CN105068550A - Auction mode-based underwater robot multi-target selection strategy - Google Patents
Auction mode-based underwater robot multi-target selection strategy Download PDFInfo
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- CN105068550A CN105068550A CN201510518011.3A CN201510518011A CN105068550A CN 105068550 A CN105068550 A CN 105068550A CN 201510518011 A CN201510518011 A CN 201510518011A CN 105068550 A CN105068550 A CN 105068550A
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Abstract
The invention discloses an auction mode-based underwater robot multi-target selection strategy, which comprises the following steps: in a given monitoring water area, multiple underwater moving robots with perceptive functions are deployed, and each underwater moving robot determines self position information and target position information via a sensor carried with the underwater moving robot itself; the position information is used for calculating a cost function, a value function and a gain function; a virtual auction public platform is then built, and based on a Dutch decreasing public auction form, auction is carried out on multiple targets; underwater robots which complete selection already exist the multi-target selection platform, the remaining underwater robots which do not complete selection perform selection circularly, and finally, a whole multi-target selection process is completed. The multi-target selection strategy of the invention dynamically and reasonably select corresponding target points according to changes of target positions, and stability of the selection process is effectively improved.
Description
Technical field
The present invention relates to underwater robot field of intelligent control technology, especially a kind of underwater robot multiobjective selection strategy based on auction mechanism.
Background information
Underwater moving target tracing system is intended to Timeliness coverage target, obtains data message to realize the tracking to suspicious moving target, has important practical significance for safeguarding that China's maritime rights and interests and Support Resource are explored.Due to the complicacy of underwater environment and the dynamic of target state, underwater robot likely monitors multiple target at synchronization, and how dynamically choose reasonable follows the trail of the objective, and becomes the key building underwater multi-target tracing system.
Retrieve from prior art and find, Chinese Patent Application No. is 201310655173.2, and name is called: underwater multi-target method for tracing, and a kind of improvement of the method employing resampling is non-is augmented several tasteless particle filter algorithm to realize underwater multi-target tracking.But said method is when designing pursive strategy, not for the status information design multiobjective selection strategy of target.Because underwater monitoring region is larger, and underwater robot sports energy consumption is large and the features such as battery difficulty changed by subsea equipment, determine the status information be necessary in conjunction with underwater moving target in tracing process, design a kind of dynamic multi-objective selection strategy, to save underwater robot mass motion energy consumption, and then extend the tracing system life-span.
Further, Chinese Patent Application No. is 201310556313.0, name is called: objective design system of selection and system, and the method utilizes the thought of game theory and threshold decision to devise a kind of multiobjective selection strategy.But above-mentioned multiobjective selection strategy is a kind of centralized algorithm, in order to get the status information of all the other individualities, each individual need and all the other individual real-time Communication for Power." weak communication " characteristics such as underwater sound communication link instability, multi-path jamming and communication delay, make centralized multiobjective selection strategy be difficult to be applied in underwater multi-target tracing system.Therefore, the distributed multiple target selection strategy how designing a kind of applicable underwater environment seems particularly important.
Summary of the invention
The object of the invention is to provide a kind of effective raising selection course stability, the rational underwater robot multiobjective selection strategy based on auction model.
To achieve these goals, the present invention includes following steps:
(1) underwater robot location information and detection of a target positional information is obtained
In given monitoring waters, dispose multiple mobile robot under water with perceptional function, multirobot carries out networking by underwater sound communication mode under water, forms the multi-robot system under water with synergic monitoring ability;
Any underwater robot i can utilize existing location technology, underwater robot i position p
i=(x
i, y
i, z
i)
t,
In formula, x
i, y
i, z
irepresent underwater robot i respectively at X-axis, Y-axis, position coordinates that Z axis is corresponding, symbol " T " represents the transposition of vector;
When target enters monitored area, target position information is determined to obtain by echo principle and triangulation by multiple mobile robot under water;
(2) calculation cost function
When following the tracks of multiple target, form by inch of candle realizes multiobjective selection; Suppose that each target is the commodity having valency for underwater robot, the value setting these commodity is V, for each underwater robot, the cost that its tracking different target point spends is different, and namely underwater robot i estimates and moves to cost function needed for target j is c
ij;
(3) according to cost function c
ij, build the cost function based on auction mechanism;
(4) revenue function is constructed
According to cost function and cost function, build the revenue function r that underwater robot i select target j obtains
ij(t), namely
In formula, V
ijrepresent the getable value of underwater robot i select target j, c
ijrepresent that underwater robot i estimates and move to cost needed for target j;
Underwater robot only selects the target exceeding prospective earnings to follow the trail of, underwater robot try to achieve sensing region internal object institute bear interest after, choose wherein the highest income as quotation, i.e. b
i=max{r
i1, r
i2..., r
in, max{r in formula
i1, r
i2..., r
inrepresent at r
i1, r
i2..., r
inin choose maximal value, b
ifor the maximal value returned;
(5) dynamic multi-objective selects platform
Build a virtual auction common platform, the form of public auction of successively decreasing based on holland type, auctions multiple target, and auction platform is outcry by a certain price; Setting underwater robot is bidder, and all bidders know current outcry, and outcry reduces gradually, until certain bidder responds outcry in certain price; Such as, on " auction platform ", current outcry is less than or equal to b
itime, then this bidder obtains commodity (i.e. point of destination) with paying of current outcry.
(6) underwater robot having completed selection exits multiobjective selection platform, and all the other underwater robots not completing selection repeat step (4), finally complete multiobjective selection task.
In step (2), underwater robot i estimates and moves to cost function c needed for target j
ijdesign as follows: c
ij=|| e
j-p
i||,
Wherein, e
j∈ R
3represent the positional information of target j, R
3represent three dimensions, p
ifor underwater robot i positional information.
In step (3), the method built based on the cost function of auction mechanism is as follows:
In multiobjective selection process, the initial value setting of target is identical, and arbitrary underwater robot select target j, is so set to V by the value of acquisition
j=V/N
j,
Wherein, N
jfor the underwater robot individual amount of synchronization select target j, V is the commodity value that submarine target corresponds to underwater robot, by adding variable N
j, can the dispense value of equalization target, and then reduce the redundance repeating to select.
Between multiple underwater robot during close together, underwater robot is estimated and is moved to target j required cost cost c
ijdiffer less, for avoiding the chaotic problem of underwater robot multiobjective selection, cost function is improved, except initial time, it is different for being set in target value when at every turn selecting, target j was after being selected by underwater robot i, and underwater robot i second time select target j, will be worth V as follows
ij=V+ ε;
In formula, V
ijrepresent underwater robot i select target j getable value, V is the commodity value that submarine target corresponds to underwater robot, ε be greater than zero constant;
By the above-mentioned value adjustment with memory capability, under original income is more or less the same situation, underwater robot i will tend to select the last target j selected.
Compared with prior art, tool of the present invention has the following advantages:
1, compared to centralized static allocation mode, above-mentioned Dynamic Selection mode only needs individual communications in each individuality and its neighborhood, and underwater robot can be made reasonably to select corresponding impact point according to the change tread of target location.
2, cost function and increment are as design factor, there is memory function, the phenomenon of the irregular dynamic hop of underwater robot multiobjective selection process caused due to factors such as underwater environment complicacy and underwater sound communication are unstable can be avoided, effectively improve the stability of selection course.
Accompanying drawing explanation
Fig. 1 is process flow diagram of the present invention.
Fig. 2 is the selection course schematic diagram of embodiment one.
Drawing reference numeral: 1-underwater robot I, 2-underwater robot II, 3-underwater robot III, 4-submarine target I, 5-submarine target II.
Specific embodiments
Below in conjunction with accompanying drawing, the present invention will be further described:
As shown in Figure 1, the present invention includes following steps:
(1) underwater robot location information and detection of a target positional information is obtained
In given monitoring waters, dispose multiple mobile robot under water with perceptional function, multirobot carries out networking by underwater sound communication mode under water, forms the multi-robot system under water with synergic monitoring ability;
Mobile robot determines the positional information of self and target by self-contained sensor under water; Any underwater robot i can utilize existing location technology, gets self-position p
i=(x
i, y
i, z
i)
t,
In formula, x
i, y
i, z
irepresent underwater robot i respectively at X-axis, Y-axis, position coordinates that Z axis is corresponding, symbol " T " represents the transposition of vector;
When target enters monitored area, target position information is determined to obtain by echo principle and triangulation by multiple mobile robot under water;
(2) calculation cost function
When following the tracks of multiple target, form by inch of candle realizes multiobjective selection; Suppose that each target is the commodity having valency for underwater robot, the value setting these commodity is V, for each underwater robot, the cost that its tracking different target point spends is different, and namely underwater robot i estimates and moves to cost function needed for target j is c
ij;
Underwater robot i estimates and moves to cost function c needed for target j
ijfor:
C
ij=|| e
j-p
i||, wherein, e
j∈ R
3represent the positional information of target j, R
3represent three dimensions, p
ifor underwater robot i positional information;
(3) according to cost function c
ij, build the cost function based on auction mechanism;
Build as follows based on the method for the cost function of auction mechanism:
In multiobjective selection process, the initial value setting of target is identical, and arbitrary underwater robot select target j, is so set to V by the value of acquisition
j=V/N
j,
Wherein, N
jfor the underwater robot individual amount of synchronization select target j, V is the commodity value that submarine target corresponds to underwater robot, by adding variable N
j, can the dispense value of equalization target, and then reduce the redundance repeating to select.
Between multiple underwater robot during close together, underwater robot is estimated and is moved to target j required cost cost c
ijdiffer less, due to complicacy and the labile factor such as underwater sound communication multi-path jamming and Doppler propagation of underwater environment, make subaqueous sound ranging process there is certain deviation.The noisiness of underwater environment, makes underwater robot multiobjective selection process present irregular dynamic hop phenomenon, causes the confusion of underwater robot multiobjective selection.For avoiding the chaotic problem of underwater robot multiobjective selection, cost function is improved, except initial time, it is different for being set in target value when at every turn selecting, target j was after being selected by underwater robot i, and underwater robot i second time select target j, will be worth V as follows
ij=V+ ε;
In formula, V
ijrepresent underwater robot i select target j getable value, V is the commodity value that submarine target corresponds to underwater robot, ε be greater than zero constant;
By the above-mentioned value adjustment with memory capability, under original income is more or less the same situation, underwater robot i will tend to select the last target j selected, and then ensure that the stationarity of multiobjective selection process under noise situations.
(4) revenue function is constructed
According to cost function and cost function, build the revenue function r that underwater robot i select target j obtains
ij(t), namely
In formula, V
ijrepresent the getable value of underwater robot i select target j, c
ijrepresent that underwater robot i estimates and move to cost needed for target j;
Can find out, above-mentioned revenue function is more than or equal to zero, and namely underwater robot can not do " losing proposition ", only selects the target exceeding prospective earnings to follow the trail of.Underwater robot try to achieve sensing region internal object institute bear interest after, choose wherein the highest income as quotation, i.e. b
i=max{r
i1, r
i2..., r
in,
In formula, max{r
i1, r
i2..., r
inrepresent at r
i1, r
i2..., r
inin choose maximal value, b
ifor the maximal value returned;
(5) dynamic multi-objective selects platform
Build a virtual auction common platform, the form of public auction of successively decreasing based on holland type, auctions multiple target, and auction platform is outcry by a certain price; Setting underwater robot is bidder, and all bidders know current outcry, and outcry reduces gradually, until certain bidder responds outcry in certain price; Such as, on " auction platform ", current outcry is less than or equal to b
itime, then this bidder obtains commodity (i.e. point of destination) with paying of current outcry.
(6) underwater robot having completed selection exits multiobjective selection platform, and all the other underwater robots not completing selection repeat step (4), finally complete multiobjective selection task.
Embodiment one:
(1) as shown in Figure 2, in given monitoring waters, dispose three under water mobile robot two targets are monitored, underwater robot is underwater robot I1, underwater robot II2, underwater robot III3 respectively, target is respectively submarine target I4, submarine target II5, and underwater robot communicates with its monitored area inner machine people.Mobile robot passes through the positional information of " echo principle " and triangulation determination target under water.
(2) sampling time interval δ >0, in a sampling time, calculates revenue function, and determines selected target successively according to calculated revenue function.After target selection completes, carry out tracking monitor to target direction stepping.
(3) in the next sampling time, system determines the position coordinates of current underwater robot and target, and underwater robot recalculates revenue function, upgrades target selection.
(4) continue to upgrade, until multi-target tracking process terminates.
Above-described embodiment is only be described the preferred embodiment of the present invention; not scope of the present invention is limited; under not departing from the present invention and designing the prerequisite of spirit; the various distortion that those of ordinary skill in the art make technical scheme of the present invention and improvement, all should fall in protection domain that claims of the present invention determines.
Claims (3)
1., based on a underwater robot multiobjective selection strategy for auction model, it is characterized in that, comprise the following steps:
(1) underwater robot location information and detection of a target positional information is obtained
In given monitoring waters, dispose multiple mobile robot under water with perceptional function, multirobot carries out networking by underwater sound communication mode under water, forms the multi-robot system under water with synergic monitoring ability;
Underwater robot i position p
i=(x
i, y
i, z
i)
t,
In formula, x
i, y
i, z
irepresent underwater robot i respectively at X-axis, Y-axis, position coordinates that Z axis is corresponding, symbol T represents the transposition of vector;
When target enters monitored area, target position information is determined to obtain by echo principle and triangulation by multiple mobile robot under water;
(2) calculation cost function
When following the tracks of multiple target, form by inch of candle realizes multiobjective selection; Suppose that each target is the commodity having valency for underwater robot, the value setting these commodity is V, for each underwater robot, the cost that its tracking different target point spends is different, and namely underwater robot i estimates and moves to cost function needed for target j is c
ij;
(3) according to cost function c
ij, build the cost function based on auction mechanism;
(4) revenue function is constructed
According to cost function and cost function, build the revenue function r that underwater robot i select target j obtains
ij, namely
In formula, V
ijrepresent the getable value of underwater robot i select target j, c
ijrepresent that underwater robot i estimates and move to cost needed for target j;
Underwater robot only selects the target exceeding prospective earnings to follow the trail of, underwater robot try to achieve sensing region internal object institute bear interest after, choose wherein the highest income as quotation, i.e. b
i=max{r
i1, r
i2..., r
in,
Max{r in formula
i1, r
i2..., r
inrepresent at r
i1, r
i2..., r
inin choose maximal value, b
ifor the maximal value returned;
(5) dynamic multi-objective selects platform
Build a virtual auction common platform, the form of public auction of successively decreasing based on holland type, auctions multiple target, and auction platform is outcry by a certain price; Setting underwater robot is bidder, and all bidders know current outcry, and outcry reduces gradually, until certain bidder responds outcry in certain price;
(6) underwater robot having completed selection exits multiobjective selection platform, and all the other underwater robots not completing selection repeat step (4), finally complete multiobjective selection task.
2. a kind of underwater robot multiobjective selection strategy based on auction model according to claim 1, is characterized in that, in step (2), underwater robot i estimates and moves to cost function c needed for target j
ijfor: c
ij=|| e
j-p
i||,
Wherein, e
j∈ R
3represent the positional information of target j, R
3represent three dimensions, p
ifor underwater robot i positional information.
3. a kind of underwater robot multiobjective selection strategy based on auction model according to claim 1, it is characterized in that, in step (3), the method built based on the cost function of auction mechanism is as follows:
In multiobjective selection process, the initial value setting of target is identical, and arbitrary underwater robot select target j, is so set to V by the value of acquisition
j=V/N
j,
Wherein, N
jfor the underwater robot individual amount of synchronization select target j, V is the commodity value that submarine target corresponds to underwater robot;
Between multiple underwater robot during close together, underwater robot is estimated and is moved to target j required cost cost c
ijdiffer less, for avoiding the chaotic problem of underwater robot multiobjective selection, cost function is improved, except initial time, it is different for being set in target value when at every turn selecting, target j was after being selected by underwater robot i, and underwater robot i second time select target j, will be worth V as follows
ij=V+ ε,
In formula, V
ijrepresent underwater robot i select target j getable value, V is the commodity value that submarine target corresponds to underwater robot, ε be greater than zero constant;
By the above-mentioned value adjustment with memory capability, under original income is more or less the same situation, underwater robot i will tend to select the last target j selected.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105242275A (en) * | 2015-09-29 | 2016-01-13 | 燕山大学 | Underwater target cooperative tracking method based on consistency estimation and dormancy scheduling |
| CN105843227A (en) * | 2016-04-15 | 2016-08-10 | 上海大学 | Task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101825903A (en) * | 2010-04-29 | 2010-09-08 | 哈尔滨工程大学 | Water surface control method for remotely controlling underwater robot |
| WO2014020596A1 (en) * | 2012-08-02 | 2014-02-06 | Israel Aerospace Industries Ltd. | An unmanned aerial vehicle |
| CN103901893A (en) * | 2014-04-02 | 2014-07-02 | 哈尔滨工程大学 | Water surface control system of autonomous underwater robot |
| CN103970144A (en) * | 2014-03-28 | 2014-08-06 | 哈尔滨工程大学 | Autonomous underwater robot water-surface control system |
| US8820672B2 (en) * | 2012-05-07 | 2014-09-02 | Honeywell International Inc. | Environmental sampling with an unmanned aerial vehicle |
| CN104731103A (en) * | 2015-01-21 | 2015-06-24 | 北京航空航天大学 | Stewart six degrees of freedom flight simulation platform under multi-layer closed-loop control strategy |
-
2015
- 2015-08-21 CN CN201510518011.3A patent/CN105068550B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101825903A (en) * | 2010-04-29 | 2010-09-08 | 哈尔滨工程大学 | Water surface control method for remotely controlling underwater robot |
| US8820672B2 (en) * | 2012-05-07 | 2014-09-02 | Honeywell International Inc. | Environmental sampling with an unmanned aerial vehicle |
| WO2014020596A1 (en) * | 2012-08-02 | 2014-02-06 | Israel Aerospace Industries Ltd. | An unmanned aerial vehicle |
| CN103970144A (en) * | 2014-03-28 | 2014-08-06 | 哈尔滨工程大学 | Autonomous underwater robot water-surface control system |
| CN103901893A (en) * | 2014-04-02 | 2014-07-02 | 哈尔滨工程大学 | Water surface control system of autonomous underwater robot |
| CN104731103A (en) * | 2015-01-21 | 2015-06-24 | 北京航空航天大学 | Stewart six degrees of freedom flight simulation platform under multi-layer closed-loop control strategy |
Non-Patent Citations (1)
| Title |
|---|
| 张帆: "基于MAS的多机器人搜救系统及任务分配方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
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| CN110609571B (en) * | 2019-08-06 | 2022-01-07 | 同济大学 | Multi-moving-object distributed collaborative visual positioning method based on multiple unmanned aerial vehicles |
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