CN110488849A - A kind of person's of surrounding and seize decision-making technique based on improvement auction algorithm - Google Patents
A kind of person's of surrounding and seize decision-making technique based on improvement auction algorithm Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
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Abstract
The invention belongs to multi-robotic tasks to distribute field, and in particular to a kind of based on the person's of the surrounding and seize decision-making technique for improving auction algorithm.Deficiency existing for defect and competitive bidding value of the present invention for auctioneer's identity in traditional auction algorithm causes the selected process safety difference for the person of surrounding and seize and the low problem of the efficiency of decision-making to propose a kind of person's of surrounding and seize decision-making technique based on improvement auction algorithm, as the decision-making technique for the person of surrounding and seize, for selecting the person of surrounding and seize.The present invention changes tradition and determines the mode of auctioneer's identity, and then accelerate auction process, improve the efficiency of decision-making, enhance the safety of decision process by introducing geometric center principle；By improving competitive bidding value function, to determine the optimal person's of surrounding and seize identity, improves and whole surround and seize efficiency.
Description
Technical field
The invention belongs to multi-robotic tasks to distribute field, and in particular to a kind of to be determined based on the person of surrounding and seize for improving auction algorithm
Plan method.
Background technique
With the development of naval technology, investment and research to ocean are increasingly deepened.Because underwater robot has various aspects
Advantage, so being becoming increasingly popular in ocean engineering.Underwater robot can realize the scientific investigation exploration at deep-sea, salvage, number
According to acquisition, follow-up study of the scientific research personnel to abyssopelagic organism is assisted, and is effectively surrounded and seize.However single underwater autonomous robot has
Self-ability limitation, especially complex sea area execute complex task when, such as the working time limitation, explore range limit
System.For more efficient execution task, multiple robots can be formed to a formation cooperation execution task；It can thus advise
The deficiency of individual integration capability is kept away, the benefit of group collaboration is sufficiently showed.
Auction algorithm based on market mechanism is the classic algorithm of task distribution, often has a center intelligent body full powers negative
The task distribution of whole system is blamed, there are following three points deficiencies:
(1) in the selection of underwater robot auctioneer, it then follows the principle of " whose discovery target, who is auctioneer ".This band
The hidden danger come is huge, because the underwater robot for being generally found target is also that nearest of distance objective, this principle
Applicable basis is that target does not have attacking ability.Once target, which has both, scouts striking capabilities, target sends out finder
Attack is played, will upset or even destroy and entirely surround and seize the assigning process of task, and then the person of surrounding and seize can not be selected.
(2) from the viewpoint of communication efficiency, auctioneer issues target information, receives competitive bidding information, issues competitive bidding knot
Fruit have passed through communication process three times.In this three times communication process, auctioneer is communication center node.The finder of target and
It is no positive connection between optimal communication center node, even finder is frequently not optimal message center.So,
The finder of target is defaulted as communication center node, Lai Jinhang communications are unknown from the viewpoint of communication efficiency
Intelligence.
(3) by traditional auction process it is found that auctioneer be according to the scale value of all suitors from big to small, select and surround and seize water
Lower robot.The scale value of so competitive bidding has any determination, is made of which factor, is most important.Improving these factors will
It has an immense impact on to the efficiency of auction algorithm.
How the person of surrounding and seize is selected, determine optimal communication central node, determine the competitive bidding scale value strategy of robot, to faster more
It is significant that multi-robotic task distribution is completed well.
Summary of the invention
It is an object of the invention to deficiencies existing for the defect and competitive bidding value for auctioneer's identity in traditional auction algorithm
Cause the selected process safety difference for the person of surrounding and seize and the low problem of the efficiency of decision-making to provide and be used to the selected person of surrounding and seize, as the person's of surrounding and seize
A kind of person's of surrounding and seize decision-making technique based on improvement auction algorithm of decision.
The purpose of the present invention is realized by following technical solution, comprising the following steps:
Step 1: after underwater robot formation searches for and finds target, target detection person calculates flight pattern geometric center；
Step 2: target detection person calculates the distance between all underwater robot members and geometric center in formation, and selects
Select with geometric center apart from nearest member be auctioneer；
Step 3: the auctioneer's identity information determined after target position, movement speed and calculating is packaged by target detection person
It is sent to other all members of underwater robot formation；
Step 4: auctioneer starts to auction, and continuous time period T, competitive bidding underwater robot calculates separately the competitive bidding of oneself
Value, is then sent respectively to auctioneer for respective competitive bidding value；After the time being more than period of time T, auction marketplace is closed；
Step 5: auctioneer is according to the competitive bidding value received and surrounds and seize mission requirements, by the competitive bidding of each competitive bidding underwater robot
It is worth and surrounds and seize compared with mission requirements, meets leaving as the standard person of surrounding and seize for condition；From big to small by the competitive bidding value of the quasi- person of surrounding and seize
Arrangement, screening n before determining competitive bidding value is greatly to surround and seize underwater robot；N is the minimum machine number that setting meets task of surrounding and seize；
Step 6: if the suitor's quantity for meeting condition is less than n, return step 4, auctioneer opens the bat of a new round
Market is sold, competitive bidding value is recalculated；If qualified suitor's quantity is more than or equal to n, the person's of surrounding and seize decision terminates；
Step 7: entering and surround and seize state, if it exceeds surrounding and seize successfully time threshold does not surround and seize success also, then abandon surrounding and seize；It encloses
Underwater robot formation returns to cruise mode after catching successfully.
The present invention may also include:
The method of flight pattern geometric center is calculated in the step 1 are as follows: according to underwater robot in actual environment
Longitude and latitude determines plane coordinates, determines longitudinal coordinate according to the reference depth of water, each underwater robot member coordinate is denoted as in formation
(xi,yi,zi), then the calculation formula of flight pattern geometric center are as follows:
Wherein m is the quantity of underwater robot member in forming into columns, pointFor flight pattern geometric center；Underwater machine in formation
Device people PiThe calculation formula of distance between geometric center are as follows:
The calculation method of the competitive bidding value of competitive bidding underwater robot in the step 4 are as follows:
Step 4.1: calculating ability g of the competitive bidding underwater robot relative to target；
G (v, l)=λ3v+μ3l
Wherein v is movement speed odds ratio of the competitive bidding underwater robot relative to target,v1For the underwater machine of competitive bidding
The speed of device people, v2For the speed of target；L is spacing of the competitive bidding underwater robot relative to target；λ3And μ3For adjustment factor,
And λ3+μ3=1；
Step 4.2: calculating competitive bidding underwater robot current state p；
P=λ2E+μ2σ
Wherein E is the energy residual of competitive bidding underwater robot, and σ is the attacking ability of competitive bidding underwater robot；λ2And μ2To adjust
Save coefficient, and λ2+μ2=1；
Step 4.3: calculating the cost c of competitive bidding underwater robot；
C (E, v, l)=ρ1E+ρ2v+ρ3l；
Wherein ρ1、ρ2And ρ3The respectively weight of E, v and l in cost function, and value range is between section (0,1)
Real number；
Step 4.4: calculating the theoretical income τ of competitive bidding underwater robot；
τ=ε-c (E, v, l)；
Wherein ε is competitive bidding underwater robot gained reward value after successfully completing the task of surrounding and seize；
Step 4.5: calculating the ability value f of competitive bidding underwater robot；
F (τ, p)=λ1τ+μ1p；
Wherein λ1And μ1For adjustment factor, and λ1+μ1=1；
Step 4.6: calculating the competitive bidding value b of competitive bidding underwater roboti；
bi=α f (τ, p)+β g (v, l)
Wherein α and β is coefficient, and alpha+beta=1.
The beneficial effects of the present invention are:
Deficiency existing for defect and competitive bidding value of the present invention for auctioneer's identity in traditional auction algorithm leads to the person of surrounding and seize
Selected process safety difference and the low problem of the efficiency of decision-making propose it is a kind of based on the person's of the surrounding and seize decision-making party for improving auction algorithm
Method, as the decision-making technique for the person of surrounding and seize, for selecting the person of surrounding and seize.The present invention changes tradition by introducing geometric center principle
It determines the mode of auctioneer's identity, and then accelerates auction process, improve the efficiency of decision-making, enhance the safety of decision process
Property；By improving competitive bidding value function, to determine the optimal person's of surrounding and seize identity, improves and whole surround and seize efficiency.
Detailed description of the invention
Fig. 1 is the flow chart that auctioneer is determined in the present invention.
Fig. 2 is the flow chart that process is surrounded and seize in the present invention.
Specific embodiment
The present invention is described further with reference to the accompanying drawing.
In the decision-making technique for the person of surrounding and seize, the traditional auction algorithm based on market mechanism is favored deeply.The present invention is for biography
It unites in auction algorithm, deficiency existing for the defect and competitive bidding value of auctioneer's identity causes the selected process safety for the person of surrounding and seize poor
The problem low with the efficiency of decision-making proposes improvement auction algorithm, as the decision-making technique for the person of surrounding and seize, for selecting the person of surrounding and seize.Change
Auction algorithm after changes tradition and determines the mode of auctioneer's identity, and then accelerate by introducing geometric center principle
Auction process improves the efficiency of decision-making, enhances the safety of decision process；It is best to determine by improving competitive bidding value function
The person's of surrounding and seize identity, improve and whole surround and seize efficiency.
Deficiency existing for defect and competitive bidding value of the present invention for auctioneer's identity in traditional auction algorithm leads to the person of surrounding and seize
Selected process safety difference and the low problem of the efficiency of decision-making propose improvement auction algorithm, as the decision-making technique for the person of surrounding and seize,
For selecting the person of surrounding and seize.
Underwater robot formation member quantity is denoted as m, and each member's coordinate is denoted as (xi, yi, zi), in actual ocean ring
In border, plane coordinates can refer to longitude and latitude, longitudinally can refer to the depth of water.
After the formation of step 001. underwater robot searches for and finds target, target detection person is calculated in flight pattern geometry
The heart.
Step 001 specifically includes:
Step 00101. initialization data.Underwater robot formation member quantity is denoted as m, and each member's coordinate is denoted as (xi,
yi, zi), in actual marine environment, plane coordinates can refer to longitude and latitude, longitudinally can refer to the depth of water.
Each member's coordinate is (x in step 00102. formationi, yi, zi)
Step 00103. calculates flight pattern geometric center after finding targetNamelyM is formation robot
Quantity:
Step 002. target detection person calculates the nearest underwater member of geometric distance centre coordinate, and determines the member
For auctioneer.
Step 002 specifically includes:
Underwater robot and geometric center minimum range in step 00201. formationFor
Step 00202. determines the artificial auctioneer of underwater with minimum range.
Step 003. target detection person is by the coordinate position of target, movement speed, and the auctioneer's body determined after calculating
Part information, auctioneer coordinate transmit to other all members of underwater robot team formation.
Step 004. auctioneer starts to auction, continuous time period T.Competitive bidding underwater robot calculates separately the competitive bidding of oneself
Value, is then sent respectively to auctioneer for respective competitive bidding value.
Step 004 specifically includes:
Step 00401. competitor starts to calculate competitive bidding value as follows after receiving data；
The ability g of step 00402. calculating competitive bidding robot relative target；
G (v, l)=λ3v+μ3l
V is movement speed odds ratio of the competitive bidding underwater robot relative to target,v1For competitive bidding underwater robot
Speed, v2For the speed of target；L is spacing of the competitive bidding underwater robot relative to target；λ3And μ3For adjustment factor, and λ3+
μ3=1；
Step 00403. calculates competitive bidding robot current state p；
P=λ2E+μ2σ
Wherein E is the energy residual of competitive bidding underwater robot, and σ is the attacking ability of competitive bidding underwater robot；λ2And μ2To adjust
Save coefficient, and λ2+μ2=1；
The cost c of step 00404. calculating competitive bidding robot；
C (E, v, l)=ρ1E+ρ2v+ρ3l
ρ1、ρ2And ρ3The respectively weight of E, v and l in cost function, and reality of the value range between section (0,1)
Number；
The theoretical income τ of step 00405. calculating competitive bidding robot；
τ=ε-c (E, v, l)
ε is competitive bidding underwater robot gained reward value after successfully completing the task of surrounding and seize；
Step 00406. calculates competitive bidding robot capability value f；
F (τ, p)=λ1τ+μ1p
λ1And μ1For adjustment factor, and λ1+μ1=1；
Step 00407. calculates competitive bidding robot competitive bidding value bi；
bi=α f (τ, p)+β g (v, l)
α and β is coefficient, and alpha+beta=1；
Competitive bidding value is sent to auctioneer by each competitive bidding robot of step 00408.；
Step 00409. closes auction marketplace after the time being more than period of time T.
Step 005. auctioneer is according to the competitive bidding value received and surrounds and seize mission requirements, to the competitive bidding value received by from big to small
Sequence to be ranked up n before screening determines competitive bidding value be greatly to surround and seize underwater robot, n is that satisfaction surrounds and seize the minimum machine of task
Number.
Step 005 specifically includes:
Step 00501. compares the competitive bidding value of each suitor with mission requirements are surrounded and seize, and meeting leaving for condition becomes
The standard person of surrounding and seize.
Step 00502. arranges the competitive bidding value of the quasi- person of surrounding and seize from big to small, n before leaving, less than n with regard to all leaving.
If suitor's quantity of step 006. step 00601. qualification is less than n, return step 004 opens new one
Competitive bidding value is recalculated in the auction marketplace of wheel.If qualified suitor's quantity is more than or equal to n, the person's of surrounding and seize decision terminates.
Step 007. enters state of surrounding and seize, and captures target.Success is successfully surrounded and seize surrounding and seize successfully to surround and seize in time threshold, it is extensive
Multiple cruise mode.It is more than to surround and seize successfully time threshold when the time, abandons surrounding and seize, restores cruising condition.
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.
Claims (3)
1. a kind of based on the person's of the surrounding and seize decision-making technique for improving auction algorithm, which comprises the following steps:
Step 1: after underwater robot formation searches for and finds target, target detection person calculates flight pattern geometric center；
Step 2: target detection person calculate form into columns in the distance between all underwater robot members and geometric center, and select and
Geometric center is auctioneer apart from nearest member；
Step 3: target detection person transmits the auctioneer's identity information determined after target position, movement speed and calculating
To other all members of underwater robot team formation；
Step 4: auctioneer starts to auction, continuous time period T, and competitive bidding underwater robot calculates separately the competitive bidding value of oneself, so
Respective competitive bidding value is sent respectively to auctioneer afterwards；After the time being more than period of time T, auction marketplace is closed；
Step 5: auctioneer is according to the competitive bidding value that receives and surrounds and seize mission requirements, by the competitive bidding value of each competitive bidding underwater robot with
It surrounds and seize mission requirements to compare, meets leaving as the standard person of surrounding and seize for condition；The competitive bidding value of the quasi- person of surrounding and seize is arranged from big to small,
Screening n before determining competitive bidding value is greatly to surround and seize underwater robot；N is the minimum machine number that setting meets task of surrounding and seize；
Step 6: if the suitor's quantity for meeting condition is less than n, return step 4, auctioneer opens the auction city of a new round
, recalculate competitive bidding value；If qualified suitor's quantity is more than or equal to n, the person's of surrounding and seize decision terminates；
Step 7: entering and surround and seize state, if it exceeds surrounding and seize successfully time threshold does not surround and seize success also, then abandon surrounding and seize；It surrounds and seize into
Underwater robot formation returns to cruise mode after function.
2. according to claim 1 a kind of based on the person's of the surrounding and seize decision-making technique for improving auction algorithm, it is characterised in that: described
Step 1 in calculate the method for flight pattern geometric center are as follows: determined according to the longitude and latitude of underwater robot in actual environment flat
Areal coordinate determines longitudinal coordinate according to the reference depth of water, and each underwater robot member coordinate is denoted as (x in formationi,yi,zi), then
The calculation formula of flight pattern geometric center are as follows:
Wherein m is the quantity of underwater robot member in forming into columns, pointFor flight pattern geometric center；Underwater robot in formation
PiThe calculation formula of distance between geometric center are as follows:
3. according to claim 1 or 2 a kind of based on the person's of the surrounding and seize decision-making technique for improving auction algorithm, it is characterised in that:
The calculation method of the competitive bidding value of competitive bidding underwater robot in the step 4 are as follows:
Step 4.1: calculating ability g of the competitive bidding underwater robot relative to target；
G (v, l)=λ3v+μ3l
Wherein v is movement speed odds ratio of the competitive bidding underwater robot relative to target,v1For competitive bidding underwater robot
Speed, v2For the speed of target；L is spacing of the competitive bidding underwater robot relative to target；λ3And μ3For adjustment factor, and λ3+
μ3=1；
Step 4.2: calculating competitive bidding underwater robot current state p；
P=λ2E+μ2σ
Wherein E is the energy residual of competitive bidding underwater robot, and σ is the attacking ability of competitive bidding underwater robot；λ2And μ2To adjust system
Number, and λ2+μ2=1；
Step 4.3: calculating the cost c of competitive bidding underwater robot；
C (E, v, l)=ρ1E+ρ2v+ρ3l；
Wherein ρ1、ρ2And ρ3The respectively weight of E, v and l in cost function, and reality of the value range between section (0,1)
Number；
Step 4.4: calculating the theoretical income τ of competitive bidding underwater robot；
τ=ε-c (E, v, l)；
Wherein ε is competitive bidding underwater robot gained reward value after successfully completing the task of surrounding and seize；
Step 4.5: calculating the ability value f of competitive bidding underwater robot；
F (τ, p)=λ1τ+μ1p；
Wherein λ1And μ1For adjustment factor, and λ1+μ1=1；
Step 4.6: calculating the competitive bidding value b of competitive bidding underwater roboti；
bi=α f (τ, p)+β g (v, l)
Wherein α and β is coefficient, and alpha+beta=1.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111026137A (en) * | 2019-12-26 | 2020-04-17 | 西北工业大学 | Three-dimensional distributed cooperative guidance method for simultaneously attacking targets under attack angle constraint |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105068550A (en) * | 2015-08-21 | 2015-11-18 | 燕山大学 | Auction mode-based underwater robot multi-target selection strategy |
| CN105843227A (en) * | 2016-04-15 | 2016-08-10 | 上海大学 | Task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm |
| CN107562074A (en) * | 2017-07-24 | 2018-01-09 | 哈尔滨工程大学 | A kind of method for allocating tasks surrounded and seize towards MAUVS |
| US20180326581A1 (en) * | 2017-05-11 | 2018-11-15 | King Fahd University Of Petroleum And Minerals | System and method for auction-based and adaptive multi-threshold multi-agent task allocation |
-
2019
- 2019-08-29 CN CN201910810370.4A patent/CN110488849A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105068550A (en) * | 2015-08-21 | 2015-11-18 | 燕山大学 | Auction mode-based underwater robot multi-target selection strategy |
| CN105843227A (en) * | 2016-04-15 | 2016-08-10 | 上海大学 | Task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm |
| US20180326581A1 (en) * | 2017-05-11 | 2018-11-15 | King Fahd University Of Petroleum And Minerals | System and method for auction-based and adaptive multi-threshold multi-agent task allocation |
| CN107562074A (en) * | 2017-07-24 | 2018-01-09 | 哈尔滨工程大学 | A kind of method for allocating tasks surrounded and seize towards MAUVS |
Non-Patent Citations (1)
| Title |
|---|
| 王杰: "多AUV围捕决策与行为协同控制研究", 《中国优秀博硕士学位论文全文数据库（硕士）信息科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111026137A (en) * | 2019-12-26 | 2020-04-17 | 西北工业大学 | Three-dimensional distributed cooperative guidance method for simultaneously attacking targets under attack angle constraint |
| CN111026137B (en) * | 2019-12-26 | 2022-10-28 | 西北工业大学 | Three-dimensional distributed cooperative guidance method for simultaneously attacking targets under attack angle constraint |
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