CN107368086B - Unmanned underwater vehicle path planning device and method based on detection threat domain - Google Patents

Unmanned underwater vehicle path planning device and method based on detection threat domain Download PDF

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CN107368086B
CN107368086B CN201710538828.6A CN201710538828A CN107368086B CN 107368086 B CN107368086 B CN 107368086B CN 201710538828 A CN201710538828 A CN 201710538828A CN 107368086 B CN107368086 B CN 107368086B
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CN107368086A (en
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杜雪
严浙平
管凤旭
邱天畅
张耕实
陈涛
周加佳
张宏瀚
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Harbin Engineering University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • 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
    • G05D1/04—Control of altitude or depth
    • G05D1/06—Rate of change of altitude or depth
    • G05D1/0692—Rate of change of altitude or depth specially adapted for under-water vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/343—Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
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Abstract

The invention provides a detection threat domain-based unmanned underwater vehicle path planning device and method, which solve the problem of path planning of a UUV (unmanned underwater vehicle) in a terrain obstacle environment based on a path planning algorithm of a detection threat domain and can meet the kinematic constraint, collision avoidance constraint and hidden detection constraint of the UUV. A path from a motion starting point to a motion terminal point is planned at a given initial position, a terminal point position, a maximum curvature constraint, a path discrete point resolution, a hidden safety index and the like, and the path is smooth, continuous and derivable, and meets the navigation turning curvature constraint, the hidden safety index and the like of a UUV (unmanned Underwater vehicle) so that the UUV can safely and covertly reach the terminal point in the shortest time. The method applies the detection threat theory and the geometric theory of navigation turning curvature constraint to the field of path planning of UUV for the first time, can rapidly realize path planning, is simple and reliable, easy to realize, small in calculated amount and good in real-time performance, can meet the requirement of path planning, improves the practicability of path planning, and has positive significance for the development of the field of underwater path planning in the future.

Description

Unmanned underwater vehicle path planning device and method based on detection threat domain
Technical Field
The invention relates to the research of a hidden path planning algorithm of an unmanned underwater vehicle, in particular to a path planning device and a path planning method of the unmanned underwater vehicle based on a detection threat domain, belonging to the fields of terrain visibility, underwater sound, complex systems and intelligent control.
Background
Path planning is an important problem in the field of unmanned underwater vehicle research, and can generate a safe navigable path for an unmanned underwater vehicle, wherein the optimal path or the near optimal path avoids obstacles in the process from the initial state to the target state.
The UUV path planning has important practical significance as an important function for guaranteeing that the UUV can complete the operation task safely and efficiently. Numerous scholars at home and abroad carry out a great deal of research on the aspect, a great deal of effective planning algorithms are proposed, and abundant research results are obtained, such as artificial potential fields, A-algorithm, visual graph method, fast expansion random numbers, Voronoi diagrams, various optimization algorithms and the like are widely researched and applied. The above path planning problem of generating a path between one or more nodes of a network is well known in the fields of operations research, communication, computational geometry and graphics, however, applying the planning concepts in these fields to covert planning of unmanned undersea vehicles is a challenge. Two of the most important constraints for hidden path planning of unmanned underwater vehicles are the navigability and the hidden safety of the path. Navigability is that a navigation path is usually composed of a series of segmented straight line segments connecting waypoints, but such a path is not a navigable path, and it is difficult to satisfy aircraft kinematics or motion constraints, and since each pair of connected navigable segments must have the same tangent to form a path of continuous curvature, it is important to orient each waypoint so that each waypoint matches each other. The concealed safety refers to the threat that the vehicle is threatened by sonar matrixes, defense facilities and the like from the other party when the vehicle executes a certain concealed task, so that the success rate of task execution is influenced, and the concealed safety of the vehicle is threatened to a certain extent. Meanwhile, the path planning of the detection threat area needs to be based on certain geographic data, the large-scale geographic data volume urgently needs to be efficiently stored and transmitted, and therefore, the environmental data needs to be compressed, so that the data redundancy is reduced, and the data storage and the corresponding communication cost are reduced.
In summary, the existing unmanned underwater vehicle path planning method mostly only considers the existing problems of the obstacles, but does not consider how to process the path points to enable the path points to meet the constraint conditions such as turning curvature and the like, and the problems of safety constraints such as detection threats and the like based on underwater acoustics.
Disclosure of Invention
The invention aims to solve the problem of hidden path planning of an unmanned underwater vehicle under the condition of multiple constraints and provides a path planning device and a path planning method of the unmanned underwater vehicle based on a detection threat domain, which can plan a continuously feasible path for the unmanned underwater vehicle under the condition of terrain obstacles and solve various physical constraint conditions of the unmanned underwater vehicle when the unmanned underwater vehicle detects threats on enemies.
The purpose of the invention is realized as follows: unmanned underwater vehicle route planning device based on survey threat territory, its characterized in that: the method comprises an environment detection threat domain unit, a UUV hidden path initialization unit, a UUV safe path point generation unit, a UUV hidden path correction optimization unit and a UUV hidden path output unit, wherein the environment detection threat domain unit constructs a UUV navigation basic space frame according to given underwater environment parameters by an interpolation method and calculates a threat index which can be detected at each position point by adopting an underwater visibility method; the UUV hidden path initialization unit is connected with the environment detection threat domain unit, defines and disperses each detection threat domain according to hidden safety indexes, threatens the geometric representation of the domain obstacle by a covering circle domain method, and receives UUV navigation initial parameters or conditions; the UUV safe path point generating unit generates a UUV safe path point according to the initial point A of the UUV1Position, randomly generating the next path point A2Preventing the path from colliding with the threat domain obstacle; the UUV hidden path generating unit generates a hidden path according to A1And A12Sequentially generates the subsequent path points A13,A14,...,A1mAnd make it twoTwo connected paths P1So that P is1Does not collide with the obstacle in the threat domain, and generates a group of path clusters which are used for solving the collision with the obstacle in the same way
Figure BDA0001341346080000021
The UUV hidden path correction optimization unit sets a flexible feasible curve cluster
Figure BDA0001341346080000022
For candidate path population, correcting the current maneuvering feasible curve cluster by adopting a safety constraint evolutionary algorithm
Figure BDA0001341346080000023
Selecting an optimal hidden safety path of the UUV which meets the kinematic constraint in the three-dimensional environment; and the UUV hidden path output unit receives and outputs the planned optimal hidden safety path meeting the constraint condition.
The invention also includes such structural features:
1. the unmanned underwater vehicle path planning method based on the detection threat domain comprises the unmanned underwater vehicle path planning device based on the detection threat domain, and comprises the following steps:
step 1: the method comprises the following steps that an environment detection threat domain unit constructs a basic space frame of an underwater environment, sets latitude and longitude and elevation information of a navigation range, a terrain interpolation method and an environment unit resolution, calculates detected threat evaluation of each position point in an underwater environment model according to the environment unit resolution and an underwater visibility method, and fuses with the latitude and longitude information of the environment to form four-dimensional coordinate description;
the threat assessment is: the detection threat vis of the unmanned underwater vehicle is characterized by a terrain visibility value; each position point (x)i,yi) Detection of threat level vis and covert security index vismaxComparing and obtaining threat assessment vis of each positioni: when visi≥vismaxThe time indicates that the position is not hidden at this time, so that visiWhen vis 1i＜vismaxThen, then visiThe value is less than 1 and is not changed;
step 2: the UUV navigates in a fixed-height mode, the hidden safe path initialization unit of the UUV sets state parameters and constraint conditions of path planning, the state parameters comprise the initial position and the final position of the UUV, and the constraint conditions comprise maximum curvature constraint kappamaxConcealed safety index vismax(ii) a Defining and dispersing each detection threat domain according to the concealed safety index based on an environment detection threat domain unit, and solving the geometric representation of the threat domain obstacle by a covering circular domain method;
and step 3: a UUV safe path point generating unit, wherein the UUV navigates in a height-fixed mode according to the initial position of the UUV, and the initial point is set as AsAnd randomly generating a first path point A11And make AsAnd A11Length of line between does not exceed dmAnd the path does not collide with the threat domain obstacle, wherein AnmM and n in (1) respectively represent the m-th point on the n-th path;
judging point A by using the principles of computational geometrys(xs,ys) And A11(x11,y11) Whether the paths between the two are connected can be reached: according to As(xs,ys) And A11(x11,y11) Linear function L of the line segment1：
Figure BDA0001341346080000031
For a certain obstacle circle ObiRespectively find out the center O of the circlei(xoi,yoi) To AsA11Distance of line segment lLs-11And respectively reacting them with ObiRadius R ofOiAnd (3) comparison: when l isLs-11＞ROiWhen it is shown that Ob isiDoes not affect AsA11Thereby sequentially judging each obstacle circle to obtain As(xs,ys) Point and point A11(x11,y11) Whether the paths between the two paths are communicated and can be reached;
and 4, step 4: the UUV hidden path generating unit generates a hidden path according to AsAnd A11Sequentially generates the subsequent path points A12,A13,A14,...,Am1And connecting the two paths to obtain a path P1So that P is1Does not collide with the obstacle in the threat domain, and generates a group of path clusters which are used for solving the collision with the obstacle in the same way
Figure BDA0001341346080000032
N is a natural number, and each path point meets the maximum rotation angle range under the maximum curvature constraint of the UUV;
the length of the connecting line between each path point does not exceed dmWhen d ismWhen the distance is set within a reasonable range, the broken line path P can be considered according to the local linearization principlenThe method is characterized by comprising the following steps that a curve path is represented in a linear mode, wherein the four dimensions of a curve control point of a planned path are composed of a space three-dimensional coordinate and a one-dimensional threat coordinate, and the planned path is also described by a four-dimensional coordinate comprising the space three-dimensional coordinate and the threat coordinate;
and 5: UUV hidden path correction optimization unit set flexible feasible curve cluster
Figure BDA0001341346080000033
Calculating the safety comprehensive cost of the hidden path for the candidate path population based on the detection threat domain unit, and correcting the current maneuvering feasible curve cluster by adopting a safety constraint evolutionary algorithm
Figure BDA0001341346080000034
Selecting an optimal hidden safety path of the UUV which meets the kinematic constraint in the three-dimensional environment;
step 6: and the UUV hidden path output unit outputs the reconstructed hidden security path which meets the constraint condition.
2. The step 2 specifically comprises the following steps: setting the sailing height as z0And z is expressed in terms of a grid representation of the elevation terrain0To various terrain elevations ziPerforming step function calculation:
Figure BDA0001341346080000035
evaluating threats visiAnd elevation information ziPerforming an identity-or operation, i.e. for non-integer forms of visiIn other words, when visiWhen < 1, the vis is viewed in the same or operationi0, thereby obtaining a comprehensive threat obstacle Oi(ii) a Barrier to grid OiPerforming XOR operation with surrounding obstacle points to obtain continuous obstacles
Figure BDA0001341346080000041
And according to a method of covering a circular field for a continuous obstacle
Figure BDA0001341346080000042
Maximum circular characterization achieved;
the method for covering the circular domain specifically comprises the following steps: according to continuous obstacles, to prevent concave obstacle geometry
Figure BDA0001341346080000043
The occupied polygon formed by the grid points is expanded into a matrix by taking the maximum/minimum longitude and the maximum/minimum latitude of the polygon as boundaries
Figure BDA0001341346080000044
Then a continuous obstacle is characterized
Figure BDA0001341346080000045
Maximum circular shape of
Figure BDA0001341346080000046
The circle center position and the radius length of the matrix are respectively
Figure BDA0001341346080000047
And 1/2 diagonal length.
3. In the step 5: constructing an adaptive function at the cost of probing threat and path length as follows:
Fc(i)＝wthr·thr(i)+wd·dist(i)
wherein：wthrAnd wdFor weighting coefficients, thr (i) ═ 1/vis (i) is detection threat degree, dist (i) is path length, each feasible maneuver curve is a single independent individual, population iteration times Num are set to obtain the adaptability function of each individual at present, a certain number of parents with highest adaptability and the highest generation of individuals are selected by adopting a safety constraint evolution algorithm, continuous pairing accompanied by a certain random mutation probability is carried out to generate a new generation of population, then the adaptability function of the new individual is recalculated, iterative updating is carried out until the maximum iteration times Num is reached, and finally the path with highest adaptability is selected as the optimal hidden safety path.
Compared with the prior art, the invention has the beneficial effects that: the method adopts a path planning algorithm based on the detection threat domain to solve the path planning problem of the UUV in the terrain obstacle environment, and the planned path can meet the kinematic constraint, collision avoidance constraint, hidden detection constraint and the like of the UUV. And (3) planning a path from the UUV movement starting point to the end point by giving parameters or conditions such as the initial position, the end point position, the maximum curvature constraint, the path discrete point resolution, the hidden safety index and the like of the UUV, and meeting the navigation turning radius constraint, the hidden safety index and the like of the UUV so that the UUV can safely and covertly reach the end point in the shortest time. The method can rapidly realize the path planning of the hidden safety of the UUV, is simple and reliable, is easy to realize, has small calculated amount and better real-time property, and can better meet the path planning requirement of the UUV.
Drawings
FIG. 1 is a schematic diagram of a path planning structure of an unmanned underwater vehicle based on a detection threat domain according to an embodiment of the invention;
FIG. 2 is a flow chart of the unmanned underwater vehicle path planning algorithm based on the detection threat domain;
range D satisfying UUV maximum curvature constraint upon generation of path points in FIG. 3mAnd generating a geometric description of the process.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 shows that an embodiment of the invention is based on probingThe structure of the unmanned underwater vehicle path planning device of the threat domain can be realized in one computer, and the unmanned underwater vehicle path planning device of the threat domain is composed of an environment detection threat domain unit 1, a UUV hidden path initialization unit 2, a UUV safe path point generation unit 3, a UUV hidden path generation unit 4, a UUV hidden path modification optimization unit 5 and a UUV hidden path output unit 6. The environment detection threat domain unit 1 constructs a UUV navigation basic space frame according to given underwater environment parameters and a certain interpolation method, and calculates the detectable threat indexes of each position point by adopting an underwater visibility method; the UUV hidden path initialization unit 2 is connected with the environment detection threat domain unit 1, defines and disperses each detection threat domain according to hidden safety indexes, threatens the geometric representation of the domain obstacle by a covering circle domain method, and receives UUV navigation initial parameters or conditions; the UUV safe path point generating unit 3 is connected with the hidden path initializing unit 2 and is used for generating a path according to the initial point A of the UUV1Position, randomly generating the next path point A2Preventing the path from colliding with the threat domain obstacle; UUV hidden path generation unit 4 and safe path point generation unit 3 according to A1And A12Sequentially generates the subsequent path points A13,A14,...,A1mAnd connecting the two paths to obtain a path P1So that P is1Does not collide with the obstacle in the threat domain, and generates a group of path clusters which are used for solving the collision with the obstacle in the same way
Figure BDA0001341346080000051
A UUV hidden path correction optimizing unit 5 and a hidden path generating unit 4 are provided with a mobile feasible curve cluster
Figure BDA0001341346080000052
For candidate path population, correcting the current maneuvering feasible curve cluster by adopting a safety constraint evolutionary algorithm
Figure BDA0001341346080000053
Selecting an optimal hidden safety path of the UUV which meets the kinematic constraint in the three-dimensional environment; UUV hidden path output unit 6 and hidden path correction optimization sheetAnd 5, receiving and outputting the planned optimal concealed safe path meeting the constraint condition. The four-dimensional curve control point of the planned path consists of a space three-dimensional coordinate and a one-dimensional threat coordinate, and the planned path is also described by a four-dimensional coordinate comprising the space three-dimensional coordinate and the threat coordinate.
In order to make the objects, technical solutions and advantages of the present invention more clearly apparent, the UUV path planning method and apparatus based on detecting threat domains of the present invention are further described in detail below with reference to the accompanying drawings.
The following structure is utilized to implement the unmanned underwater vehicle path planning method based on the detection threat domain, please refer to fig. 2 for showing the following specific steps:
step 1: the method comprises the following steps that an environment detection threat domain unit constructs a basic space frame of an underwater environment, sets latitude and longitude and elevation information of a navigation range, a terrain interpolation method and environment unit resolution, calculates threat indexes which can be detected at each position point in an underwater environment model based on an underwater visibility method according to the environment unit resolution, and fuses with the latitude and longitude information of the environment to form four-dimensional coordinate description;
a basic space frame of an underwater environment is based on a Digital Elevation Model (DEM), a regular grid Model is adopted, all topographical points in the grid Model under the current environment are subjected to traversal analysis through a topographical visibility analysis method of a patent of an underwater surface topographical visibility analysis method based on sound ray tracks, and meanwhile certain limiting factors such as sound intensity attenuation are considered according to actual design requirements. For example, a passive sonar is selected for analysis, according to the sonar equation:
SL-TL-(NL-DI)≥DT (1)
the inter-AB trajectory is only valid if SL-TL- (NL-DI) satisfying the target noise A is greater than the detection threshold DT of sonar B. Points on the sound ray emerging from A, where the sound source level SL is the radiated noise of the target, produced by target A; NL-DI is the level of self-noise and ambient noise received by the directional transducer, and both these and the detection threshold DT are determined by the sonar B. When the object a and sonar B are determined, SL, NL-DI can be considered as constants, so the decision whether the sound ray trajectory is valid depends mainly on the propagation loss TL. Due to geometrical attenuation and physical absorption caused by the expansion of the wave front, the sound intensity gradually weakens in the propagation process, the process of converting sound energy into heat energy is physical absorption, and the correspondingly reduced sound intensity is propagation loss. The propagation loss can be derived from the propagation distance and the radiated noise frequency, and the formula is expressed as:
TL＝15lgr+0.036f3/2·r+60(dB) (2)
where r is in km and frequency in kHz. The first and third terms of the right formula are geometric losses, and the middle term is physical absorption losses. The sound waves are spread in a spherical manner around, and the maximum spherical radius is represented by r. According to the definition of terrain visibility, the unmanned underwater vehicle is difficult to navigate in a range with a high probability of being found in a region with a high terrain visibility value, so the patent firstly normalizes the terrain visibility value and uses the terrain visibility value to represent the detection threat vis of the unmanned underwater vehicle. Each position (x)i,yi) Detection of threat level vis and covert security index vismaxComparing and obtaining threat assessment vis of each positioniI.e. when visi≥vismaxThe time indicates that the position is not hidden at this time, so that visiWhen vis 1i＜vismaxThen, then visiValues < 1 are unchanged.
Step 2: the method comprises the following steps that a UUV hidden safety path initialization unit sets parameters or conditions of a path planning UUV initial position, an end position, maximum curvature constraint, path discrete point resolution, hidden safety indexes and the like, an environment-based detection threat domain unit defines and disperses each detection threat domain according to the hidden safety indexes, and the geometric representation of a threat domain obstacle is solved through a coverage circular domain method;
the UUV adopts a fixed-height navigation state, and the parameters comprise initial position coordinates (x) of the UUVs,ys,zs) Initial position coordinate (x)f,yf,zf) The constraint includes a maximum curvature constraint kmaxAnd covert security indicators vismax. Let the sailing height be z0Then, thenUnder the condition that the elevation information is certain, the three-dimensional UUV path planning problem is converted into a two-dimensional problem, and z is represented according to the grid representation form of the elevation terrain0To various terrain elevations ziPerforming step function calculations, i.e.
Figure BDA0001341346080000061
Each position (x)i,yi) Detection of threat level vis and covert security index vismaxComparing and obtaining threat assessment vis of each positioniAnd evaluate the threat visiAnd elevation information ziPerforming an identity-or operation, i.e. for non-integer forms of visiIn other words, when visiWhen < 1, the vis is viewed in the same or operationi0, thereby obtaining a comprehensive threat obstacle Oi. Barrier to grid OiPerforming XOR operation with surrounding obstacle points to obtain continuous obstacles
Figure BDA0001341346080000062
And according to a method of covering a circular field for a continuous obstacle
Figure BDA0001341346080000071
Maximum circular characterization achieved.
The method for covering the circular area is realized by preventing the concave obstacle geometry from generating according to the continuous obstacle
Figure BDA0001341346080000072
The occupied polygon formed by the grid points is expanded into a matrix by taking the maximum/minimum longitude and the maximum/minimum latitude of the polygon as boundaries
Figure BDA0001341346080000073
Then a continuous obstacle is characterized
Figure BDA0001341346080000074
Maximum circular shape of
Figure BDA0001341346080000075
The circle center position and the radius length of the matrix are respectively
Figure BDA0001341346080000076
And 1/2 diagonal length.
And step 3: a UUV safe path point generating unit for generating a UUV safe path point according to the initial point A of the UUVsPosition, randomly generating the first waypoint A11So that A issAnd A11Length of line between does not exceed dmAnd the path does not collide with the threat domain obstacle, wherein AnmM and n in (1) respectively represent the m-th point on the n-th path;
judging point A by applying principles of computational geometrys(xs,ys) And A11(x11,y11) Whether the paths between are connected and reachable. According to As(xs,ys) And A11(x11,y11) Linear function L of the line segments-11I.e. by
Figure BDA0001341346080000077
For a certain obstacle circle ObiRespectively find out the center O of the circlei(xoi,yoi) To AsA11Distance of line segment lLs-11And respectively reacting them with ObiRadius R ofOiComparison, whenLs-11＞ROiWhen it is shown that Ob isiDoes not affect AsA11Thereby sequentially judging each obstacle circle, A can be judgeds(xs,ys) Point and point A11(x11,y11) Whether the paths between are connected and reachable. Wherein
Figure BDA0001341346080000078
Figure BDA0001341346080000079
And 4, step 4: UUV hidden path generation unit according to AsAnd A11Sequentially generates the subsequent path points A12,A13,A14,...,A1mAnd connecting the two paths to obtain a path P1So that P is1Does not collide with the obstacle in the threat domain, and generates a group of path clusters which are used for solving the collision with the obstacle in the same way
Figure BDA00013413460800000710
N is a natural number, and each path point meets the maximum rotation angle range under the maximum curvature constraint of the UUV. The four-dimensional control point of the planned path consists of a space three-dimensional coordinate and a one-dimensional threat coordinate, and the planned path is also described by a four-dimensional coordinate comprising the space three-dimensional coordinate and a threat coordinate time coordinate;
the whole environment is constructed based on a northeast coordinate system, and the length of a connecting line between every two path points does not exceed dmWhen d ismWhen the distance is set within a reasonable range, the broken line path P can be considered according to the local linearization principlenIs a linearized characterization of the curved path. Setting maximum value rho of path point step length of UUVmaxAnd ρ ismaxIt is not desirable to set the path to be too large so that the curve path of the UUV is characterized by local linearization between path points. The position of each path point is directly encoded by coordinates, genes are represented by real number point coordinates, and chromosomes are represented by point connections with variable numbers.
As shown in FIG. 3, point A in the pathmAre all Am-1(xm-1,ym-1) And Am-2(xm-2,ym-2) Range D formed to satisfy UUV maximum curvature constraintmIn which D is generated randomlymIs defined by the polar coordinate form (p)m-1,θm-1) Denotes, the superscript m-1 denotes Am-1Is an origin, and has a coordinate axis Xm-1And Ym-1Coordinates when they are parallel to the North east, respectively, and satisfy ρm-1∈(0,ρmax)，θm-1∈ (-gamma +, gamma +), wherein is Am-1(xm-1,ym-1) And Am-2(xm-2,ym-2) The slope of the line in which the line segment is located.
Since the length of the link between each path point is set within a reasonable range, the broken-line path can be considered as a linearized representation of the curved path according to the principle of local linearization. According to analytic geometrical principle, when O' Am-1＝O'Am-2＝1/κmaxThen, the maximum range D can be obtainedmDue to the fact that
Figure BDA0001341346080000081
Let the maximum curvature constraint be κmaxAccording to the cosine law
Figure BDA0001341346080000082
Then γ is obtained from equation (7):
Figure BDA0001341346080000083
thus with Am-1As the center of circle, opening angle is 2 gamma, radius dmThe sector formed in the UUV is the range D of the maximum curvature constraint of the UUVmWherein the coordinates of the two segments of the arc are respectively (d)mcos(γ+)-xm-1,dmcos(γ+)-ym-1)，(dmcos(-γ)-xm-1,dmcos(-γ)-ym-1)，
Figure BDA0001341346080000084
Is Am-1Am-2The slope of (a). In the range DmIn random generation
Figure BDA0001341346080000085
And
Figure BDA0001341346080000086
next path point
Figure BDA0001341346080000087
And obtaining the coordinates under the northeast coordinate system through coordinate conversion
Figure BDA0001341346080000088
And 5: a UUV hidden path correction optimization unit for setting a flexible feasible curve cluster
Figure BDA0001341346080000089
Calculating the safety comprehensive cost F of the hidden path based on the detection threat domain unit for the candidate path populationcCorrecting the current maneuvering feasible curve cluster by adopting a safety constraint evolutionary algorithm
Figure BDA00013413460800000810
Selecting an optimal hidden safety path of the UUV which meets the kinematic constraint in the three-dimensional environment;
path cluster formed by N movable paths at present
Figure BDA00013413460800000811
The kinematics of the UUV are satisfied, but the safety constraint is not satisfied, and the requirement is to construct an adaptive function at the cost of detecting the threat degree and the path length, as described by equation (8):
Fc(i)＝wthr·thr(i)+wd·dist(i) (8)
wherein wthrAnd wdFor the weighting coefficients, thr (i) ═ 1/vis (i) represents the detection threat level, dist (i) represents the path length, and this can be determined by geometric analysis. Each mobile feasible curve is a single independent individual, the iteration number Num of the population is set, the adaptive function of each current individual is calculated according to the formula (3), a certain number of parents with the highest fitness and the most generations are selected by adopting a safety constraint evolution algorithm, continuous pairing accompanied by a certain random mutation probability is carried out, a new generation population is generated, then the adaptive function of the new individual is recalculated, iterative updating is carried out until the maximum iteration number Num is reached, and finally, the path with the highest adaptability is selected as the optimal hidden safety path; the function shows that the shorter the path and the higher the chromosome fitness with better imperceptibility, the higher the probability of inheritance to the next generation, and finally an optimal path or a sub-optimal path (satisfactory solution) is planned under the guidance of the fitness function.To facilitate understanding of the relationship of each waypoint in a path, waypoints in the following will take XiAnd (4) showing. The genetic operation module comprises a selection operator, a crossover operator, a mutation operator, a special insertion operator and a special deletion operator, and the specific implementation method comprises the following steps:
1. selection process
(1) Calculating the fitness F of each individual according to the designed fitness functionc(i)；
(2) Solving the sum of the fitness of all the individuals;
(3) calculating the proportion cfitness (i) ═ F of each individual fitnessc(i)/sum；
(4) Calculating the cumulative value of the fitness proportion of each individual;
(5) by generating a random number X between (0, 1);
(6) comparing the cfitness value of the first individual with the random number X in sequence, and selecting the individual i with the first occurring cfitness value larger than X (namely the random number falls in the probability area of the individual) as the genetic object of the operation;
(7) repeating the above steps (5) and (6) until the number of inherited individuals reaches a predetermined number.
In order to accelerate the overall optimization speed and ensure that the individual fitness is better and better, an elite retention strategy is added after the selection is completed:
(1) finding out the optimal individual and the worst individual in the current population;
(2) comparing the current optimal individual with the optimal individual fitness so far, and if the current optimal individual fitness is better, taking the current optimal individual as a new optimal individual so far;
(3) the worst individual in the current population is replaced with the best individual so far.
2. The crossing process theoretically comprises single-point crossing and multi-point crossing, and in view of ensuring the continuity of the path when planning the path of the aircraft, the invention selects the multi-point crossing from a certain point to ensure the continuity of the path as much as possible:
(1) randomly pairing chromosomes in pairs with a crossover probability PcJudging whether each pair of chromosomes is subjected to cross operation;
(2) if the cross probability is met, randomly generating a cross point i;
(3) carrying out mutual crossing operation on the two chromosomes which are paired from the cross point i to the last gene n, namely simultaneously crossing at multiple points;
(4) after the two chromosomes have crossed randomly, the two new chromosomes cross at randomly generated crossover position XiAnd the previous position Xi-1There may be an obstacle or X in betweeniIs not at Xi-2And Xi-1The determined navigation constraint range is combined, and the requirement is not met, so that a special insert operator is introduced for the situation, and insert operation is carried out, so that the final navigation path is rationalized;
(5) and (4) repeating the steps (2) to (4), and performing the operation once on all the paired chromosomes according to the process, thus finishing the intersection.
3. The purpose of the mutation process is two: the method has the advantages that the local search capability of the genetic algorithm is enhanced, the diversity of the population is kept, the premature phenomenon is prevented, and the local optimal solution is generated. The invention adopts a basic bit variation method:
(1) with a mutation probability PmJudging whether to perform mutation operation on each individual;
(2) if the mutation probability is satisfied, a mutation position X is randomly generatedi；
(3) Chromosomal genes were mutated at the site of mutation. There are two basic principles of variation:
on the one hand, the newly generated position XiMust be based on the first two adjacent positions Xi-2And Xi-1And satisfies the navigation constraint range D of UUVm；
On the other hand, the newly generated position XiMust be in contact with the previous position Xi-1There is no obstacle in between.
And if the new position generated by the variation does not meet the two basic principles, reselecting the variation position until the variation position meets the requirement.
(4) And (5) repeating the operations (2) and (3), and performing mutation on all chromosomes once according to the process.
4. Insert operation and delete operation
In the invention, a navigation path generated by a genetic algorithm according to a random principle is beneficial to the diversity of a population, but a plurality of same positions (the actual path is represented by circles) can appear, aiming at the problem, the invention provides a deletion operator, and meanwhile, the intersection operation is likely to cause a new path to no longer meet two basic principles of intersection near the intersection position, so that an insertion operator is provided:
(1) the previous position X of the crossing positioni-1As a starting point, the crossing position XiAs an end point;
(2) using a method for generating a starting population at Xi-1And XiA series of sailing positions meeting the requirements are generated between the X and the Xi-1And XiBecomes a feasible path.
The specific implementation steps of the deletion operation are as follows:
(1) from the current position X of the pathi(including the starting point) and finding the first identical position X from front to backmDelete XiTo XmAll positions in between (including X)i)；
(2) With XmContinue to find the next identical location for the current location, delete all locations (including X) between it and the next identical locationm)；
(3) And (3) repeatedly executing the steps (1) and (2) until the current position is the final end point, stopping the deletion operation, and finishing the deletion operation.
Finally, because the map environment depended on by the invention is comparatively large, the planning time is not required forcibly, therefore, the termination condition is directly set that the population genetic algebra exceeds the set value, the genetic operation is immediately ended, and the final result is output.
Step 6: and the UUV hidden path output unit outputs the reconstructed hidden security path which meets the constraint condition.
Outputting the planned unmanned underwater vehicle path, which comprises the optimal path P obtained in the steps 2 to 5nEach path point A ofnmAs well as the initial and end points of the,and the detection threat of each path point.
In summary, the invention provides a detection threat domain-based unmanned underwater vehicle path planning device, which adopts a detection threat domain-based path planning algorithm to solve the path planning problem of a UUV in a terrain obstacle environment, and the planned path can meet the kinematic constraint, collision avoidance constraint, hidden detection constraint and the like of the UUV. And planning a path from the UUV motion starting point to the end point by giving parameters or conditions such as the initial position, the end point position, the maximum curvature constraint, the path discrete point resolution, the hidden safety index and the like of the UUV, wherein the planned path is smooth and continuous and can meet the navigation turning curvature constraint, the hidden safety index and the like of the UUV, so that the UUV can safely and covertly reach the end point in the shortest time. The method applies the detection threat theory and the geometric theory of navigation turning curvature constraint to the field of path planning of UUV for the first time. The method can rapidly realize UUV path planning, is simple and reliable, is easy to realize, has small calculated amount and good real-time performance, can better meet the path planning requirement of the UUV, improves the practicability of the UUV path planning, and has positive significance for the development of the field of underwater path planning in future.

Claims (4)

1. Unmanned underwater vehicle route planning device based on survey threat territory, its characterized in that: the method comprises an environment detection threat domain unit, a UUV hidden path initialization unit, a UUV safe path point generation unit, a UUV hidden path correction optimization unit and a UUV hidden path output unit, wherein the environment detection threat domain unit constructs a UUV navigation basic space frame according to given underwater environment parameters by an interpolation method and calculates a threat index which can be detected at each position point by adopting an underwater visibility method; the UUV hidden path initialization unit is connected with the environment detection threat domain unit, defines and disperses each detection threat domain according to hidden safety indexes, threatens the geometric representation of the domain obstacle by a covering circle domain method, and receives UUV navigation initial parameters or conditions; the UUV safe path point generating unit generates a UUV safe path point according to the initial point A of the UUVsPosition, randomly generating the next path point A11To prevent the path from colliding with the threat domain obstacleCollision; the UUV hidden path generating unit generates a hidden path according to AsAnd A11Sequentially generates the subsequent path points A12,A13,...,A1mAnd connecting the two paths to obtain a path P1So that P is1Does not collide with the obstacle in the threat domain, and generates a group of path curve clusters which do not collide with the obstacle in the same way
Figure FDA0002515794470000011
The UUV hidden path correction optimization unit is provided with a flexible and feasible path curve cluster
Figure FDA0002515794470000012
For candidate path population, a safety constraint evolutionary algorithm is adopted to correct the path curve cluster which is feasible by the current maneuver
Figure FDA0002515794470000013
Selecting an optimal hidden safety path of the UUV which meets the kinematic constraint in the three-dimensional environment; and the UUV hidden path output unit receives and outputs the planned optimal hidden safety path meeting the constraint condition.
2. The unmanned underwater vehicle path planning method based on the detection threat domain is characterized by comprising the following steps: the unmanned underwater vehicle path planning device based on the detection threat domain comprises the following steps:
step 1: the method comprises the following steps that an environment detection threat domain unit constructs a basic space frame of an underwater environment, sets latitude and longitude and elevation information of a navigation range, a terrain interpolation method and an environment unit resolution, calculates detected threat evaluation of each position point in an underwater environment model according to the environment unit resolution and an underwater visibility method, and fuses with the latitude and longitude information of the environment to form four-dimensional coordinate description;
the threat assessment is: the detection threat vis of the unmanned underwater vehicle is characterized by a terrain visibility value; each position point (x)i,yi) Detection of threat level vis and covert security index vismaxCompared with each other to obtainThreat assessment vis to locationsi: when visi≥vismaxThe time indicates that the position is not hidden at this time, so that visiWhen vis 1i＜vismaxThen, then visiThe value is less than 1 and is not changed;
step 2: the UUV navigates in a fixed-height mode, the hidden safe path initialization unit of the UUV sets state parameters and constraint conditions of path planning, the state parameters comprise the initial position and the final position of the UUV, and the constraint conditions comprise maximum curvature constraint kappamaxConcealed safety index vismax(ii) a Defining and dispersing each detection threat domain according to the concealed safety index based on an environment detection threat domain unit, and solving the geometric representation of the threat domain obstacle by a covering circular domain method;
and step 3: a UUV safe path point generating unit, wherein the UUV navigates in a height-fixed mode according to the initial position of the UUV, and the initial point is set as AsAnd randomly generating a first path point A11And make AsAnd A11Length of line between does not exceed dmAnd the path does not collide with the threat domain obstacle, wherein AnmM and n in (1) respectively represent the m-th point on the n-th path;
judging point A by using the principles of computational geometrys(xs,ys) And A11(x11,y11) Whether the paths between the two are connected can be reached: according to As(xs,ys) And A11(x11,y11) Linear function L of the line segments-11：
Figure FDA0002515794470000021
For a certain obstacle circle ObiRespectively find out the center O of the circlei(xoi,yoi) To AsA11Distance of line segment lLs-11And respectively reacting them with ObiRadius R ofOiAnd (3) comparison: when l isLs-11＞ROiWhen it is shown that Ob isiDoes not affect AsA11Thereby sequentially judging each barrierObstruct the circle, obtain As(xs,ys) Point and point A11(x11,y11) Whether the paths between the two paths are communicated and can be reached;
and 4, step 4: the UUV hidden path generating unit generates a hidden path according to AsAnd A11Sequentially generates the subsequent path points A12,A13,A14,...,A1mAnd connecting the two paths to obtain a path P1So that P is1Does not collide with the obstacle in the threat domain, and generates a group of path curve clusters which do not collide with the obstacle in the same way
Figure FDA0002515794470000022
N is a natural number, and each path point meets the maximum rotation angle range under the maximum curvature constraint of the UUV;
the length of the connecting line between each path point does not exceed dmWhen d ismWhen the distance is set within a reasonable range, the broken line path P can be considered according to the local linearization principlenThe method is characterized by comprising the following steps that a curve path is represented in a linear mode, wherein the four dimensions of a curve control point of a planned path are composed of a space three-dimensional coordinate and a one-dimensional threat coordinate, and the planned path is also described by a four-dimensional coordinate comprising the space three-dimensional coordinate and the threat coordinate;
and 5: UUV hidden path correction optimization unit is set with flexible and feasible path curve cluster
Figure FDA0002515794470000023
Calculating the safe comprehensive cost of the hidden path for the candidate path population based on the detection threat domain unit, and correcting the current feasible path curve cluster by adopting a safe constraint evolution algorithm
Figure FDA0002515794470000024
Selecting an optimal hidden safety path of the UUV which meets the kinematic constraint in the three-dimensional environment;
step 6: and the UUV hidden path output unit outputs the reconstructed hidden security path which meets the constraint condition.
3. The unmanned underwater vehicle path planning method based on the detection threat domain according to claim 2, characterized in that: the step 2 specifically comprises the following steps: setting the sailing height as z0And z is expressed in terms of a grid representation of the elevation terrain0To various terrain elevations ziPerforming step function calculation:
Figure FDA0002515794470000025
evaluating threats visiAnd elevation information ziPerforming an identity-or operation, i.e. for non-integer forms of visiIn other words, when visiWhen < 1, the vis is viewed in the same or operationi0, thereby obtaining the comprehensive threat obstacle COi(ii) a Rasterizing barrier GCOiPerforming XOR operation with surrounding obstacle points to obtain continuous obstacles
Figure FDA0002515794470000031
And according to a method of covering a circular field for a continuous obstacle
Figure FDA0002515794470000032
Maximum circular characterization achieved;
the method for covering the circular domain specifically comprises the following steps: according to continuous obstacles, to prevent concave obstacle geometry
Figure FDA0002515794470000033
The occupied polygon formed by the grid points is expanded into a matrix by taking the maximum/minimum longitude and the maximum/minimum latitude of the polygon as boundaries
Figure FDA0002515794470000034
Then a continuous obstacle is characterized
Figure FDA0002515794470000035
Maximum circular shape of
Figure FDA0002515794470000036
The circle center position and the radius length of the matrix are respectively
Figure FDA0002515794470000037
And 1/2 diagonal length.
4. The unmanned underwater vehicle path planning method based on the detection threat domain according to claim 2, characterized in that: in the step 5: constructing an adaptive function at the cost of probing threat and path length as follows:
Fc(i)＝wthr·thr(i)+wd·dist(i)
wherein: w is athrAnd wdFor weighting coefficients, thr (i) ═ 1/vis (i) is the degree of detection threat, dist (i) is the path length, each feasible maneuver curve is a single independent individual, the population iteration number Num is set to obtain the adaptability function of each individual at present, a certain number of parents with the highest adaptability of the individual with the highest adaptability are selected by adopting a safety constraint evolution algorithm, continuous pairing accompanied by a certain random mutation probability is carried out to generate a new generation population, then the adaptability function of the new individual is recalculated, iterative updating is carried out until the maximum iteration number Num is reached, and finally the path with the highest adaptability is selected as the optimal covert safety path.
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