CN111307159A - Multi-AUV three-dimensional collaborative route planning method - Google Patents

Multi-AUV three-dimensional collaborative route planning method Download PDF

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CN111307159A
CN111307159A CN202010197447.8A CN202010197447A CN111307159A CN 111307159 A CN111307159 A CN 111307159A CN 202010197447 A CN202010197447 A CN 202010197447A CN 111307159 A CN111307159 A CN 111307159A
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level set
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CN111307159B (en
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刘厂
刘瑞航
盛亮
张志强
高峰
靳光强
赵艳玲
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Harbin Engineering University
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    • 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
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Abstract

The invention discloses a multi-AUV three-dimensional collaborative route planning method, which mainly comprises the following steps: three-dimensional environment abstract modeling and level set function initialization, level set function evolution, optimal route point selection, collaborative route scheme design, route conflict judgment and re-planning, and planning result output. According to the collaborative planning method, a plurality of air routes can be planned in the air space at the same time by improving the level set algorithm, the planning efficiency of the algorithm is improved, all AUVs are required to reach the destination at the same time when the collaborative scheme is designed, each AUV needs to start one by one according to delay time, in addition, under the condition of considering rapidity and concealment, a collaborative planning scheme with optimum combination of concealment and air time is designed, the influence of ocean current and sound velocity factors is considered at the same time, and a conflict judgment and re-planning link is added, so that the collaborative air routes are safer; compared with two-dimensional route planning, the three-dimensional route planning has higher practicability and can better meet the actual navigation needs.

Description

Multi-AUV three-dimensional collaborative route planning method
Technical Field
The invention belongs to the technical field of three-dimensional path planning of underwater vehicles, relates to a multi-AUV three-dimensional collaborative route planning method, and particularly relates to a multi-AUV three-dimensional collaborative route planning method considering marine environment influence.
Background
At present, most of researches are limited to the problem of single AUV (autonomous underwater vehicle) route planning, and the problem of multi-route collaborative planning from multiple starting points to multiple end points under the influence of marine environment is not considered. In practical situations, to increase the completion rate of tasks, all AUVs are required to reach the multitasking point at the same time. At this time, how to generate an effective air route for each AUV and cooperate with the arrival time of each AUV is one of the preconditions for completing the task. Due to the requirement of practical application, a multi-AUV concept is generated, namely, on the basis of application of a single AUV, a plurality of AUVs are coordinated with each other to jointly complete certain complex underwater target tasks. The method not only makes up the defects of the execution task of a single AUV, but also improves the working efficiency, saves the cost of researching more complicated AUVs, and shows the superiority of multiple AUVs in executing multiple cooperative tasks. In addition, the complex functions which can be realized by multiple AUVs cannot be realized by a single AUV. Therefore, the research of multiple AUVs is increasingly receiving high attention from students worldwide.
The Level Set Method (Level Set Method) is proposed by American mathematicians Stanley Osher and James Sethian, developed from the research field of simulating interface evolution, and is an effective calculation tool for processing geometric topology in the interface evolution process. The core of the improved level set algorithm is that the gravity of ocean current and a hidden field and the speed of an AUV (autonomous underwater vehicle) are used as the power of level set function evolution, and the gradient direction of a zero level set curve is an optimal solution. The current level set algorithm has a complete mathematical theory basis, is simple in concept, easy to program and implement, and has low requirements on the speed and storage of computer hardware. The method has good application prospect in various fields as an emerging optimization algorithm. Patent CN201810519546.6 discloses a UUV path planning method based on energy consumption and sampling amount multi-objective optimization under the influence of complex marine environment, and the considered marine environment information is only ocean current, and marine environment information such as temperature, salinity, etc. is not considered. Patent CN201610887874.2 discloses a time-optimal route planning method based on a level set algorithm, which only designs a single route planning algorithm of a single AUV under the influence of ocean currents in the marine environment, does not consider multiple AUVs for collaborative route research, and does not disclose a document to apply the level set algorithm to a three-dimensional collaborative route planning research considering the influence of the marine environment at present.
Disclosure of Invention
Aiming at the prior art, the technical problem to be solved by the invention is to provide a multi-AUV three-dimensional collaborative route planning method which can simultaneously plan a plurality of optimal routes in a one-time iterative cycle process and add the influence of ocean current and sound velocity.
In order to solve the technical problem, the invention provides a multi-AUV three-dimensional collaborative route planning method, which comprises the following steps:
step 1: performing three-dimensional environment modeling according to the electronic chart, the ocean current, the temperature, the salinity and the depth information, and establishing a terrain field, an ocean current field and a hidden field; initializing a level set function, initializing the level set function into a circular function with a starting point as a circle center and r as a radius, and setting a proper narrow band width d to enable the level set function to evolve in a narrow band;
step 2: level set function evolution: the level set function considers the AUV self speed, the ocean current speed and the hidden field to obtain an improved level set function evolution mathematical model, the evolution is carried out according to the improved level set function evolution mathematical model, when the narrow band edge is reached, the level set function and the narrow band width are reconstructed, the level set function is reinitialized into a new distance function, the new narrow band width is calculated, whether each terminal point is reached or not is judged, and all zero level set curves before the terminal point are stored when each terminal point is reached;
and step 3: selecting an optimal waypoint: carrying out forward iteration on the end point of each zero level set curve to find out the gradient direction point of each zero level set curve until the start point, and sequentially connecting all the gradient direction points to obtain all the optimal routes;
and 4, step 4: designing a collaborative route allocation scheme: simultaneously planning all m multiplied by m routes from m starting points to m end points by using the improved level set function evolution mathematical model obtained in the step 2, wherein n route combinations coexist in the planned m multiplied by m routes, wherein
Figure BDA0002418120040000021
M routes from n route combinations to m task points are selected, and all routes are set to H ═ H { (H)1,1,H1,2,…,H1,m；H2,1,H2,2,…,H2,m；…；Hn,1,Hn,2,…,Hn,mIn which H isn,mRepresenting navigation routes of m AUVs in the n schemes, so that H represents navigation routes of all AUVs in the n schemes;
AUV navigation energy consumption is set as:
Figure BDA0002418120040000022
where k is the coefficient of energy consumption, VaxRepresents the X-direction component in the speed of the AUV; vayRepresents the Y-direction component in the speed of the AUV; l isx＝|Lxi+1-Lxi| represents the route length of the X-direction component of the route; l isy＝|Lyi+1-Lyi| represents the route length of the Y-direction component of the route;
thus, an energy loss N is obtained that represents all the routes in each combination:
N＝{N1,1,N1,2,…,N1,m；N2,1,N2,2,…,N2,m；…；Nn,1,Nn,2,…,Nn,m}；
suppose that the selected route is Hi＝{Hi,1,Hi,2,…Hi,mTherein, the time of flight Ti＝{Ti,1,Ti,2,…Ti,m}, navigation energy consumption Ni＝{Ni,1,Ni,2,…Ni,mThe hiding size of the route, i.e. the place on the route nodeThe sum of the magnitudes of the attractive forces is as follows: mi＝{Mi,1,Mi,2,…Mi,m}, intersection D of air pathsi＝{Di,1,Di,2,…Di,m}；
Constraint (1):
Max{Ni}≤Nmax
constraint (1) indicates that: n is a radical ofmaxRepresenting the maximum energy carried by each AUV, namely the energy consumption of all the routes in the selected routes cannot exceed the maximum energy carried by the AUV;
constraint (2):
Figure BDA0002418120040000023
constraint (2) indicates that: the time of different AUVs reaching the intersection point of the routes cannot be the same, namely, no time conflict point exists between different routes;
constraint (3): each task can be allocated or executed only once, namely the task has uniqueness;
putting all routes into a m multiplied by m cellular array A, knowing that AUV can not reach the same task point according to the constraint condition (3), and combining n (wherein
Figure BDA0002418120040000031
) And (3) planning the collaborative air routes, and then calculating a time optimal matrix Ta ═ Ta of each scheme in n1,Ta2,…,TanAnd a concealment optimization matrix Ma ═ Ma1,Ma2,…,ManCalculating a cooperative objective function value C of each scheme by using a formula C which is p.Ta + q.Ma, wherein p is a set time influence factor, q is a set hiding influence factor, arranging all the route schemes from small to large according to the objective function values, and storing the route schemes into an array S, wherein the route scheme corresponding to the minimum value of the objective function value is an optimal route scheme;
and 5: selecting an optimal route scheme from the array S to perform route conflict judgment, wherein the route conflict judgment comprises collision conflict judgment and energy conflict judgment, executing the step 6 when collision conflict or energy conflict exists, otherwise, outputting the current route scheme, and finishing the multi-AUV three-dimensional collaborative route planning;
wherein the collision conflict determination specifically comprises:
(1) all the routes HiEach route is represented by a line segment, and Hi,1Represented as all discrete segments, H, in the ith flight pathi,1＝(X1,1,X1,2,…,X1,n1) Wherein X is1,1Representing a segment of two adjacent discrete points in the route, n1 represents that the route is composed of n1 line segments, so that the route is re-represented as Hi＝{(X1,1,X1,2,…,X1,n1)，(X，2,1，,X2,2,…,X2,n2)…(Xm,1,Xm,2,…,Xm,nm)}；
(2) The linear function expression of a line segment composed of adjacent discrete points in an airway is known as y-kx + b, so the airway is re-expressed as Hi＝{(K1,1,K1,2,…,K1,n1),(K2,1,K2,2,…,K2,n2)，…，(Km,1,Km,2,…,Km,nm) In which K ism,nmRepresents Xm,nmA functional expression of this line segment;
(3) then comparing each line segment in each route with each line segment in other routes to obtain all intersection points in the range of the line segments, namely route intersection points Di＝{Di,1,Di,2,…Di,m}; calculating the time difference of the two AUVs reaching the intersection point, and if the time difference of the two AUVs reaching the intersection point is smaller than the set safe time difference, collision conflict exists;
the energy conflict judgment specifically comprises the following steps: setting navigation energy consumption Ni＝{Ni,1,Ni,2,…Ni,mJudge maximum route energy consumption Max (N) in the schemei) Relationship with the nominal energy P carried by the AUV, when Max (N)i)>P, there is an energy conflict;
step 6: and (5) carrying out route re-planning, removing the route schemes with collision conflict or energy conflict from the array S, and executing the step 5.
The invention also includes:
1. the three-dimensional environment modeling in the step 1 specifically comprises the following steps:
firstly, dividing a real marine environment space subjected to AUV route planning into lon x lat x depth grids, wherein the grid division principle is as follows: the grid spacing in the longitude direction and the latitude direction is consistent with the resolution of the grid water depth data file in the navigation area, and the grid spacing in the depth direction should not exceed 1/10 of the maximum depth value of the navigation area;
and (3) carrying out terrain field modeling of an AUV navigation area: the method is realized by reading a grid water depth data file of a navigation area, wherein an area below a water depth value read from the file in a navigation space is set as a no-navigation area, and AUV speed data, ocean current data and hidden field gravity data contained in a grid of the no-navigation area are set as 0 to be used for restricting the navigation depth of an AUV;
carrying out ocean current field modeling of an AUV navigation area: the method is realized by reading a grid ocean current data file of a navigation area, if the resolution of the ocean current data file is lower than that of a water depth data file, linear interpolation processing is carried out on the ocean current data file, otherwise, the ocean current data file is subjected to thinning processing until the resolution of the ocean current data file is the same as that of the water depth data file, so that each grid contains ocean current information of the current position; the size of the ocean current in the depth direction is set to 0;
hidden field modeling of an AUV navigation area is carried out: firstly, reading a grid temperature, salinity and depth data file of a navigation area, performing the same processing as a sea flow data file, then calculating sound velocity information c of different grid points of the navigation area according to the temperature, salinity and depth information in the AUV navigation area, knowing the position of a detected sonar, and calculating sound propagation loss under the influence of the sound velocity of the AUV navigation area, wherein the calculation of the sound propagation loss specifically comprises the following steps:
the acoustic propagation loss TL in the AUV navigation region is:
TL＝20logR+α·R
wherein R is distance, α is suctionThe coefficient of the light-receiving coefficient is,
Figure BDA0002418120040000041
β is the set sound speed influence coefficient;
the known high-quality factor of the detection sonar is FOM, and the navigation space is divided into an exposed area, a dangerous area and a hidden area according to the relationship between the sound propagation loss and the quality factor of the sonar, specifically:
when TL > FOM, the area is a hidden area;
when in use
Figure BDA0002418120040000042
When the area is the exposed area;
when in use
Figure BDA0002418120040000043
The area is a hazardous area;
the hidden field modeling of the navigation area is carried out by calculating the gravity value of the AUV by grid points in different areas of the navigation area, which specifically comprises the following steps:
in the navigation space, a hidden field is represented by a gravitational field, the gravitations of the divided hidden area, dangerous area and exposed area to the AUV are different in magnitude, the gravitation of the hidden area is set to be a fixed value F ═ F, wherein F is a factor influencing the AUV route planning algorithm by the concealment and is set to be 2 times of the maximum ocean current value of the navigation area, the gravitation of the exposed area is 0, and the gravitation of the dangerous area is set to be:
Figure BDA0002418120040000044
wherein R is the distance between the current position of AUV and sonar position, RTLTL is the distance of the FOM position.
2. The initialization of the level set function in the step 1 specifically comprises the following steps:
setting a level set function as phi (X, t), wherein X represents a smooth closed curve, at the time of t, using an evolution curve X (t) to represent a zero level set curve phi (X, t) of the current time as 0, initializing the zero level set curve as a circle with a starting point as a circle center and r as a radius, setting d as a narrow band width, wherein d > r, the level set function is a distance function, a level set function value in the zero level set curve is a negative value, a level set function value outside the zero level set curve is a positive value, and in order to ensure that the zero level set curve can reach an end point, the level set function only evolves in a direction that the level set function value is a positive value, and the level set function is initialized as follows:
φ(X,t)＝0:(x-X1)2+(y-Y1)2+(z-Z1)2＝r2
wherein X, y, z are grid point coordinates, (X)1，Y1，Z1) As starting point coordinates.
3. The level set function evolution mathematical model in the step 2 specifically comprises the following steps:
Figure BDA0002418120040000051
v (H, t) is ocean current, U is the speed of AUV, M is the size of a hidden field, and H represents a position vector in a navigation space;
Figure BDA0002418120040000052
as the direction of the navigation,
Figure BDA0002418120040000053
which is the direction of the attractive force.
The invention has the beneficial effects that: according to the multi-AUV three-dimensional collaborative route planning method, the level set algorithm is improved and applied, the designed collaborative scheme firstly requires that all AUVs can make full use of ocean currents to reach the terminal point most quickly, sonar detection can be avoided in route planning, and collaborative routes with optimal concealment are planned.
Drawings
FIG. 1 is a flow chart of a multi-AUV three-dimensional collaborative route planning method considering marine environmental influence according to the present invention.
FIG. 2 is a flow chart of an improved level set algorithm employed by the present invention.
Fig. 3 is a flow chart of the collaborative planning scheme in the present invention.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings.
The invention discloses a multi-AUV three-dimensional collaborative route planning method, which mainly comprises the following steps:
step 1: constructing a three-dimensional route planning environment space based on the electronic chart, the ocean current, the temperature, the salinity and the depth information, and establishing a terrain field, an ocean current field and a hidden field; and (3) performing abstract modeling of a three-dimensional environment and initialization of a level set function, initializing the level set function into a circular function with a starting point as a circle center and r as a radius, and setting a proper narrow band width d to enable the level set function to evolve in a narrow band.
Step 2: and (4) evolving a level set function. And (3) the level set function evolves under the common influence of the AUV self speed, the ocean current speed and the hidden field, evolves according to an improved level set function evolution equation, reconstructs the level set function and the narrow band width when reaching the narrow band edge, re-initializes the level set function into a new distance function, calculates the new narrow band width, judges whether each terminal point is reached, and stores all zero level set curves before the terminal point is reached every time one terminal point is reached.
And step 3: and selecting the optimal waypoints. And carrying out forward iteration from the end point of each zero level set curve to find out the gradient direction point of each zero level set curve until the start point, and sequentially connecting all the gradient direction points to obtain all the optimal routes.
And 4, step 4: and designing a collaborative route allocation scheme. In the invention, the collaborative route scheme considers the condition that the AUV navigation time and the concealment are optimally fused. Therefore, in the design of the scheme, an optimal air route combination scheme of the air time and the concealment in all the AUV collaborative air route schemes needs to be selected, and in order to further consider the problem of the concealment, all the AUVs need to reach the destination at the same time, so that the departure time of each AUV is different.
And 5: and (4) determining and re-planning the route conflict. The route conflict judgment comprises the conditions that collision occurs in the AUV route process and the AUV cannot reach a target point due to energy problems. The collision problem is mainly the condition that a plurality of AUVs intersect at the same time in position and time, the energy problem is mainly that the energy carried by the AUVs cannot support the AUVs to reach a target point, any one of the two conditions occurs, the cooperative combination is used up, the airway re-planning is carried out, the re-planning is mainly to select the suboptimal airway combination in the scheme, and then the collision judgment is carried out again.
Step 6: and when the conflict-free route combination scheme is selected according to the algorithm, outputting the currently selected collaborative route, and finishing the multi-AUV three-dimensional collaborative route planning.
With reference to the attached drawings 1 to 3, the method comprises the following specific steps:
step 1: three-dimensional environment modeling and level set function initialization.
Step 1.1, constructing a three-dimensional route planning environment space based on the electronic chart and the marine environment information
Firstly, dividing a real marine environment space subjected to AUV route planning into lon x lat x depth grids. The grid division principle is as follows: and the grid spacing in the longitude direction and the latitude direction is consistent with the resolution of the grid water depth data file in the navigation area, the grid spacing in the depth direction should not exceed 1/10 of the maximum depth value of the navigation area, and then modeling of a terrain field, an ocean current field and a hidden field of the AUV navigation area is carried out.
The modeling of the terrain field of the AUV navigation area is realized by reading a grid water depth data file of the navigation area, an area below a water depth value read from the file in a navigation space is set as a no-navigation area, and AUV speed data, ocean current data and hidden field gravitation data contained in a grid of the no-navigation area are set as 0 to restrict the navigation depth of the AUV and ensure the safety of the AUV.
The ocean current field modeling of the AUV navigation area is realized by reading grid ocean current data files of the navigation area, if the resolution of the ocean current data files is lower than that of the water depth data files, linear interpolation processing is carried out on the ocean current data files, otherwise, the ocean current data files are subjected to thinning processing until the resolution of the ocean current data files is the same as that of the water depth data files, and each grid contains ocean current information of the current position; the size of the ocean current in the depth direction is set to 0.
Hidden field modeling of AUV navigation area: firstly, grid temperature, salinity and depth data files of a navigation area are read, the same processing as a sea flow data file is carried out, then, sound velocity information of the navigation area is calculated according to the temperature, the salinity and the depth information in the AUV navigation area, the position of a detection sonar is known, sound propagation loss under the influence of the sound velocity of the AUV navigation area is calculated, the quality factor of the detection sonar is known as FOM, a navigation space is divided into an exposed area, a dangerous area and a hidden area according to the relation between the sound propagation loss and the quality factor of the sonar, and hidden field modeling of the navigation area is carried out by calculating the gravity value of the AUV through grid points of different areas of the navigation area.
(1) Sound velocity calculation
And obtaining the sound velocity c of different grid points of the AUV navigation area according to the temperature, salinity and depth data of the AUV navigation area and a conventional sound velocity formula.
(2) Calculation of acoustic propagation loss and blind field gravity in navigational area
The acoustic propagation loss TL in the AUV navigation region is:
TL＝20logR+α·R
where R is the distance, α is the absorption coefficient,
Figure BDA0002418120040000071
β the sound speed influence coefficient was set to 0.5.
When TL > FOM, the area is a hidden area;
when in use
Figure BDA0002418120040000072
When this region isAn exposed region;
when in use
Figure BDA0002418120040000073
The area is a hazardous area;
in the navigation space, a hidden field is represented by a gravitational field, and the gravitational force of the divided hidden area, the dangerous area and the exposed area to the AUV is different in magnitude. The gravity of the hidden area is set to be a fixed value F ═ F, wherein F is a factor influencing the AUV route planning algorithm by the concealment, the gravity of the hidden area is set to be 2 times of the maximum ocean current value of the navigation area, the gravity of the exposed area is 0, and the gravity of the dangerous area is set to be:
Figure BDA0002418120040000074
wherein R is the distance between the current position of AUV and sonar position, RTLTL is the distance of the FOM position.
The attraction of the hidden area to the AUV is the largest, and the AUV is most easily selected to enter the hidden area when planning a route; the gravitation of the danger area is increased along with the increase of the distance between the AUV and the sonar; the attraction of the exposure area to the AUV is 0, and the AUV generally does not choose to enter the exposure area when planning a route unless the starting point is in the exposure area.
Step 1.2 level set function initialization
Let the level set function be phi (X, t), where X represents a smooth closed curve, and at time t, the evolution curve X (t) is used to represent the zero level set curve phi (X, t) at the current time as 0. The zero level set curve is initialized to a circle with the starting point as the center and r as the radius, and d is set to a narrow bandwidth, wherein d > r. The level set function is a distance function, the level set function value in the zero level set curve is a negative value, the level set function value outside the zero level set curve is a positive value, and in order to ensure that the zero level set curve can reach a terminal point, the level set function only evolves towards the direction in which the level set function value is a positive value. The level set function is initialized to:
φ(X,t)＝0:(x-X1)2+(y-Y1)2+(z-Z1)2＝r2
where X, y, z are grid point coordinates, X1，Y1，Z1As starting point coordinates.
Step two: and (4) evolving a level set function.
The evolution of the level set function value is mainly influenced by ocean currents V (H, t), the AUV self speed U and a hidden field M, wherein H represents a position vector in a navigation space, and the level set evolution function under the influence of the AUV self speed U is as follows:
Figure BDA0002418120040000081
under the influence of ocean currents, the level set evolution function is:
Figure BDA0002418120040000082
the influence of a hidden field is added, the influence of the hidden field generated in the zero level set curve evolution process is processed by adopting a method of combining an artificial potential field method and a level set algorithm, namely, the attraction of a region with strong concealment to the zero level set curve is larger, the size of the hidden field is M, and the direction of the attraction force is M
Figure BDA0002418120040000083
The gravity direction is the sum direction of the curve of the zero level set pointing to different hidden areas and dangerous areas in the navigation space, so the level set evolution function under the action of the hidden field is as follows:
Figure BDA0002418120040000084
in addition, the sailing direction is
Figure BDA0002418120040000085
Comprehensively, the level set function evolution mathematical model is as follows:
Figure BDA0002418120040000086
the level set function evolves in a narrow band according to the equation, and an evolvable curve is composed of a multipoint set. When the level set curve evolves to the narrowband edge, the level set function value outside the narrowband needs to be periodically calculated by calculating the distance of the point outside the narrowband from the current zero level set curve because the level set function value outside the narrowband is not updated.
When the improved level set algorithm provided by the invention carries out level set function evolution, all the zero level set curves from the current terminal point to the starting point are saved when the zero level set curve reaches one terminal point, and the level set function evolution is finished when the level set function values at all the terminal points are zero or negative values.
Step three: and selecting the optimal waypoints.
In order to obtain the time and the hidden optimal navigation path, the sum speed of the AUV in the navigation process is composed of the speed U of the AUV, the current speed and the gravity of a hidden field. When the navigation direction of the AUV is perpendicular to the gradient direction of the level set function and navigates at a speed U, the AUV is always located on the evolving zero level set curve, and because of the arbitrariness of the gradient direction of the level set function, if the optimal route solution is performed from the starting point along the gradient direction of the zero level set curve, there are multiple routes, and therefore, it is necessary to find a gradient direction point on each zero level set curve from the end point to the starting point direction as an optimal route point.
Step four: and designing a collaborative route scheme.
All m × m routes from m starting points to m end points can be planned simultaneously by using the improved level set algorithm, so that m collaborative routes need to be selected from the m × m routes. The planned m multiplied by m routes coexist with n route combinations, wherein
Figure BDA0002418120040000091
M routes for m AUVs to reach m task points need to be selected from n route combinations. Setting all routes to H ═ H1,1,H1,2,…,H1,m；H2,1,H2,2,…,H2,m；…；Hn,1,Hn,2,…,Hn,mIn which H isn,mRepresents the navigation routes of the m AUVs in the n-th scenario, so H represents the navigation routes of all AUVs in the n scenarios.
The energy consumption of the AUV is related to the speed of the AUV itself and the length of the route, so the energy consumption for AUV navigation is set as:
Figure BDA0002418120040000092
where k represents the coefficient of energy consumption, constant determined by the AUV's own parameters, VaxRepresents the X-direction component in the speed of the AUV; vayRepresents the Y-direction component in the speed of the AUV; l isx＝|Lxi+1-Lxi| represents the route length of the X-direction component of the route; l isy＝|Lyi+1-Lyi| represents the route length of the Y-direction component of the route.
Thus, an energy loss N is obtained that represents all the routes in each combination.
N＝{N1,1,N1,2,…,N1,m；N2,1,N2,2,…,N2,m；…；Nn,1,Nn,2,…,Nn,m}。
Suppose that the selected route is Hi＝{Hi,1,Hi,2,…Hi,mTherein, the time of flight Ti＝{Ti,1,Ti,2,…Ti,m}, navigation energy consumption Ni＝{Ni,1,Ni,2,…Ni,mThe hiding size of the air route (the sum of all gravitation sizes on the air route nodes) is Mi＝{Mi,1,Mi,2,…Mi,m}, intersection D of air pathsi＝{Di,1,Di,2,…Di,m}。
Constraint (1):
Max{Ni}≤Nmax
constraint (1) indicates that: n is a radical ofmaxThe maximum energy carried by each AUV indicates that the energy consumption of all the routes in the selected routes must not exceed the maximum energy carried by the AUV.
Constraint (2):
Figure BDA0002418120040000101
constraint (2) indicates that: the time of different AUVs reaching the intersection point of the routes cannot be the same, which indicates that no time conflict point exists between different routes[7]。
Constraint (3): each task can only be assigned or executed once, indicating that the task is unique.
Putting all routes into a m multiplied by m cellular array A, knowing that AUV can not reach the same task point according to the constraint condition (3), and combining n (wherein
Figure BDA0002418120040000102
) And (3) planning the collaborative air routes, and then calculating a time optimal matrix Ta ═ Ta of each scheme in n1,Ta2,…,TanAnd a concealment optimization matrix Ma ═ Ma1,Ma2,…,ManAnd calculating a cooperative objective function value C of each scheme by using a formula C, wherein p is a time influence factor set to be 0.4, q is a hiding influence factor set to be 0.6, arranging all the route schemes from small to large according to the objective function values, storing the route schemes into an array S, and selecting an optimal scheme from the array S for collision judgment, wherein the route scheme corresponding to the minimum value of the objective function value is the optimal route scheme.
Step five: and (4) determining and re-planning the route conflict.
Step 5.1 route conflicts
Because the route planned by the improved level set algorithm consists of unequal discrete points and the route consists of connecting lines of all adjacent discrete points, the invention can adopt a segment-by-segment comparison method on the aspect of judging the intersection points of the routes.
(1) All the routes HiEach route is represented by a line segment, and Hi,1Represented as all discrete segments, H, in the ith flight pathi,1＝(X1,1,X1,2,…,X1,n1) Wherein X is1,1Representing a segment of two adjacent discrete points in the route, n1 represents that the route is composed of n1 line segments, so that the route is re-represented as Hi＝{(X1,1,X1,2,…,X1,n1)，(X，2,1，,X2,2,…,X2,n2)…(Xm,1,Xm,2,…,Xm,nm)}。
(2) The linear function expression of a line segment composed of adjacent discrete points in an airway is known as y-kx + b, so the airway is re-expressed as Hi＝{(K1,1,K1,2,…,K1,n1),(K2,1,K2,2,…,K2,n2)，…，(Km,1,Km,2,…,Km,nm) In which K is1,1Represents X1,1A functional expression of this line segment;
(3) then comparing each line segment in each route with each line segment in other routes to obtain all intersection points in the range of the line segments, namely route intersection points Di＝{Di,1,Di,2,…Di,m}; in addition, let navigation consume Ni＝{Ni,1,Ni,2,…Ni,mThe maximum route energy consumption Max (N) in the scheme only needs to be judgedi) Less than the rated energy P carried by the AUV.
Step 5.2 route re-planning
Calculating whether two AUVs generate intersection in time at the intersection point; if the two AUVs are in collision, the probability of collision of the two AUVs is extremely high, a suboptimal scheme in the schemes is selected, and then collision judgment is carried out until a scheme that the AUVs do not collide is selected; in addition, when the energy consumption conflict problem occurs, namely the situation that the energy carried by the AUV cannot support the AUV to reach the target point occurs, a suboptimal scheme in the schemes is selected, and then conflict judgment is carried out until the scheme that the AUV does not conflict is selected.
Step six: and when the conflict-free route combination scheme is selected according to the algorithm, outputting the currently selected collaborative route, and finishing the multi-AUV three-dimensional collaborative route planning.
According to the invention, by improving the level set algorithm, a plurality of air routes can be planned in the air space at the same time, and the planning efficiency of the algorithm is improved. And simultaneously, an AUV three-dimensional collaborative route planning scheme is provided, an improved level set algorithm is applied to the AUV three-dimensional collaborative route planning scheme, all AUVs are required to arrive at a destination simultaneously when the collaborative scheme is designed, each AUV needs to start one by one according to delay time, and in addition, a collaborative planning scheme with the optimal combination of the concealment and the navigation time is designed under the condition of considering rapidity and the concealment. The method mainly comprises the following steps: three-dimensional environment abstract modeling and level set function initialization, level set function evolution, optimal route point selection, collaborative route scheme design, route conflict judgment and re-planning, and planning result output. Compared with the traditional level set algorithm, the collaborative route planning method provided by the patent considers the influence of ocean current and sound velocity factors, and adds the links of conflict judgment and re-planning, so that the collaborative route is safer; meanwhile, compared with two-dimensional route planning, the three-dimensional route planning has higher practicability and can better meet the actual navigation requirement.

Claims (4)

1. A multi-AUV three-dimensional collaborative route planning method is characterized by comprising the following steps:
step 1: performing three-dimensional environment modeling according to the electronic chart, the ocean current, the temperature, the salinity and the depth information, and establishing a terrain field, an ocean current field and a hidden field; initializing a level set function, initializing the level set function into a circular function with a starting point as a circle center and r as a radius, and setting a proper narrow band width d to enable the level set function to evolve in a narrow band;
step 2: level set function evolution: the level set function considers the AUV self speed, the ocean current speed and the hidden field to obtain an improved level set function evolution mathematical model, the evolution is carried out according to the improved level set function evolution mathematical model, when the narrow band edge is reached, the level set function and the narrow band width are reconstructed, the level set function is reinitialized into a new distance function, the new narrow band width is calculated, whether each terminal point is reached or not is judged, and all zero level set curves before the terminal point are stored when each terminal point is reached;
and step 3: selecting an optimal waypoint: carrying out forward iteration on the end point of each zero level set curve to find out the gradient direction point of each zero level set curve until the start point, and sequentially connecting all the gradient direction points to obtain all the optimal routes;
and 4, step 4: designing a collaborative route allocation scheme: simultaneously planning all m multiplied by m routes from m starting points to m end points by using the improved level set function evolution mathematical model obtained in the step 2, wherein n route combinations coexist in the planned m multiplied by m routes, wherein
Figure FDA0002418120030000011
M routes from n route combinations to m task points are selected, and all routes are set to H ═ H { (H)1,1,H1,2,…,H1,m；H2,1,H2,2,…,H2,m；…；Hn,1,Hn,2,…,Hn,mIn which H isn,mRepresenting navigation routes of m AUVs in the n schemes, so that H represents navigation routes of all AUVs in the n schemes;
AUV navigation energy consumption is set as:
Figure FDA0002418120030000012
where k is the coefficient of energy consumption, VaxRepresents the X-direction component in the speed of the AUV; vayRepresents the Y-direction component in the speed of the AUV; l isx＝|Lxi+1-Lxi| represents the route length of the X-direction component of the route; l isy＝|Lyi+1-Lyi| represents the route length of the Y-direction component of the route;
thus, an energy loss N is obtained that represents all the routes in each combination:
N＝{N1,1,N1,2,…,N1,m；N2,1,N2,2,…,N2,m；…；Nn,1,Nn,2,…,Nn,m}；
suppose that the selected route is Hi＝{Hi,1,Hi,2,…Hi,mTherein, the time of flight Ti＝{Ti,1,Ti,2,…Ti,m}, navigation energy consumption Ni＝{Ni,1,Ni,2,…Ni,mAnd the hiding degree of the air route, namely the sum of all gravitation degrees on the air route nodes, is as follows: mi＝{Mi,1,Mi,2,…Mi,m}, intersection D of air pathsi＝{Di,1,Di,2,…Di,m}；
Constraint (1):
Max{Ni}≤Nmax
constraint (1) indicates that: n is a radical ofmaxRepresenting the maximum energy carried by each AUV, namely the energy consumption of all the routes in the selected routes cannot exceed the maximum energy carried by the AUV;
constraint (2):
Figure FDA0002418120030000021
constraint (2) indicates that: the time of different AUVs reaching the intersection point of the routes cannot be the same, namely, no time conflict point exists between different routes;
constraint (3): each task can be allocated or executed only once, namely the task has uniqueness;
putting all routes into a m multiplied by m cellular array A, knowing that AUV can not reach the same task point according to the constraint condition (3), and combining n (wherein
Figure FDA0002418120030000022
) And (3) planning the collaborative air routes, and then calculating a time optimal matrix Ta ═ Ta of each scheme in n1,Ta2,…,TanAnd a concealment optimization matrix Ma ═ Ma1,Ma2,…,ManAnd calculating a cooperative objective function value C of each scheme by using a formula C, p, Ta + q, Ma, wherein p is a set time influence factor, q is a set hiding influence factor, and arranging all the routes from small to large according to the objective function valuesThe scheme is stored in the array S, wherein the route scheme corresponding to the minimum value of the objective function value is the optimal route scheme;
and 5: selecting an optimal route scheme from the array S to perform route conflict judgment, wherein the route conflict judgment comprises collision conflict judgment and energy conflict judgment, executing the step 6 when collision conflict or energy conflict exists, otherwise, outputting the current route scheme, and finishing the multi-AUV three-dimensional collaborative route planning;
wherein the collision conflict determination specifically comprises:
(1) all the routes HiEach route is represented by a line segment, and Hi,1Represented as all discrete segments, H, in the ith flight pathi,1＝(X1,1,X1,2,…,X1,n1) Wherein X is1,1Representing a segment of two adjacent discrete points in the route, n1 represents that the route is composed of n1 line segments, so that the route is re-represented as Hi＝{(X1,1,X1,2,…,X1,n1)，(X，2,1，,X2,2,…,X2,n2)…(Xm,1,Xm,2,…,Xm,nm)}；
(2) The linear function expression of a line segment composed of adjacent discrete points in an airway is known as y-kx + b, so the airway is re-expressed as Hi＝{(K1,1,K1,2,…,K1,n1),(K2,1,K2,2,…,K2,n2)，…，(Km,1,Km,2,…,Km,nm) In which K ism,nmRepresents Xm,nmA functional expression of this line segment;
(3) then comparing each line segment in each route with each line segment in other routes to obtain all intersection points in the range of the line segments, namely route intersection points Di＝{Di,1,Di,2,…Di,m}; calculating the time difference of the two AUVs reaching the intersection point, and if the time difference of the two AUVs reaching the intersection point is smaller than the set safe time difference, collision conflict exists;
the energy conflict judgment specifically comprises the following steps: setting navigation energy consumption Ni＝{Ni,1,Ni,2,…Ni,m}, judging the squareMaximum route energy consumption Max (N) in casei) Relationship with the nominal energy P carried by the AUV, when Max (N)i)>P, there is an energy conflict;
step 6: and (5) carrying out route re-planning, removing the route schemes with collision conflict or energy conflict from the array S, and executing the step 5.
2. The multi-AUV three-dimensional collaborative route planning method according to claim 1, characterized in that: the step 1 of carrying out three-dimensional environment modeling specifically comprises the following steps:
firstly, dividing a real marine environment space subjected to AUV route planning into lon x lat x depth grids, wherein the grid division principle is as follows: the grid spacing in the longitude direction and the latitude direction is consistent with the resolution of the grid water depth data file in the navigation area, and the grid spacing in the depth direction should not exceed 1/10 of the maximum depth value of the navigation area;
and (3) carrying out terrain field modeling of an AUV navigation area: the method is realized by reading a grid water depth data file of a navigation area, wherein an area below a water depth value read from the file in a navigation space is set as a no-navigation area, and AUV speed data, ocean current data and hidden field gravity data contained in a grid of the no-navigation area are set as 0 to be used for restricting the navigation depth of an AUV;
carrying out ocean current field modeling of an AUV navigation area: the method is realized by reading a grid ocean current data file of a navigation area, if the resolution of the ocean current data file is lower than that of a water depth data file, linear interpolation processing is carried out on the ocean current data file, otherwise, the ocean current data file is subjected to thinning processing until the resolution of the ocean current data file is the same as that of the water depth data file, so that each grid contains ocean current information of the current position; the size of the ocean current in the depth direction is set to 0;
hidden field modeling of an AUV navigation area is carried out: firstly, reading a grid temperature, salinity and depth data file of a navigation area, performing the same processing as a sea flow data file, then calculating sound velocity information c of different grid points of the navigation area according to the temperature, salinity and depth information in the AUV navigation area, knowing the position of a detected sonar, and calculating sound propagation loss under the influence of the sound velocity of the AUV navigation area, wherein the calculation of the sound propagation loss specifically comprises the following steps:
the acoustic propagation loss TL in the AUV navigation region is:
TL＝20log R+α·R
where R is the distance, α is the absorption coefficient,
Figure FDA0002418120030000031
β is the set sound speed influence coefficient;
the known high-quality factor of the detection sonar is FOM, and the navigation space is divided into an exposed area, a dangerous area and a hidden area according to the relationship between the sound propagation loss and the quality factor of the sonar, specifically:
when TL > FOM, the area is a hidden area;
when in use
Figure FDA0002418120030000032
When the area is the exposed area;
when in use
Figure FDA0002418120030000033
The area is a hazardous area;
the hidden field modeling of the navigation area is carried out by calculating the gravity value of the AUV by grid points in different areas of the navigation area, which specifically comprises the following steps:
in the navigation space, a hidden field is represented by a gravitational field, the gravitations of the divided hidden area, dangerous area and exposed area to the AUV are different in magnitude, the gravitation of the hidden area is set to be a fixed value F ═ F, wherein F is a factor influencing the AUV route planning algorithm by the concealment and is set to be 2 times of the maximum ocean current value of the navigation area, the gravitation of the exposed area is 0, and the gravitation of the dangerous area is set to be:
Figure FDA0002418120030000041
wherein R is the distance between the current position of AUV and sonar position, RTLTL is the distance of the FOM position.
3. The multi-AUV three-dimensional collaborative route planning method according to claim 1 or 2, characterized in that: step 1, the initialization of the level set function specifically includes:
setting a level set function as phi (X, t), wherein X represents a smooth closed curve, at the time of t, using an evolution curve X (t) to represent a zero level set curve phi (X, t) of the current time as 0, initializing the zero level set curve as a circle with a starting point as a circle center and r as a radius, setting d as a narrow band width, wherein d > r, the level set function is a distance function, a level set function value in the zero level set curve is a negative value, a level set function value outside the zero level set curve is a positive value, and in order to ensure that the zero level set curve can reach an end point, the level set function only evolves in a direction that the level set function value is a positive value, and the level set function is initialized as follows:
φ(X,t)＝0:(x-X1)2+(y-Y1)2+(z-Z1)2＝r2
wherein X, y, z are grid point coordinates, (X)1，Y1，Z1) As starting point coordinates.
4. The multi-AUV three-dimensional collaborative route planning method according to claim 1, characterized in that: the level set function evolution mathematical model in the step 2 is specifically as follows:
Figure FDA0002418120030000042
v (H, t) is ocean current, U is the speed of AUV, M is the size of a hidden field, and H represents a position vector in a navigation space;
Figure FDA0002418120030000043
as the direction of the navigation,
Figure FDA0002418120030000044
which is the direction of the attractive force.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112097773A (en) * 2020-08-26 2020-12-18 中国人民解放军军事科学院国防科技创新研究院 Collaborative route planning method and device for autonomous underwater vehicle formation

Citations (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003029922A2 (en) * 2001-10-01 2003-04-10 Kline & Walker, Llc Pfn/trac system faa upgrades for accountable remote and robotics control
US20070276600A1 (en) * 2006-03-06 2007-11-29 King Timothy I Intersection collision warning system
US20130325325A1 (en) * 2012-05-30 2013-12-05 Toyota Motor Engineering & Manufacturing North America System and method for hazard detection and sharing
WO2014200959A2 (en) * 2013-06-10 2014-12-18 Honda Motor Co., Ltd. Improved method and system for tank refilling using active fueling speed control
CN104777092A (en) * 2015-04-14 2015-07-15 电子科技大学 Printed circuit board service life assessment method based on salt-spray environment test
CN106503837A (en) * 2016-10-11 2017-03-15 哈尔滨工程大学 A kind of time optimal Route planner based on improvement level set algorithm
CN106970648A (en) * 2017-04-19 2017-07-21 北京航空航天大学 Unmanned plane multi-goal path plans combined method for searching under the environment of city low latitude
US20180120424A1 (en) * 2016-09-20 2018-05-03 Innoviz Technologies Ltd. Adaptive noise mitigation for different parts of the field of view
EP3344479A1 (en) * 2016-09-27 2018-07-11 Baidu USA LLC A vehicle position point forwarding method for autonomous vehicles
CN108680168A (en) * 2018-04-19 2018-10-19 哈尔滨工程大学 A kind of hidden paths planning method of underwater hiding-machine based on transparency and spring layer data
CN108829134A (en) * 2018-07-03 2018-11-16 中国船舶重工集团公司第七〇九研究所 A kind of real-time automatic obstacle avoiding method of deepwater robot
US20190196480A1 (en) * 2017-12-22 2019-06-27 X Development Llc Planning Robot Stopping Points to Avoid Collisions
AU2018208404A1 (en) * 2017-01-10 2019-07-25 Cavh Llc Connected automated vehicle highway systems and methods
CN110207709A (en) * 2019-06-25 2019-09-06 西安交通大学 Method for planning path for mobile robot based on parametrization level set
US20190333229A1 (en) * 2015-09-18 2019-10-31 Qualcomm Incorporated Systems and methods for non-obstacle area detection
KR20200010654A (en) * 2018-06-29 2020-01-31 현대엠엔소프트 주식회사 Apparatus for controlling autonomous vehicle and method thereof

Patent Citations (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003029922A2 (en) * 2001-10-01 2003-04-10 Kline & Walker, Llc Pfn/trac system faa upgrades for accountable remote and robotics control
US20070276600A1 (en) * 2006-03-06 2007-11-29 King Timothy I Intersection collision warning system
US20130325325A1 (en) * 2012-05-30 2013-12-05 Toyota Motor Engineering & Manufacturing North America System and method for hazard detection and sharing
WO2014200959A2 (en) * 2013-06-10 2014-12-18 Honda Motor Co., Ltd. Improved method and system for tank refilling using active fueling speed control
CN104777092A (en) * 2015-04-14 2015-07-15 电子科技大学 Printed circuit board service life assessment method based on salt-spray environment test
US20190333229A1 (en) * 2015-09-18 2019-10-31 Qualcomm Incorporated Systems and methods for non-obstacle area detection
US20180120424A1 (en) * 2016-09-20 2018-05-03 Innoviz Technologies Ltd. Adaptive noise mitigation for different parts of the field of view
EP3344479A1 (en) * 2016-09-27 2018-07-11 Baidu USA LLC A vehicle position point forwarding method for autonomous vehicles
CN106503837A (en) * 2016-10-11 2017-03-15 哈尔滨工程大学 A kind of time optimal Route planner based on improvement level set algorithm
AU2018208404A1 (en) * 2017-01-10 2019-07-25 Cavh Llc Connected automated vehicle highway systems and methods
CN106970648A (en) * 2017-04-19 2017-07-21 北京航空航天大学 Unmanned plane multi-goal path plans combined method for searching under the environment of city low latitude
US20190196480A1 (en) * 2017-12-22 2019-06-27 X Development Llc Planning Robot Stopping Points to Avoid Collisions
CN108680168A (en) * 2018-04-19 2018-10-19 哈尔滨工程大学 A kind of hidden paths planning method of underwater hiding-machine based on transparency and spring layer data
KR20200010654A (en) * 2018-06-29 2020-01-31 현대엠엔소프트 주식회사 Apparatus for controlling autonomous vehicle and method thereof
CN108829134A (en) * 2018-07-03 2018-11-16 中国船舶重工集团公司第七〇九研究所 A kind of real-time automatic obstacle avoiding method of deepwater robot
CN110207709A (en) * 2019-06-25 2019-09-06 西安交通大学 Method for planning path for mobile robot based on parametrization level set

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
MIRABITO, C.等: ""Autonomy for Surface Ship Interception"", 《OCEANS ABERDEEN CONFERENCE》 *
SHENG LIANG等: ""A Survey on Route Planning Methods of AUV Considering Influence of Ocean Current"", 《2018 IEEE 4TH INTERNATIONAL CONFERENCE ON CONTROL SCIENCE AND SYSTEMS ENGINEERING (ICCSSE)》 *
TAMAYO: ""A modular, visual simulator of underwater sensor networks."", 《UNIVERSITY OF RHODE ISLAND》 *
刘厂，等: ""基于改进水平集的AUV时间最优航路规划方法"", 《PROCEEDINGSOFTHE36THCHINESECONTROLCONFERENCE》 *
姚淑香: ""基于海流预测模型的AUV路径规划方法"", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 *
孙天龙: ""考虑海流影响的AUV航路规划方法研究"", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112097773A (en) * 2020-08-26 2020-12-18 中国人民解放军军事科学院国防科技创新研究院 Collaborative route planning method and device for autonomous underwater vehicle formation

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