided by the embodiments of the present application a kind of according to the weighted Voronoi diagrams nomography adjustment guarantee point task minute
The flow chart matched.
Specific embodiment
In order to make the above objects, features, and advantages of the present application more apparent, with reference to the accompanying drawing to the application
Specific embodiment be described in detail.Many details are explained in the following description in order to fully understand this Shen
Please.But the application can be implemented with being much different from other way described herein, those skilled in the art can be not
Similar improvement is done in the case where violating the application intension, therefore the application is not limited by following public specific implementation.
It should be noted that it can directly on the other element when element is referred to as " being fixed on " another element
Or there may also be elements placed in the middle.When an element is considered as " connection " another element, it, which can be, is directly connected to
To another element or it may be simultaneously present centering elements.
Unless otherwise defined, all technical and scientific terms used herein and the technical field for belonging to the application
The normally understood meaning of technical staff is identical.The term used in the description of the present application is intended merely to description tool herein
The purpose of the embodiment of body, it is not intended that in limitation the application.Term " and or " used herein includes one or more phases
Any and all combinations of the listed item of pass.
Referring to Figure 1, the application provides a kind of Intelligent Mobile Robot method for allocating tasks.The substation inspection machine
Device people's method for allocating tasks includes: S10, constructs task point set, the task point set includes task point position and task point
Patrol task amount.The guarantee point quantity of crusing robot is arranged in S20.S30 constructs institute according to the task point patrol task amount
State the location optimization model for ensureing point.S40, according to the guarantee point quantity, in conjunction with particle swarm algorithm and weighted Voronoi diagrams graphic calculation
Method solves the location optimization model, and be protected a position and guarantee point task distribution.
In the step S10, sliding-model control is carried out to the mission area of substation first, obtains the task of substation
Discrete region map.The sliding-model control is to be appointed according to the position building of the patrol task amount and examined in determination of examined in determination
Business point set.It is appreciated that task point position corresponds to the position of the examined in determination, the task point patrol task amount pair
Answer the patrol task amount of the examined in determination.In one embodiment, the task point is set to the position of examined in determination, described
Task point patrol task amount is the activity duration of task point described in crusing robot inspection.It is appreciated that the task point inspection
Task amount can be calculated according to the activity duration of the point of task described in crusing robot inspection.
In the step S20, the quantity of the guarantee point of support is provided usually according to substation for crusing robot
Actual conditions and experience set.It is appreciated that the quantity for ensureing point is fixed value, and the substation inspection machine
No longer the quantity for ensureing point is optimized in the subsequent calculating process of device people's method for allocating tasks.In one embodiment
In, it is described setting crusing robot guarantee point quantity the step of include: according to patrol task region area, patrol task amount,
The guarantee point quantity of staffing and crusing robot quantity setting crusing robot.It is appreciated that the guarantee point can
Think the crusing robot supplement or replacement energy source that energy will exhaust, and can be used as crusing robot and open for the first time or repeatedly
The starting point of beginning patrol task.
In the step S30, the location optimization model for ensureing point is constructed according to the task point patrol task amount.
The location optimization model needs to consider each harmony for ensureing point task distribution.It is also required to consider the guarantee simultaneously
The limitation of communication distance between point and crusing robot.Parameter, building are limited by setting task balance parameter and communication distance
The objective function of the particle swarm algorithm.It is appreciated that the task is assigned as each task point distributing to each institute
State guarantee point.
In the step S40, in conjunction with the preset quantity for ensureing point, and the location optimization model is used
It, can be by particle swarm algorithm and weighted Voronoi diagrams nomography to the addressing as the objective function of the particle swarm algorithm
Optimized model is solved.It, can by carrying out continuous iteration optimization to the guarantee point position and guarantee point task distribution
To obtain the optimal solution for ensureing point position and the guarantee point task distribution.In one embodiment, the substation patrols
The output result for examining robot task distribution method may include optimum particle position, Voronoi diagram weight and task point minute
Match.The optimum particle position is the position for ensureing point.The Voronoi diagram weight is the weighted value after Optimized Iterative.
The task point is assigned as each task point and is assigned to each scheme for ensureing point.
The Intelligent Mobile Robot method for allocating tasks combines the location optimization model for ensureing point and presets
The guarantee point quantity, by particle swarm algorithm and weighted Voronoi diagrams nomography to the guarantee point position and the guarantor
Barrier point task distribution optimizes, and optimal location and task allocation plan is obtained, so as to improve Intelligent Mobile Robot
Routing inspection efficiency.The Intelligent Mobile Robot method for allocating tasks can for substation inspection region big, patrol task
More problems makes rational planning for the guarantee point for providing support for crusing robot, and closes for guarantee point distribution
The mission area of reason.In addition, the Intelligent Mobile Robot method for allocating tasks can be by particle swarm algorithm and Voronoi diagram
In conjunction with may be implemented to described to overcome the problem that the distribution of more crusing robot tasks is uneven and routing inspection efficiency is low
Ensure point position and the optimization for ensureing point task distribution.
It is in one embodiment, described to construct the guarantee according to the task point patrol task amount please also refer to Fig. 2
The step of location optimization model of point includes: S310, calculates the different inspection machines ensured between point patrol task amount for each person
The normalized value of maximum difference is as task balance parameter Qcost.S320 calculates the communication distance of crusing robot to the choosing
The affecting parameters of location Optimized model limit parameter D as communication distancecost.S330, according to the task balance parameter QcostAnd institute
State communication distance limitation parameter DcostConstruct the location optimization model minZ=α Qcost+(1-α)Dcost。
In the step S310, the task balance parameter are as follows:
QcostFor crusing robot patrol task amount maximum difference single in the same solution space normalization as a result, described
Normalize bearing reaction is the disequilibrium of different crusing robot task amounts.It is appreciated that when all crusing robots are appointed
Routing inspection efficiency can be substantially improved when equal in business amount.Wherein, kijThe task point for indicating that number is j is mentioned by numbering the guarantee point for being i
It is supported for inspection.TjFor the patrol task amount at task point j.WiThe inspection machine number distributed for the guarantee point numbered by i
Amount.QstdFor the specified patrol task amount of single crusing robot, can indicate are as follows:
In the step S320, communication distance limits parameter DcostThe communication distance of crusing robot has been reacted to described
The influence of location optimization model solution space.In one embodiment, the communication distance limits parameter DcostAre as follows:
Wherein, DcommFor the practical maximum communication distance of crusing robot, D be crusing robot specified maximum communication away from
From.Draw DcostCurve, the curve shows: when the maximum communication distance of solution space meets constraint condition, DcostVariation
Target function value is influenced smaller.When maximum communication distance is unsatisfactory for constraint condition, DcostInfluence to target function value is anxious
Increase severely big.Meanwhile please also refer to Fig. 3, standard D is drawncostCurve compares, standard DcostCurve can indicate are as follows:
The processing of maximum communication distance can be provided for the evolution direction of population in particle swarm algorithm in the application and drawn
It leads, and remains to the planning for ensureing point and the distribution of task point when meeting constraint condition to reducing maximum communication distance
Direction iteration updates, to be further ensured that the superiority for ensureing point position and the guarantee point task allocation result.
In the step S330, the location optimization model of building can consider task balance and survey monitor simultaneously
The communication distance of device people limits.The location optimization model can be converted into an optimization problem.The optimization problem
Objective function is minZ.It is appreciated that zed without specific meanings.α indicates the adjusting weight of task balance parameter, takes
Being worth range is (0,1).The optimization aim considered emphatically different in the objective function is indicated using different α values.At one
In embodiment, α can be 0.8, which can empirically be set.
It is in one embodiment, described according to the guarantee point quantity please also refer to Fig. 4, in conjunction with particle swarm algorithm and
Weighted Voronoi diagrams nomography solves the location optimization model, and be protected a position and guarantee point task distribution
Step, comprising: S410, initialization population, and using the particle position in the population as the guarantee point position.S420, according to
The guarantee point task distribution is adjusted according to weighted Voronoi diagrams nomography.S430 calculates each institute according to the location optimization model
The adaptive value for ensureing point is stated, and records the globally optimal solution of each history optimal solution for ensureing point and the population respectively.
S440 updates each guarantee point position and speed, and returns and adjust the guarantee point times according to weighted Voronoi diagrams nomography
The step of business distribution, until meeting stopping criterion for iteration and exporting the guarantee point position and guarantee point task distribution.?
In one embodiment, the stopping criterion for iteration are as follows: current iteration number k and default maximum number of iterations kmaxIt is equal.
In the step S410, initialization of population is carried out to particle swarm algorithm.The initialization population includes setting kind
Group's scale, the population scale are the guarantee point quantity of crusing robot.It, can be with it is appreciated that in initialization of population
The maximum number of iterations k of particle swarm algorithm is setmax, and whether current iteration number k is reached into maximum number of iterations kmaxAs
Stopping criterion for iteration.The initialization population further includes that the form of objective function solution is arranged, and the form of the solution is that algorithm is final
The form of solving result.In one embodiment, the objective function is the location optimization model.The form of the solution can be with
It is distributed for the guarantee point position and the guarantee point task.It is appreciated that the guarantee point position is that crusing robot rises
Initial point position, the objective function are that the fitness of particle swarm algorithm calculates function.
In the step S420 into the step S430, it will be understood that according to weighted Voronoi diagrams nomography according to initial
The adjustable scheme for ensureing point task distribution of weight.In one embodiment, the initial power of weighted Voronoi diagrams nomography
It can be set to 1 again.By the iterative process of particle swarm algorithm, weighted Voronoi diagrams nomography can be carried out in the guarantee point
Adaptive weighting adjusting is carried out when task is distributed.In one embodiment, in iterative process each time, weighted Voronoi diagrams figure
Algorithm can complete an adaptive weighting adjustment process.By the successive ignition of particle swarm algorithm, can make to solve continuous approach
Optimal solution, to obtain preferred plan to be solved.It is appreciated that according to the addressing in the iterative process of particle swarm algorithm
The each adaptive value for ensureing point of seismic responses calculated, each adaptive value for ensureing point are the target letter of particle swarm algorithm
Numerical value.Meanwhile it can recorde the globally optimal solution of each history optimal solution for ensureing point and the population.
In the step S440, it will be understood that can be according to iteration end in each iterative process of particle swarm algorithm
Only condition judges whether to terminate the iterative process of particle swarm algorithm.In one embodiment, the iterated conditional can be judgement
Whether k=k is metmax.If satisfied, then exporting calculated result.Otherwise it updates each guarantee point position and speed and repeats to hold
The step of row adjusts the task point allocation plan for ensureing point according to weighted Voronoi diagrams nomography, until meeting iteration ends
Condition simultaneously exports the optimal location for ensureing point, Voronoi diagram weight and the task allocation plan for ensureing point.
Please also refer to Fig. 5, in one embodiment, the guarantee point task is adjusted according to weighted Voronoi diagrams nomography
The step of distribution, comprising: S421 distributes task point to the guarantee point according to the task point method of salary distribution.S422, according to described in
Task point patrol task amount and the maximum communication distance ensured between point and the task point distributed are to the guarantor
Barrier point task distribution carries out adaptive weighting adjusting.In one embodiment, the task point method of salary distribution are as follows:
Wherein, x is any task point, pi、pjRespectively number be i, j guarantee point, λi、λjRespectively ensure point pi、pj
Weight, d (x, pi)、d(x,pj) it is respectively any task point and guarantee point pi、pjBetween Euclidean distance.
In one embodiment, the adjustment criteria that the adaptive weighting is adjusted are as follows: whenWhen, the guarantor
The weight for hindering point reduces according to Adaptive Adjustment of Step Length.WhenAndWhen, the guarantee point
Weight increases according to Adaptive Adjustment of Step Length.WhenAndWhen, the guarantee point position cannot
Meet inspection requirement, update each guarantee point position and speed, and returns according to described in the adjustment of weighted Voronoi diagrams nomography
The step of ensureing point task distribution.Wherein, QiFor the task amount that the guarantee point numbered by i distributes, QstdFor single inspection machine
The specified patrol task amount of people, diTo number the maximum communication distance between the guarantee point and distribution task point by i, D is to patrol
The specified maximum communication distance of robot is examined, t-1 is the number of iterations.In one embodiment, the range of Adaptive Adjustment of Step Length
It is 0.05~0.15.
It is appreciated that the adjustment criteria of the adaptive weighting can according to the patrol task amount in region, maximum communication away from
It is adjusted from constraint, distributes each mission area for ensureing point to rationalize.It is appreciated that each guarantor of distribution
The mission area of barrier point is the concrete condition of the task point for ensureing point distribution.The adjustment criteria of the adaptive weighting
It can be with visual representations such as table 1.It is appreciated that Adaptive Adjustment of Step Length is Δ, value range is 0.05~0.15.When weight increases
Added-time executesWhen weight reduces, executeWhenWithIt sets up simultaneously, then shows that current addressing scheme is not able to satisfy inspection requirement, the adjusting of adaptive weighting
Journey terminates.It is appreciated that working asWithIt sets up, can be distributed again for the guarantee point simultaneously
Task point.
1 adjustment criteria of table
In one embodiment, the update mode for ensureing point position and speed are as follows:
ω=ωmax-0.1*(ωmax-ωmin)*exp(k/kmax)
Wherein,To number the speed for ensureing point kth time iteration for being i,Guarantee point kth+1 time for being i for number
The speed of iteration, ω are inertial factor, and r (1) and r (2) are the random number between [0,1], c1、c2For Studying factors,WithThe history optimal solution of the guarantee point and the population that are i is respectively numbered,For the guarantor of kth time iteration
The set of barrier point position,For the set of the guarantee point position of+1 iteration of kth.
It is appreciated that ω is inertial factor, then ωmaxAnd ωminRespectively inertia weight maximum value and minimum value.At one
In embodiment, ωmax=0.8, ωmin=0.4.The Studying factors are the Studying factors in standard particle group algorithm.XiFor kind
Population C=[the X that group's scale is N1,X2,...,XN] in an individual, indicate position coordinates of the particle in solution space, represent
One d of required problem ties up solution, is represented by Xi=[xi1,xi2,...,xid].Each particle is by solving the suitable of objective function
Angle value is answered, obtains the history optimal solution and the population optimal solution for ensureing point, and according to the guarantee point current time
The update to position and speed may be implemented in position, speed and history optimal solution and the population history optimal solution.By
Weight is adjusted in iterative process may be implemented the balance between global search and local search.
The application provides a kind of computer equipment.The computer equipment includes memory and processor, the memory
On be stored with the computer program that can be run on a processor, the processor realizes the change when executing the computer program
The step of power station crusing robot method for allocating tasks.The application provides a kind of computer readable storage medium, is stored thereon with
Computer program, the computer program realize the Intelligent Mobile Robot method for allocating tasks when being executed by processor
Step.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality
It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, all should be considered as described in this specification.
The several embodiments of the application above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
The limitation to claim therefore cannot be interpreted as.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the concept of this application, various modifications and improvements can be made, these belong to the protection of the application
Range.Therefore, the scope of protection shall be subject to the appended claims for the application patent.
Claims (14)
1. a kind of Intelligent Mobile Robot method for allocating tasks characterized by comprising
Building task point set, the task point set include task point position and task point patrol task amount；
The guarantee point quantity of crusing robot is set；
The location optimization model for ensureing point is constructed according to the task point patrol task amount；
According to the guarantee point quantity, in conjunction with particle swarm algorithm and weighted Voronoi diagrams nomography to the location optimization model into
Row solves, and be protected a position and guarantee point task distribution.
2. Intelligent Mobile Robot method for allocating tasks according to claim 1, which is characterized in that described in the foundation
Task point patrol task amount construct it is described ensure point location optimization model the step of include:
It is equal as task to calculate the different normalized values for ensureing the inspection machine patrol task amount maximum difference for each person between point
Weigh parameter Qcost；
The communication distance for calculating crusing robot limits parameter as communication distance to the affecting parameters of the location optimization model
Dcost；
According to the task balance parameter QcostParameter D is limited with the communication distancecostConstruct the location optimization model minZ
=α Qcost+(1-α)Dcost, α is weight.
3. Intelligent Mobile Robot method for allocating tasks according to claim 2, which is characterized in that the communication distance
Limit parameter DcostAre as follows:
Wherein, DcommFor the practical maximum communication distance of crusing robot, D is the specified maximum communication distance of crusing robot.
4. Intelligent Mobile Robot method for allocating tasks according to claim 1, which is characterized in that described in the foundation
It ensures point quantity, the location optimization model is solved in conjunction with particle swarm algorithm and weighted Voronoi diagrams nomography, is protected
The step of barrier point position and guarantee point task distribution, comprising:
Initialization population, and using the particle position in the population as the guarantee point position；
The guarantee point task distribution is adjusted according to weighted Voronoi diagrams nomography；
Each adaptive value for ensureing point is calculated according to the location optimization model, and records each guarantee point respectively
The globally optimal solution of history optimal solution and the population；
Each guarantee point position and speed is updated, and returns and adjusts the guarantee point times according to weighted Voronoi diagrams nomography
The step of business distribution, until meeting stopping criterion for iteration and exporting the guarantee point position and guarantee point task distribution.
5. Intelligent Mobile Robot method for allocating tasks according to claim 4, which is characterized in that according to weighting
Voronoi diagram algorithm adjusts the step of guarantee point task distribution, comprising:
Task point is distributed to the guarantee point according to the task point method of salary distribution；
According to the task point patrol task amount and the maximum communication ensured between point and the task point distributed
Distance distributes the guarantee point task and carries out adaptive weighting adjusting.
6. Intelligent Mobile Robot method for allocating tasks according to claim 5, which is characterized in that the adaptive power
Reset the adjustment criteria of section are as follows:
WhenWhen, the weight for ensureing point reduces according to Adaptive Adjustment of Step Length；
WhenAndWhen, the weight for ensureing point increases according to Adaptive Adjustment of Step Length；
WhenAndWhen, the guarantee point position cannot meet inspection requirement, update each described
It ensures point position and speed, and returns to the step of adjusting guarantee point task distribution according to weighted Voronoi diagrams nomography；
Wherein, QiFor the task amount that the guarantee point numbered by i distributes, QstdFor the specified patrol task of single crusing robot
Amount, diBe number by i guarantee point distribution task point between maximum communication distance, D for crusing robot it is specified most
Big communication distance, t-1 are the number of iterations.
7. Intelligent Mobile Robot method for allocating tasks according to claim 6, which is characterized in that the adaptive tune
The range for saving step-length is 0.05~0.15.
8. Intelligent Mobile Robot method for allocating tasks according to claim 5, which is characterized in that the task point minute
With mode are as follows:
Wherein, x is any task point, pi、pjRespectively number be i, j guarantee point, λi、λjRespectively ensure point pi、pjPower
Weight, d (x, pi)、d(x,pj) it is respectively any task point and guarantee point pi、pjBetween Euclidean distance.
9. Intelligent Mobile Robot method for allocating tasks according to claim 4, which is characterized in that the iteration ends
Condition are as follows: current iteration number k and default maximum number of iterations kmaxIt is equal.
10. Intelligent Mobile Robot method for allocating tasks according to claim 4, which is characterized in that the guarantee point
The update mode of position and speed are as follows:
ω=ωmax-0.1*(ωmax-ωmin)*exp(k/kmax)
Wherein,To number the speed for ensureing point kth time iteration for being i,Guarantee point+1 iteration of kth for being i for number
Speed, ω is inertial factor, and r (1) and r (2) are the random number between [0,1], c1、c2For Studying factors,WithThe history optimal solution of the guarantee point and the population that are i is respectively numbered,For the guarantee point of kth time iteration
The set set,For the set of the guarantee point position of+1 iteration of kth.
11. Intelligent Mobile Robot method for allocating tasks according to claim 1, which is characterized in that the task point
Position is the position of examined in determination, when the task point patrol task amount is the operation of task point described in crusing robot inspection
Between.
12. Intelligent Mobile Robot method for allocating tasks according to claim 1, which is characterized in that the setting is patrolled
The step of examining the guarantee point quantity of robot, comprising:
According to patrol task region area, patrol task amount, staffing and crusing robot quantity setting crusing robot
The guarantee point quantity.
13. a kind of computer equipment, including memory and processor, be stored on the memory to run on a processor
Computer program, which is characterized in that the processor realizes any one of claims 1 to 12 when executing the computer program
The step of Intelligent Mobile Robot method for allocating tasks.
14. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of Intelligent Mobile Robot method for allocating tasks described in any one of claims 1 to 12 is realized when being executed by processor
Suddenly.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910198217.0A CN109872010B (en) | 2019-03-15 | 2019-03-15 | Task allocation method for inspection robot of transformer substation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910198217.0A CN109872010B (en) | 2019-03-15 | 2019-03-15 | Task allocation method for inspection robot of transformer substation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109872010A true CN109872010A (en) | 2019-06-11 |
| CN109872010B CN109872010B (en) | 2021-05-25 |
Family
ID=66920622
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910198217.0A Active CN109872010B (en) | 2019-03-15 | 2019-03-15 | Task allocation method for inspection robot of transformer substation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109872010B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115952942A (en) * | 2023-03-13 | 2023-04-11 | 季华实验室 | Multi-target inspection scheduling planning method and device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880921A (en) * | 2012-10-16 | 2013-01-16 | 山东电力集团公司电力科学研究院 | Method for optimizing site selection of electric vehicle charging stations |
| CN104680427A (en) * | 2015-03-06 | 2015-06-03 | 国家电网公司 | Planning system for comprehensive optimization of regional power distribution network |
| CN105389633A (en) * | 2015-12-01 | 2016-03-09 | 上海电力学院 | Optimization planning method of substation considering distributed power supplies |
| CN107886186A (en) * | 2017-10-16 | 2018-04-06 | 清华大学 | A kind of charging pile method to set up based on travelling data and Wei Nuotu zonings |
-
2019
- 2019-03-15 CN CN201910198217.0A patent/CN109872010B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880921A (en) * | 2012-10-16 | 2013-01-16 | 山东电力集团公司电力科学研究院 | Method for optimizing site selection of electric vehicle charging stations |
| CN104680427A (en) * | 2015-03-06 | 2015-06-03 | 国家电网公司 | Planning system for comprehensive optimization of regional power distribution network |
| CN105389633A (en) * | 2015-12-01 | 2016-03-09 | 上海电力学院 | Optimization planning method of substation considering distributed power supplies |
| CN107886186A (en) * | 2017-10-16 | 2018-04-06 | 清华大学 | A kind of charging pile method to set up based on travelling data and Wei Nuotu zonings |
Non-Patent Citations (5)
| Title |
|---|
| NOR AZLINA BT. AB AZIZ等: "A Wireless Sensor Network Coverage Optimization Algorithm Based on Particle Swarm Optimization and Voronoi Diagram", 《2009 INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL》 * |
| 彭向阳 等: "变电站机器人智能巡检***设计及应用", 《中国电力》 * |
| 董彦君 等: "基于加权Voronoi图和自适应PSO算法的电动汽车充换电站联合规划", 《电源学报》 * |
| 路志英 等: "基于粒子群优化的加权伏罗诺伊图变电站规划", 《中国电机工程学报》 * |
| 魏秀岭 等: "基于粒子群算法加权的Voronoi 图电动汽车充网络优化规划", 《数字技术与应用》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115952942A (en) * | 2023-03-13 | 2023-04-11 | 季华实验室 | Multi-target inspection scheduling planning method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109872010B (en) | 2021-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107944594B (en) | Short-term load prediction method based on spearman grade and RKELM microgrid | |
| US9843190B2 (en) | Power system management device and method | |
| Bacanin et al. | RFID network planning by ABC algorithm hybridized with heuristic for initial number and locations of readers | |
| CN107609583A (en) | Parameter optimization method, device, computer equipment and the storage medium of disaggregated model | |
| CN109412202A (en) | Distributed generation resource control method for coordinating and system | |
| CN109086900A (en) | Power Material guarantee and deployment platform based on multi-objective particle | |
| Sun et al. | Escaping local optima in a class of multi-agent distributed optimization problems: A boosting function approach | |
| CN112784493A (en) | Geographic space prediction method and system based on self-adaptive deep Q network | |
| CN109872010A (en) | Intelligent Mobile Robot method for allocating tasks | |
| Mayer et al. | Cooperative and hierarchical fuzzy MPC for building heating control | |
| Kosmatopoulos et al. | Local4Global adaptive optimization and control for system-of-systems | |
| CN113852080A (en) | Method, system, equipment and storage medium for optimizing power flow of smart power grid | |
| CN110994643B (en) | Control parameter adjusting method and device for speed regulator of hydroelectric generating set | |
| Li et al. | Research on task allocation in multiple logistics robots based on an improved ant colony algorithm | |
| CN116974185A (en) | Multi-agent binary consistency control method, device, equipment and storage medium | |
| Hashim et al. | Greedy spiral dynamic algorithm with application to controller design | |
| CN108181503A (en) | Electric power metering device O＆M plan acquisition methods, device, storage medium and equipment | |
| Lehnhoff | Towards fully decentralized multi-objective energy scheduling | |
| CN112925793A (en) | Distributed mixed storage method and system for multiple structural data | |
| Fagan et al. | Dynamic multi-agent reinforcement learning for control optimization | |
| Feng et al. | Adaptive particle swarm optimization based on quantum oscillator model | |
| Yetkin et al. | Use of the particle swarm optimization algorithm for second order design of levelling networks | |
| Katipoğlu et al. | Design of Optimal FOPI Controller for Two-Area Time-Delayed Load Frequency Control System with Demand Response | |
| CN112598312B (en) | Electric vehicle charging scheduling method and device based on longicorn stigma search algorithm | |
| Benmiloud et al. | Hybrid method for determination of power systems dynamic equivalents based on measurements |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |