CN111307159B - Multi-AUV three-dimensional collaborative route planning method - Google Patents
Multi-AUV three-dimensional collaborative route planning method Download PDFInfo
- Publication number
- CN111307159B CN111307159B CN202010197447.8A CN202010197447A CN111307159B CN 111307159 B CN111307159 B CN 111307159B CN 202010197447 A CN202010197447 A CN 202010197447A CN 111307159 B CN111307159 B CN 111307159B
- Authority
- CN
- China
- Prior art keywords
- route
- auv
- level set
- area
- navigation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
- G01C21/20—Instruments for performing navigational calculations
- G01C21/203—Specially adapted for sailing ships
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
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 initialization of a level set function, 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 requirement.
Description
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 executing tasks of a single AUV, but also improves the working efficiency, saves the cost of researching more complicated AUVs, and has the advantages of executing multiple collaborative tasks by multiple AUVs. In addition, the complex functions which can be realized by multiple AUVs are not available by a single AUV. Therefore, the research of multiple AUVs is increasingly receiving high attention from students worldwide.
A 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 and the like 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 an ocean environment, does not consider multiple AUVs to perform collaborative route research, and no published document applies the level set algorithm to a three-dimensional collaborative route planning research considering the influence of the ocean 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 plan a plurality of optimal routes simultaneously in a one-time iterative loop 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 speed of the AUV, the ocean current speed and the hidden field to obtain an improved level set function evolution mathematical model, evolves according to the improved level set function evolution mathematical model, reconstructs the level set function and the narrow band width when reaching the edge of the narrow band, reinitializes the level set function into a new distance function, calculates the new narrow band width, judges whether each terminal point is reached, and saves all zero level set curves before reaching the terminal point when each terminal point is reached;
and 3, 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
M routes from m AUVs to m task points need to be selected from n route combinations, and all routes are set to be H = { H = 1,1 ,H 1,2 ,…,H 1,m ；H 2,1 ,H 2,2 ,…,H 2,m ；…；H n,1 ,H n,2 ,…,H n,m In which H is n,m Representing 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:
where k is the coefficient of energy consumption, V ax Represents the X-direction component in the speed of the AUV; v ay Represents the Y-direction component in the speed of the AUV; l is x ＝|L xi+1 -L xi | represents the route length of the X-direction component of the route; l is y ＝|L yi+1 -L yi | 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＝{N 1,1 ,N 1,2 ,…,N 1,m ；N 2,1 ,N 2,2 ,…,N 2,m ；…；N n,1 ,N n,2 ,…,N n,m }；
suppose that the selected route is H i ＝{H i,1 ,H i,2 ,…H i,m Therein, the time of flight T i ＝{T i,1 ,T i,2 ,…T i,m }, navigation energy consumption N i ＝{N i,1 ,N i,2 ,…N i,m And (5) the hiding degree of the airway, namely the sum of all gravitation degrees on airway nodes, is: m i ＝{M i,1 ,M i,2 ,…M i,m }, intersection D of air paths i ＝{D i,1 ,D i,2 ,…D i,m }；
Constraint (1):
Max{N i }≤N max
constraint (1) indicates that: n is a radical of max Representing 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):
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;
all the routes are put into an m multiplied by m cellular array A, and according to the constraint condition (3), the AUV can not reach the same task point, so n (wherein n is combined)
) And (3) planning the collaborative air routes, and then calculating a time optimal matrix Ta = { Ta) of each scheme in n 1 ,Ta 2 ,…,Ta n } and concealment optimization matrix Ma = { Ma = 1 ,Ma 2 ,…,Ma n 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, 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, when collision conflict or energy conflict exists, executing the step 6, otherwise, outputting the current route scheme, and finishing multi-AUV three-dimensional collaborative route planning;
wherein, the collision conflict judgment specifically comprises the following steps:
(1) All the routes H i Each route is represented by a line segment, and H is i,1 Represented as all discrete segments, H, in the ith route i,1 ＝(X 1,1 ,X 1,2 ,…,X 1,n1 ) Wherein X is 1,1 Representing a line segment formed by two adjacent discrete points in the route, n1 represents that the route is formed by combining n1 line segments, so that the route is represented as H again i ＝{(X 1,1 ,X 1,2 ,…,X 1,n1 )，(X， 2,1 ，,X 2,2 ,…,X 2,n2 )…(X m,1 ,X m,2 ,…,X m,nm )}；
(2) The linear function expression of a segment made up of adjacent discrete points in the route is known as y = kx + b, so the route is re-represented as H i ＝{(K 1,1 ,K 1,2 ,…,K 1,n1 ),(K 2,1 ,K 2,2 ,…,K 2,n2 )，…，(K m,1 ,K m,2 ,…,K m,nm ) In which K is m,nm Represents X m,nm A 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 D i ＝{D i,1 ,D i,2 ,…D i,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 N i ＝{N i,1 ,N i,2 ,…N i,m Judge maximum route energy consumption Max (N) in the scheme i ) 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 a grid water depth data file in a 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, setting an area below a water depth value read from the file in a navigation space as a no-navigation area, and setting AUV speed data, ocean current data and hidden field gravitation data contained in a grid of the no-navigation area as 0 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 grid ocean current data files of a 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, thinning processing is carried out on the ocean current data files 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;
carrying out hidden field modeling of an AUV navigation area: firstly, reading a grid temperature, salinity and depth data file of a navigation area, performing the same processing as a sea current 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 the distance, alpha is the absorption coefficient,
beta is a set sound velocity 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
When the area is the exposed area;
when in use
The area is at riskA danger zone;
the hidden field modeling of the navigation area is carried out by calculating the gravity value of the AUV through 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 size, 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 as:
wherein R is the distance between the current position of AUV and the sonar position, R TL TL = FOM.
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) =0 at the current time, 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-X 1 ) 2 +(y-Y 1 ) 2 +(z-Z 1 ) 2 ＝r 2
where X, y, z are grid point coordinates, (X) 1 ，Y 1 ，Z 1 ) As starting point coordinates.
3. The level set function evolution mathematical model in the step 2 specifically comprises the following steps:
v (H, t) is ocean current, U is the speed of the AUV, M is the size of a hidden field, and H represents a position vector in a navigation space;
is the direction of the flight of the ship,
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 end point fastest, sonar detection can be avoided in route planning, and a collaborative route with optimal concealment is 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 3, 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 situation that the AUV navigation time and the concealment are optimally fused is considered in the collaborative navigation scheme. 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 replanning 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 mainly is the condition that a plurality of AUVs intersect at the same time in position and time, the energy problem mainly is that the energy carried by the AUVs cannot support the AUVs to reach target points, any one of the two conditions occurs, the cooperative combination is abandoned, the airway re-planning is carried out, the re-planning mainly comprises the step of selecting 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 (3) keeping the grid spacing in the longitude direction and the latitude direction consistent with the resolution of the grid water depth data file in the navigation area, wherein the grid spacing in the depth direction should not exceed 1/10 of the maximum depth value of the navigation area, and then modeling the terrain field, the ocean current field and the hidden field of the AUV navigation area.
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, rarefaction processing is carried out on the ocean current data files 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
wherein R is the distance, alpha is the absorption coefficient,
β is a sound velocity influence coefficient set to 0.5.
When TL > FOM, the area is a hidden area;
when in use
When the area is the exposed area;
when in use
The area is a hazard zone;
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 of influence of the concealment on the AUV route planning algorithm and is set to be 2 times of the maximum 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:
wherein R is the distance between the current position of AUV and sonar position, R TL TL = FOM.
The gravitation 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 navigation path; 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) =0 at the current time. Initializing the zero level set curve into a circle with the starting point as the center and r as the radius, and setting d as 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-X 1 ) 2 +(y-Y 1 ) 2 +(z-Z 1 ) 2 ＝r 2
where X, y, z are grid point coordinates, X 1 ，Y 1 ，Z 1 As the 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:
under the influence of ocean currents, the level set evolution function is:
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
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:
in addition, the sailing direction is
Comprehensively, the level set function evolution mathematical model is as follows:
the level set function evolves in a narrow band according to the equation, and an evolvement 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 ending 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 start 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
M routes for m AUVs to reach m task points need to be selected from n route combinations. Setting all routes to H = { H = { (H) 1,1 ,H 1,2 ,…,H 1,m ；H 2,1 ,H 2,2 ,…,H 2,m ；…；H n,1 ,H n,2 ,…,H n,m H wherein H n,m Represents 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 and the length of the navigation path, so the navigation energy consumption of the AUV is set as follows:
where k represents the coefficient of energy consumption, constant determined by the AUV's own parameters, V ax Represents the X-direction component in the speed of the AUV; v ay Represents the Y-direction component in the speed of the AUV; l is x ＝|L xi+1 -L xi L represents the route length of the X-direction component of the route; l is y ＝|L yi+1 -L yi | represents the route length of the Y-direction component of the route.
Thus, energy losses N are obtained that represent all the routes in each combination.
N＝{N 1,1 ,N 1,2 ,…,N 1,m ；N 2,1 ,N 2,2 ,…,N 2,m ；…；N n,1 ,N n,2 ,…,N n,m }。
Suppose that the selected route is H i ＝{H i,1 ,H i,2 ,…H i,m Therein, the time of flight T i ＝{T i,1 ,T i,2 ,…T i,m }, navigation energy consumption N i ＝{N i,1 ,N i,2 ,…N i,m Is M, the hiding magnitude of the airway (the sum of all gravitation magnitudes on the airway nodes) is i ＝{M i,1 ,M i,2 ,…M i,m }, intersection D of air paths i ＝{D i,1 ,D i,2 ,…D i,m }。
Constraint (1):
Max{N i }≤N max
constraint (1) indicates that: n is a radical of max The 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):
constraint (2) indicates that: the time for different AUVs to reach the intersection point of the routes cannot be the same, which indicates that no time conflict point exists between different routes [7] 。
Constraint condition (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, and knowing that the AUV cannot reach the phase according to the constraint condition (3)The same task point, thus combining n (where
) Planning schemes for the collaborative air routes, and then calculating a time optimal matrix Ta = { Ta) of each scheme in n 1 ,Ta 2 ,…,Ta n } and concealment optimization matrix Ma = { Ma = 1 ,Ma 2 ,…,Ma n And calculating a cooperative objective function value C of each scheme by using a formula C = p · Ta + q · Ma, wherein p is a time influence factor set to 0.4, q is a hiding influence factor set to 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 replanning 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 H i Each route is represented by a line segment, and H i,1 Represented as all discrete segments, H, in the ith flight path i,1 ＝(X 1,1 ,X 1,2 ,…,X 1,n1 ) Wherein X is 1,1 Representing a line segment formed by two adjacent discrete points in the route, n1 represents that the route is formed by combining n1 line segments, so that the route is represented as H again i ＝{(X 1,1 ,X 1,2 ,…,X 1,n1 )，(X， 2,1 ，,X 2,2 ,…,X 2,n2 )…(X m,1 ,X m,2 ,…,X m,nm )}。
(2) The linear function expression of a segment made up of adjacent discrete points in the route is known as y = kx + b, so the route is re-represented as H i ＝{(K 1,1 ,K 1,2 ,…,K 1,n1 ),(K 2,1 ,K 2,2 ,…,K 2,n2 )，…，(K m,1 ,K m,2 ,…,K m,nm ) In which K is 1,1 Represents X 1,1 A 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 point D i ＝{D i,1 ,D i,2 ,…D i,m }; in addition, let navigation consume N i ＝{N i,1 ,N i,2 ,…N i,m The maximum route energy consumption Max (N) in the scheme only needs to be judged i ) 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 initialization of a level set function, 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 (3)
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; the 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 a grid water depth data file in a 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;
carrying out hidden field modeling of an AUV navigation area: firstly, reading a grid temperature, salinity and depth data file of a navigation area, performing the same processing as a sea current 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
wherein R is the distance, alpha is the absorption coefficient,
beta is a set sound velocity influence coefficient;
the known high-quality factor of detection sonar is FOM, and a navigation space is divided into an exposed area, a dangerous area and a hidden area according to the relationship between sound propagation loss and sonar quality factor, and the method specifically comprises the following steps:
when TL > FOM, the area is a hidden area;
when the temperature is higher than the set temperature
When the area is the exposed area;
when in use
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 through 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 size, 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 as:
wherein R is the distance between the current position of AUV and sonar position, R TL Distance at TL = FOM;
and 2, step: evolution of a level set function: 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 end point is reached, and sequentially connecting all the gradient direction points to obtain all the optimal air 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 evolutionary mathematical model obtained in the step 2, wherein n route combinations coexist in the planned m multiplied by m routes, wherein
M routes from m AUVs to m task points need to be selected from n route combinations, and all routes are set to be H = { H = 1,1 ,H 1,2 ,…,H 1,m ；H 2,1 ,H 2,2 ,…,H 2,m ；…；H n,1 ,H n,2 ,…,H n,m H wherein H n,m Representing 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:
where k is the coefficient of energy consumption, V ax Represents the X-direction component in the speed of the AUV; v ay Represents the Y-direction component in the speed of the AUV; l is x ＝|L xi+1 -L xi L represents the route length of the X-direction component of the route; l is y ＝|L yi+1 -L yi | 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＝{N 1,1 ,N 1,2 ,…,N 1,m ；N 2,1 ,N 2,2 ,…,N 2,m ；…；N n,1 ,N n,2 ,…,N n,m }；
suppose that the selected route is H i ＝{H i,1 ,H i,2 ,…H i,m Therein, the time of flight T i ＝{T i,1 ,T i,2 ,…T i,m }, navigation energy consumption N i ＝{N i,1 ,N i,2 ,…N i,m And the hiding degree of the air route, namely the sum of all gravitation degrees on the air route nodes, is as follows: m is a group of i ＝{M i,1 ,M i,2 ,…M i,m }, intersection D of air paths i ＝{D i,1 ,D i,2 ,…D i,m }；
Constraint (1):
Max{N i }≤N max
constraint (1) indicates that: n is a radical of max Representing 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):
constraint (2) states 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 condition (3): each task can be distributed or executed only once, namely the task has uniqueness;
putting all routes into an m multiplied by m cellular array A, knowing that the AUV can not reach the same task point according to the constraint condition (3), and combining n collaborative route planning schemes, wherein
Then, a time optimal matrix Ta = { Ta ] of each scheme in n is calculated 1 ,Ta 2 ,…,Ta n And a concealment optimization matrix Ma = { Ma = } 1 ,Ma 2 ,…,Ma n 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, 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 H i Each route is represented by a line segment, and H i,1 Represented as all discrete segments, H, in the ith flight path i,1 ＝(X 1,1 ,X 1,2 ,…,X 1,n1 ) Wherein X is 1,1 Representing a line segment formed by two adjacent discrete points in the route, n1 represents that the route is formed by combining n1 line segments, so that the route is represented as H again i ＝{(X 1,1 ,X 1,2 ,…,X 1,n1 )，(X 2,1 ,X 2,2 ,…,X 2,n2 )，…，(X m,1 ,X m,2 ,…,X m,nm )}；
(2) The linear function expression of a segment made up of adjacent discrete points in the route is known as y = kx + b, so the route is re-represented as H i ＝{(K 1,1 ,K 1,2 ,…,K 1,n1 ),(K 2,1 ,K 2,2 ,…,K 2,n2 )，…，(K m,1 ,K m,2 ,…,K m,nm ) In which K is m,nm Represents X m,nm A 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 D i ＝{D i,1 ,D i,2 ,…D i,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: energy consumption N for navigation i ＝{N i,1 ,N i,2 ,…N i,m Judge maximum route energy consumption Max (N) in the scheme i ) Relationship to the nominal energy P carried by the AUV, when Max (N) i )>P, there is an energy conflict;
and 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 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) =0 at the current time, initializing the zero level set curve as a circle taking a starting point as a circle center and r as a radius, setting d as a narrow band width, wherein d is larger than 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 towards the 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-X 1 ) 2 +(y-Y 1 ) 2 +(z-Z 1 ) 2 ＝r 2
where X, y, z are grid point coordinates, (X) 1 ，Y 1 ，Z 1 ) As the starting point coordinates.
3. 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:
v (H, t) is ocean current, U is the speed of the AUV, M is the size of a hidden field, and H represents a position vector in a navigation space;
is the direction of the flight of the ship,
which is the direction of the attractive force.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010197447.8A CN111307159B (en) | 2020-03-19 | 2020-03-19 | Multi-AUV three-dimensional collaborative route planning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010197447.8A CN111307159B (en) | 2020-03-19 | 2020-03-19 | Multi-AUV three-dimensional collaborative route planning method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111307159A CN111307159A (en) | 2020-06-19 |
| CN111307159B true CN111307159B (en) | 2022-11-18 |
Family
ID=71157398
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010197447.8A Active CN111307159B (en) | 2020-03-19 | 2020-03-19 | Multi-AUV three-dimensional collaborative route planning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111307159B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112097773B (en) * | 2020-08-26 | 2022-09-02 | 中国人民解放军军事科学院国防科技创新研究院 | Collaborative route planning device for autonomous underwater vehicle formation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014200959A2 (en) * | 2013-06-10 | 2014-12-18 | Honda Motor Co., Ltd. | Improved method and system for tank refilling using active fueling speed control |
| 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 |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6965816B2 (en) * | 2001-10-01 | 2005-11-15 | Kline & Walker, Llc | PFN/TRAC system FAA upgrades for accountable remote and robotics control to stop the unauthorized use of aircraft and to improve equipment management and public safety in transportation |
| US20070276600A1 (en) * | 2006-03-06 | 2007-11-29 | King Timothy I | Intersection collision warning system |
| US9031779B2 (en) * | 2012-05-30 | 2015-05-12 | Toyota Motor Engineering & Manufacturing North America, Inc. | System and method for hazard detection and sharing |
| 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 |
| US9761000B2 (en) * | 2015-09-18 | 2017-09-12 | Qualcomm Incorporated | Systems and methods for non-obstacle area detection |
| EP4194888A1 (en) * | 2016-09-20 | 2023-06-14 | Innoviz Technologies Ltd. | Lidar systems and methods |
| US10191493B2 (en) * | 2016-09-27 | 2019-01-29 | Baidu Usa Llc | Vehicle position point forwarding method for autonomous vehicles |
| CN106503837B (en) * | 2016-10-11 | 2019-09-27 | 哈尔滨工程大学 | A kind of time optimal Route planner based on improvement level set algorithm |
| KR102386960B1 (en) * | 2017-01-10 | 2022-04-15 | 씨에이브이에이치 엘엘씨 | Connected Automated Vehicle Road Systems and Methods |
| CN106970648B (en) * | 2017-04-19 | 2019-05-14 | 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 |
| US10466707B2 (en) * | 2017-12-22 | 2019-11-05 | 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 |
| CN110207709B (en) * | 2019-06-25 | 2021-04-20 | 西安交通大学 | Mobile robot path planning method based on parameterized level set |
-
2020
- 2020-03-19 CN CN202010197447.8A patent/CN111307159B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014200959A2 (en) * | 2013-06-10 | 2014-12-18 | Honda Motor Co., Ltd. | Improved method and system for tank refilling using active fueling speed control |
| 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 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111307159A (en) | 2020-06-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106845716B (en) | Navigation error constraint-based water surface unmanned ship local hierarchical path planning method | |
| Zeng et al. | A survey on path planning for persistent autonomy of autonomous underwater vehicles | |
| CN108871351B (en) | Dynamic path planning method for AUV (autonomous Underwater vehicle) submarine topography matching | |
| CN107356254B (en) | Particle swarm optimization method suitable for geomagnetic-assisted navigation track planning | |
| CN106503837B (en) | A kind of time optimal Route planner based on improvement level set algorithm | |
| CN103744428A (en) | Unmanned surface vehicle path planning method based on neighborhood intelligent water drop algorithm | |
| CN111123923B (en) | Unmanned ship local path dynamic optimization method | |
| CN113342008B (en) | Path planning system and method for sea-air cooperative underwater target tracking | |
| Hollinger et al. | Learning uncertainty models for reliable operation of autonomous underwater vehicles | |
| Tsou | Integration of a geographic information system and evolutionary computation for automatic routing in coastal navigation | |
| CN112612290A (en) | Underwater vehicle three-dimensional multi-task path planning method considering ocean currents | |
| CN115143970B (en) | Obstacle avoidance method and system of underwater vehicle based on threat degree evaluation | |
| CN111412918B (en) | Unmanned ship global safety path planning method | |
| CN111307159B (en) | Multi-AUV three-dimensional collaborative route planning method | |
| Yu et al. | Multi-objective AUV path planning in large complex battlefield environments | |
| Guo et al. | Mission-driven path planning and design of submersible unmanned ship with multiple navigation states | |
| Cheng et al. | Trajectory optimization for ship navigation safety using genetic annealing algorithm | |
| CN116243717A (en) | Underwater robot time optimal path planning method considering ocean current uncertainty | |
| CN115202356A (en) | Three-dimensional underwater under-actuated AUV (autonomous underwater vehicle) recovery path planning method | |
| CN115493591A (en) | Multi-route planning method | |
| JP2022123401A (en) | Route planning device, moving object, route planning method and program | |
| Ånonsen et al. | Terrain aided underwater navigation using pockmarks | |
| CN116839594B (en) | Submarine global track planning method and device based on optimized bidirectional A-Star algorithm | |
| Gao et al. | Artificial intelligence algorithms in unmanned surface vessel task assignment and path planning: A survey | |
| CN117830571A (en) | Multi-constraint optimized UUV particle swarm three-dimensional path planning method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |