CN105953799B - Underwater vehicle path planning method in gravitational field adaptation area based on Information Entropy - Google Patents
Underwater vehicle path planning method in gravitational field adaptation area based on Information Entropy Download PDFInfo
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Abstract
The invention discloses underwater vehicle path planning methods in the gravitational field adaptation area based on Information Entropy, and one is several grids for known critical subset segmentation；Two pairs of each nodes seek its gravitational field standard deviation, gravitational field longitude and latitude absolute roughness, gravitational field longitude and latitude related coefficient and gravitational field longitude and latitude slope aspect；Three determine above-mentioned parameter weight using Information Entropy, obtain comprehensive gravity field feature parameter by each parameter and its multiplied by weight and by product addition；Four determine the threshold value T of comprehensive gravity field feature parameter；Five calculate synthesis gravity field feature mean parameter of the adaptation area edge all the points in respective regional area, select the maximum marginal point of average value as the starting point n of trackstartAnd set step-size in search；Six from starting point nstartStart, using step-size in search as search radius, searches for next track points until terminal；All track points of seven connection sources to terminal；The present invention is able to solve the problem of track deviates gravity field feature informative region.
Description
Technical field
The invention belongs to the technical fields of Method in Gravity Aided INS system trajectory planning, and in particular to one kind is based on entropy
Underwater vehicle path planning method in the gravitational field adaptation area of method.
Background technique
With the significantly promotion of Aeronautics and Astronautics detective's ability, the concealment contradiction of China's strategy and the latent device of non-strategy is got over
More to protrude.The big country that, land-sea numerous as island has both, effect and status of the "Oceanic" strategy in China's overall development strategy
More and more important, underwater hiding-machine technology (such as " flood dragon number " manned underwater vehicle etc.) is indispensable in the development of marine technology
, there is highly important strategic importance.China's underwater hiding-machine mainly uses inertial navigation system at present, it can provide standard
Really, reliably course, speed and location information without from outer received signal, and have hidden without launching outward signal
Covering property height, entirely autonomous property.But its shortcomings that is that error accumulates at any time, and positioning accuracy is not able to satisfy the essence of the latent device of long endurance
Degree requires.For Energy economy quality and concealed consideration, underwater hiding-machine be likely to operation the long period without emerging,
Continue precisely to navigate so cannot achieve using inertial navigation system as navigation system merely, be improved using other supplementary means
Navigation accuracy is the trend of a certainty.
Geophysical Field Information is the intrinsic information of the earth, is often referred to attached in earth's surface or earth's surface by various geophysical methods
The information of the various physical phenomenons closely measured.In recent years, the Wuyuan navigation theory and method of geophysical character causes the country
Outer research institution and scholar's note that having carried out terrain match airmanship, geomagnetic auxiliary navigation technology and Gravity-aided navigation
Technology etc. simultaneously rapidly develops.For underwater navigation, the measurement of terrain data is relatively difficult, and earth's magnetic field is not very stable and is easy
It is interfered by ferromagnetic material etc., and gravitational field is very stable and gravimetric data is easy to measure, therefore Method in Gravity Aided INS system
The requirement of underwater hiding-machine " independence, high-precision, concealment " can greatly be met.
The trajectory planning of underwater hiding-machine is one of the core technology of Method in Gravity Aided INS system, especially in adaptation area
Trajectory planning.Trajectory planning refers to that one is found on the basis ofs considering underwater topography factor, threat and mission requirements etc. rises
Point arrives the feasible track of terminal, and main purpose is to guarantee that track can be good at correcting ins error after Gravity Matching,
Improve the precision of Gravity-aided navigation.There are many Path Planning, and there are commonly Voronoi diagram method, A* algorithms etc..Voronoi
Figure method is a kind of algorithm based on figure, it is divided into two steps in trajectory planning: the first step constructs Voronoi according to known threat
Figure, the boundary of Voronoi diagram constitute feasible initial track, and second step further navigates in conjunction with certain searching algorithm to plan
Mark.The track that this method obtains is very coarse, only just corresponds to solve that there are threaten in field to pass through from which when multiple threats
The problem of, and track is segmentation；A* algorithm is a kind of heuristic search algorithm of classics, which is typically employed in based on grid
In the numerical map of lattice, it is chiefly used in solving the problems, such as static programming, is generally taken in planning apart from cost and threat cost etc.
Weighted sum indicate actual cost, but its defect be weight gather mainly by examination and search time can with the increase of search space and
Increase.Both methods does not all account for the information of gravity Feature variation, is easy to cause to hinder to gravity matching and correlation inertial navigation.
Summary of the invention
In view of this, the present invention provides a kind of, the gravitational field based on Information Entropy is adapted to underwater vehicle trajectory planning in area
Method is able to solve the problem of track deviates gravity field feature informative region.
Realize that technical scheme is as follows:
It is a kind of based on Information Entropy gravitational field adaptation area in underwater vehicle path planning method, comprising the following steps:
Step 1: being divided into several grids according to preset resolution ratio for known adaptation area；
Step 2: it is absolutely coarse to seek its gravitational field standard deviation, gravitational field longitude to each node in several grids
Degree, gravitational field latitude absolute roughness, gravitational field longitude related coefficient, gravitational field latitude related coefficient, gravitational field longitude slope aspect
With gravitational field latitude slope aspect；
Step 3: the weight of above-mentioned each parameter is determined using Information Entropy, by each parameter and its multiplied by weight, and will
Product addition obtains comprehensive gravity field feature parameter；
Step 4: passing through hypsography degree in region caused by observation gravity field feature parameter size and gravity anomaly variation
Degree determines the threshold value T of comprehensive gravity field feature parameter；
Step 5: it is average to calculate synthesis gravity field feature parameter of the adaptation area edge all the points in respective regional area
Value selects starting point n of the comprehensive maximum marginal point of gravity field feature mean parameter as trackstart；And it is set according to selected areas
Determine step-size in search；
Step 6: from the starting point n of trackstartStart, it is big in comprehensive gravity field feature parameter using step-size in search as search radius
In searching for next track points in the region of threshold value T until terminal；
Step 7: connection source obtains the track to be planned to all track points of terminal.
Further, step 6 specifically:
Step 6.1, the starting point n with trackstartAs first current point, using current point as the center of circle, using step-length as radius
Work is justified, and the node that comprehensive gravity field feature parameter in circle is not more than threshold value T is excluded, using remaining node in circle as possible in next step
Track points, track points possible for each next step actual information amount f (n of the zequin to terminal according to the following formulastart,nj,
ngoal), wherein j >=1
Wherein, nj-1And njRespectively represent current point and in next step possible track points, as j=1, nj-1For nstart,
ngoalRepresent terminal, d (nstart,nj) indicate distance of the starting point to possible track points in next step, d (nj,ngoal) indicate next
Walk distance of the possible track points to terminal, g_charact (nj-1,nj) indicate between current point and in next step possible track points
Gravity field feature information change amount, p1、p2Respectively represent the weight of distance and gravity field feature information, p1> 0, p2> 0；
Step 6.2, using the possible track points of next step corresponding to actual information amount maximum value as next track points；
Step 6.3 continues searching next track points according to step 6 using next track points as new current point, works as terminal
When including in circle to be searched, next track points are determined as terminal, and terminate to search for.
The utility model has the advantages that
(1) path planning method of the invention determines the power of selected multiple gravity field feature parameters using Information Entropy
Weight, the comprehensive parameters enable more comprehensively describe gravity Feature variation；And by determining comprehensive gravity Feature
The threshold value of parameter reduces the search range of track, improves search efficiency.
(2) path planning method of the invention ensure that it is having preferably at the very start by the starting point of selection track
Gravity Matching effect；And the step-length by determining track search, the poor region of gravitation information will not be fallen by making track not only, but also not
The information that gravity field feature changes abundant region can be lost, ensure that the matching efficiency of track.
(3) path planning method combination Information Entropy of the invention improves A* algorithm, at the same time consider starting point distance and
The trajectory planning for carrying out passing through adaptation area on the basis of intermediate track points gravitation information, it is right in the case of only considering distance both to have avoided
Gravitation information is ignored, and in turn avoids only considering the deficiency that single parameter describes gravity Feature, improves the gravity of track
Matching rate.
Specific embodiment
Embodiment is named, the present invention will be described in detail.
The present invention provides a kind of, and the gravitational field based on Information Entropy is adapted to underwater vehicle path planning method in area, including
Following steps:
Step 1: a certain known 1 ° × 1 ° adaptation area is chosen, and taking resolution ratio is 0.5' × 0.5', critical subset segmentation
At 120 × 120 grid.
Step 2: taking gravitational field standard deviation, gravitational field longitude and latitude absolute roughness, gravitational field longitude and latitude related coefficient and again
Totally 7 characteristic parameters change field of force longitude and latitude slope aspect to describe the gravity Feature of adaptation, then 3 × 3 are taken around certain point
Regional area calculates each gravity field feature parameter value and using it as the gravity field feature parameter value of this point.
Step 3: determining the weight of several parameters selected above using the Information Entropy of proposition.Entropy be parameter index without
The measurement of sequence degree can be used for measuring effective information and determine weight that given data is included, by " entropy "
It calculates and determines weight, be exactly that the weight of each index is determined according to the difference degree of every gravity field feature parameter.When each evaluation object
A certain index value difference it is larger when, entropy is smaller, and the effective information for illustrating that the index provides is larger, and weight also should be compared with
Greatly；Conversely, entropy is larger if a certain index value difference is smaller, the information content for illustrating that the index provides is smaller, and weight also should be compared with
It is small.When certain index value for being respectively evaluated object is identical, entropy reaches maximum, it means that the index without useful information,
It can be removed from assessment indicator system.
Pass through the weight for above-mentioned selected several characteristic parameters that Information Entropy determines, available one comprehensive gravity field feature ginseng
Number, this parameter can more comprehensively describe the changing rule of gravitational field in adaptation area, avoid single parameter and describe gravitational field
The insufficient defect of information content.
Determine weight mainly using Information Entropy and have the following three steps:
(1) raw data matrix normalizes.If the raw data matrix of n evaluation object of m evaluation index is A=aij,
In the present invention, evaluation index be gravitational field standard deviation, gravitational field longitude and latitude absolute roughness, gravitational field longitude and latitude related coefficient and
Gravitational field longitude and latitude slope aspect totally 7 characteristic parameters, evaluation object is each node in grid, by matrix A=aijAfter normalization
Obtain R=(rij)m×n.For big person is excellent index, formula is normalized are as follows:
For small person is excellent index, formula is normalized are as follows:
(2) entropy is defined.In having m index, the n evaluation problems for being evaluated object, the entropy of i-th of index are as follows:
In formulaWherein k=1/ln (n) works as fijWhen=0, f is enabledijlnfij=0.
(3) entropy weight is defined
Inside above-mentioned selected several gravity field feature parameters, gravitational field standard deviation sigma, gravitational field longitude and latitude absolute roughness
rλrφ, gravitational field longitude and latitude slope aspect SλSφIt is the bigger the better, and gravity longitude and latitude field coefficient RλRφIt is the smaller the better.It therefore can be with
Obtain a comprehensive gravity field feature parameter:
New_T=w1·σ+w2·rλ+w3·rφ+w4·Rλ+w5·Rφ+w6·Sλ+w7·Sφ
Wherein w1To w7Respectively represent the weight of each gravity field feature parameter.
Step 4: to obtained comprehensive gravity field feature new parameter analysis above it is recognised that when comprehensive gravity is special
When parameter is levied less than a certain threshold value, hypsography degree is smaller in region, therefore gravity anomaly variation is unobvious, is unfavorable for
Gravity Matching is carried out, therefore is not suitable for carrying out trajectory planning；However when comprehensive gravity field feature parameter is greater than a certain threshold value,
Whole hypsography degree is bigger in region, therefore gravity anomaly variation is obvious, is conducive to carry out Gravity Matching, because
This is relatively appropriate for the trajectory planning of Gravity-aided navigation.So obtaining a comprehensive gravity by many experiments and comparison
The threshold value T of characteristic parameter is 0.45.
Step 5: the starting point and step-size in search of selection track.It obtains one and passes through the track in adaptation area it is necessary to selected
The edge in adaptation area selects gravity field feature variation most apparent a little as starting point, can be obtained with guaranteeing track from the beginning
Good matching effect.Here it proposes the maximum average value method of a comprehensive gravity field feature parameter: starting in edges of regions point
The regional area of m × n is selected around it, the size of m, n are consistent with selected step-size in search, calculate comprehensive gravity field feature parameter
Local mean values, as the gravity field feature parameter value of that point, and select starting point of the maximum value as track.Expression formula is such as
Under:
The selection of step-length also vital task for the trajectory planning in adaptation area, when taken step-length compared with
Hour, latent device is easily ensnared into the weaker point of gravity field feature variation and track is too tortuous, to increase navigation difficulty and increase
Hours underway is unfavorable for being corrected it to keep ins error excessive；And when taken step-length is larger, latent device holds very much again
Easily ignore certain gravity anomaly among track and change obvious point, to lose important information.Therefore choose one it is moderate
Track step-size in search is critically important for we carry out trajectory planning.Select 5 grid length for a step-length here.
Step 6: improving Path Planning.Traditional A* algorithm evaluation function can indicate are as follows:
F (n)=g (n)+h (n)
What it was indicated is the actual cost from starting point to the optimal path of any point n.Here it is only examined in traditional A* algorithm
On the basis of considering single parameter, the parameter that a variety of description gravity Features determined by Information Entropy are added in searching route is believed
Breath, makes track apart from moderate while passing through gravitation information region abundant, so that Gravity-aided navigation matching effect
Fruit is improved.Path Planning expression formula in improved adaptation area is as follows:
Wherein, nj-1And njRespectively represent current point and in next step possible track points, as j=1, nj-1For nstart,
ngoalRepresent terminal, d (nstart,nj) indicate distance of the starting point to possible track points in next step, d (nj,ngoal) indicate next
Walk distance of the possible track points to terminal, g_charact (nj-1,nj) indicate between current point and in next step possible track points
Gravity field feature information change amount, g_charact=abs (new_T (nj)-new_T(nj-1)), and new_T (nj) > T is (here
T represent the threshold value of comprehensive gravity field feature parameter), p1、p2Respectively represent the weight of distance and gravity field feature information, and p1+p2=
1。 (p1> 0 and p2>0)。
Using the possible track points of next step corresponding to actual information amount maximum value as next track points；By next track
Point as new current point continues searching next track points according to step 6, when terminal includes in circle to be searched, will under
One track points are determined as terminal, and terminate to search for.
The starting point n of track is selected in step 5 and step 6startAnd each track points is searched for described in the step 4
Synthesis gravity field feature parameter greater than threshold value T region in progress.
Step 7: connection source obtains the track to be planned to all track points of terminal.
Steps are as follows for improved Path Planning:
The starting point selected according to the above method is put into OPEN table, terminal is put into CLOSE table, and selects step-length for 5；
2), using starting point as origin, step-length is less than comprehensive gravity field feature threshold value T to search in surrounding's grid point of radius
Point be put into CLOSE table；
3) what is, searched within this range makes the maximum point of heuristic function value be used as next target point, and by target point
It is put into CLOSE table；
4), using this target point as new starting point, and new search is carried out according to step 2,3 with this；
5), when in search range including terminal, using terminal as next target point, and terminate to search for.
In conclusion the above is merely preferred embodiments of the present invention, being not intended to limit the scope of the present invention.
All within the spirits and principles of the present invention, any modification, equivalent replacement, improvement and so on should be included in of the invention
Within protection scope.
Claims (1)
1. underwater vehicle path planning method in the gravitational field adaptation area based on Information Entropy, which is characterized in that including following step
It is rapid:
Step 1: being divided into several grids according to preset resolution ratio for known adaptation area；
Step 2: seeking its gravitational field standard deviation, gravitational field longitude absolute roughness, again to each node in several grids
Field of force latitude absolute roughness, gravitational field longitude related coefficient, gravitational field latitude related coefficient, gravitational field longitude slope aspect and gravity
Field latitude slope aspect；
Step 3: determine the weight of above-mentioned each parameter using Information Entropy, by each parameter and its multiplied by weight, and by product
Addition obtains comprehensive gravity field feature parameter；
Step 4: passing through hypsography degree and gravity anomaly variation degree in region caused by observation gravity field feature parameter size
To determine the threshold value T of comprehensive gravity field feature parameter；
Step 5: calculating synthesis gravity field feature mean parameter of the adaptation area edge all the points in respective regional area, choosing
Select starting point n of the comprehensive maximum marginal point of gravity field feature mean parameter as trackstart；And it is set and is searched for according to selected areas
Step-length；
Step 6: from the starting point n of trackstartStart, using step-size in search as search radius, is greater than threshold in comprehensive gravity field feature parameter
Next track points are searched in the region of value T until terminal；
Step 6 specifically:
Step 6.1, the starting point n with trackstartAs first current point, using current point as the center of circle, justify by radius work of step-length,
The node that comprehensive gravity field feature parameter in circle is not more than threshold value T is excluded, using remaining node in circle as possible track in next step
Point, track points possible for each next step actual information amount f (n of the zequin to terminal according to the following formulastart,nj,
ngoal), wherein j >=1
Wherein, nj-1And njRespectively represent current point and in next step possible track points, as j=1, nj-1For nstart, ngoalIt represents
Terminal, d (nstart,nj) indicate distance of the starting point to possible track points in next step, d (nj,ngoal) indicate possible in next step
Distance of the track points to terminal, g_charact (nj-1,nj) indicate that the gravity between current point and in next step possible track points is special
Reference ceases variable quantity, p1、p2Respectively represent the weight of distance and gravity field feature information, p1>0,p2>0；
Step 6.2, using the possible track points of next step corresponding to actual information amount maximum value as next track points；
Step 6.3 continues searching next track points according to step 6 using next track points as new current point, when terminal includes
When in circle to be searched, next track points are determined as terminal, and terminate to search for；
Step 7: connection source obtains the track to be planned to all track points of terminal.
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| CN108871342A (en) * | 2018-07-06 | 2018-11-23 | 北京理工大学 | Subaqueous gravity aided inertial navigation based on textural characteristics is adapted to area's choosing method |
| CN109141426B (en) * | 2018-08-10 | 2020-11-10 | 中国空间技术研究院 | Method for matching navigation adaptation area by underwater gravity |
| CN111044041B (en) * | 2019-12-31 | 2021-04-27 | 北京理工大学 | Gravity-assisted inertial navigation adaptive area selection method based on gravity field three-dimensional characteristics |
| CN111649763B (en) * | 2020-04-26 | 2021-08-27 | 中国人民解放军61540部队 | Submarine navigation method and system established based on gravity beacon |
| CN112161621B (en) * | 2020-09-24 | 2022-11-25 | 北京理工大学 | Model-free auxiliary navigation adaptive area selection method |
| CN112729292A (en) * | 2021-01-08 | 2021-04-30 | 中国船舶重工集团公司第七0七研究所 | Gravity adaptive area selection method based on multiple statistical parameters of hierarchical analysis method |
| CN114577219A (en) * | 2022-03-01 | 2022-06-03 | 航天科工智能运筹与信息安全研究院(武汉)有限公司 | Track matching area selection system based on rule scoring |
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Effective date of registration: 20190326 Address after: 100029 3-4 Floor of Crawford Building, 23 Huixin East Street, Chaoyang District, Beijing Patentee after: Beijing techlink intelligent Polytron Technologies Inc Address before: 100081 No. 5, Zhongguancun South Street, Haidian District, Beijing Patentee before: BEIJING INSTITUTE OF TECHNOLOGY |