Ship formation path planning method, device and system

Abstract

The invention relates to the technical field of ships, and provides a ship formation path planning method, device and system. The method comprises the steps of establishing a formation path planning model; wherein the formation path planning model comprises a target function and a constraint function; solving the formation path planning model to obtain a planning path of each formation; establishing a ship dynamics model; and according to the planned path of each formation, combined with the ship dynamic model, calculating to obtain a driving force required by the ship, so that each ship in the formation advances along the planned path under the action of the driving force. According to the method, the multiple formations are modeled and solved, so that path planning can be carried out on the multiple formations, and the time constraint is introduced during modeling, so that the multiple formations can execute tasks together.

Description

Technical Field

[0001] The present invention relates to the field of ship technology, and in particular to a ship formation path planning method, device and system. Background Art

[0002] As unmanned ships are small and easy to use, they can play a significant role in various fields. Path planning for unmanned ships has become a research hotspot in recent years. As an essential function for autonomous navigation of unmanned ships, path planning can plan obstacle-avoiding and collision-free paths from the starting point to the end point for unmanned ships. It can also provide relevant local path planning when unmanned ships encounter obstacles during navigation to achieve dynamic collision avoidance. Therefore, path planning technology for unmanned ships is the basic guarantee for unmanned ship navigation.

[0003] In the prior art, a single unmanned ship is often used to perform the corresponding task. However, in actual use, the path planning of a single ship is prone to limitations such as limited working range and inability to continue the task in the event of a failure. In view of this, overcoming the defects of the prior art is an urgent problem to be solved in the field of this technology. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a ship formation path planning method.

[0005] The present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for ship formation path planning, including n formations, the method comprising:

[0007] Establish a formation path planning model; wherein the formation path planning model includes an objective function and a constraint function, and the objective function is minP=P safe (w L L+w E E+w J J+w T T), the constraint function is Among them, P safe is the obstacle avoidance cost of the ship, d is the distance between the ship and the obstacle, safe is the preset safety distance; L is the sailing length of the ship, L max is the preset maximum length; E is the track energy consumption; J is the turning cost; T is the total time difference between the formations, T max is the preset maximum time difference; w L represents the path constraint weight, w E represents the energy constraint weight, w J represents the rotation constraint weight, w T represents the time constraint weight; v is the speed of the ship, vmin is the preset minimum speed, v max is the preset maximum speed;

[0008] Solving the formation path planning model to obtain a planned path for each formation;

[0009] Establish the ship dynamics model Mv+C(v)v+D(v)v=τ w +τ thr ; where τ w =τ wind +τ wave +τ curren t, τ wind represents the force of wind on the ship, τ wave represents the force of waves on the ship, τ current represents the force of the flow on the ship, τ thr represents the ship's own propulsion force, v is the velocity vector of the ship acting on the environment and the propulsion system, M is the inertia matrix that measures the difficulty of the ship's state change during navigation, C(v) is the centripetal force matrix; D(v) is the damping matrix, expressed as D(v)∈R 2×2 ;

[0010] According to the planned path of each formation, combined with the ship dynamics model, the propulsion force required by the ship is calculated, so that each ship in the formation moves along the planned path under the action of the propulsion force.

[0011] Preferably, the wind force on the ship is wind is the first force τ windx and the second force τ windy The combined force of

[0012] Among them, the first force τ windx is the force acting on the ship in the first direction from the wind, The second force τ windy is the force exerted on the ship in the second direction from the wind, ρ a Represents the air density, A f is the orthographic projection area of ​​the ship above the waterline, A s is the side projection area of ​​the ship above the waterline, C wx (α R ) represents the wind pressure coefficient in the first direction, C wy (α R ) represents the wind pressure coefficient in the second direction, V w Represents wind speed.

[0013] Preferably, the force τ exerted by waves on the ship is wave is the third force τ wavexand the fourth force τ wavey The third force τ wavex is the force of the waves on the ship in the first direction, The fourth force τ wavey is the force of the waves on the ship in the second direction, ρ is the density of seawater; ζ D is the average amplitude during the voyage, L is the length of the ship; χ is the encounter angle during the voyage; C XD (λ) is the wave drift coefficient in the first direction, C YD (λ) is the wave drift force coefficient in the second direction.

[0014] Preferably, the force τ exerted by the flow on the ship is current is the fifth force τ currentx and the sixth force τ currenty The fifth force τ currentx is the force exerted on the ship by upstream in the first direction, The sixth force τ cuurenty is the force exerted on the ship by the upstream in the second direction, A f is the orthographic projection area above the waterline of the ship, As is the lateral projection area above the waterline of the ship, V c and β are the velocity and encounter angle of the flow relative to the ship, C X (β) is the wave force coefficient in the first direction, C Y (β) is the wave force coefficient in the second direction.

[0015] Preferably, the total time difference between formations Where, ΔT i =T k,max -T i，min , T k,max It is the time it takes for the longest formation to reach the target point.

[0016] Preferably, solving the formation path planning model to obtain the planned path of each formation specifically includes:

[0017] Perform multiple iterations until the number of iterations reaches a preset number, and determine the path of the ship based on the positions obtained in the last iteration;

[0018] In each iteration, it is determined whether the preset condition is met; wherein the preset condition is p <rand(0,1);

[0019] If the preset condition is not met, the first predator-prey model is used to determine the next position; wherein the first predator-prey model is

[0020] If the preset conditions are met, then Is it greater than or equal to 1? is greater than or equal to 1, a random search model is used to determine the next position; wherein the random search model is

[0021] like If it is less than 1, the second predator-prey model is used to determine the next position; wherein the predator-prey model is

[0022] In the first predator-prey model and the second predator-prey model, t is the current number of iterations, b is a constant that defines the shape of the logarithmic rejection, is the position vector of the global optimal solution, is the position vector of the current solution, and is the coefficient vector,

[0023] Preferably, solving the formation path planning model to obtain the planned path of each formation also includes: in each iteration, updating the individual, specifically including:

[0024] Generate test vectors Update the individual using the test vector Among them, i, j, and t represent the individual subscript, dimension subscript, and iteration number respectively; among them, CR∈(0,1), x i,j,t+1 is the crossover rate, x i,j, t is the position of the individual in the previous iteration.

[0025] In a second aspect, the present invention further provides a ship formation path planning device, which is used to implement the ship formation path planning method described in the first aspect, and the device includes:

[0026] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the ship formation path planning method described in the first aspect.

[0027] In a third aspect, the present invention further provides a non-volatile computer storage medium, wherein the computer storage medium stores computer executable instructions, which are executed by one or more processors to complete the ship formation path planning method described in the first aspect.

[0028] In a fourth aspect, the present invention further provides a ship formation path planning system, comprising a modeling module and a calculation module;

[0029] The modeling module is used to establish a formation path planning model; wherein the formation path planning model includes an objective function and a constraint function, and the objective function is minP=P safe (w L L+w E E+w J J+w T T), the constraint function is Among them, P safe is the obstacle avoidance cost of the ship, d is the distance between the ship and the obstacle, safe is the preset safety distance; L is the sailing length of the ship, L max is the preset maximum length; T is the total time difference between formations, T max is the preset maximum time difference; w L represents the path constraint weight, w E represents the energy constraint weight, w J represents the rotation constraint weight, w T represents the time constraint weight; v is the speed of the ship, v min is the preset minimum speed, v max is the preset maximum speed;

[0030] The calculation module is used to solve the formation path planning model to obtain the planned path of each formation;

[0031] The modeling module is also used to establish a ship dynamics model Mv+C(v)v+D(v)v=τ w +τ thr ; where τ w =τ wind +τ wave +τ current , τ wind represents the force of wind on the ship, τ wave represents the force of waves on the ship, τ current represents the force of the flow on the ship, τ thr represents the ship's own propulsion force, v is the velocity vector of the ship acting on the environment and the propulsion system, M is the inertia matrix that measures the difficulty of the ship's state change during navigation, C(v) is the centripetal force matrix; D(v) is the damping matrix, expressed as D(v)∈R 2×2 ;

[0032] The calculation module is also used to calculate the propulsion force required for the ship according to the planned path of each formation in combination with the ship dynamics model, so that each ship in the formation moves along the planned path under the action of the propulsion force.

[0033] The present invention performs path planning for multiple formations by modeling and solving multiple formations, and introduces time constraints during modeling, making it possible for multiple formations to perform tasks together. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 It is a flow chart of a method for ship formation path planning provided by an embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of a first ship formation path planning method provided by an embodiment of the present invention;

[0037] Figure 3 It is a flowchart of another method for ship formation path planning provided by an embodiment of the present invention;

[0038] Figure 4 It is a scene schematic diagram of a ship formation path planning method provided by an embodiment of the present invention;

[0039] Figure 5 It is a flow chart of the analytic hierarchy process in a method for ship formation path planning provided by an embodiment of the present invention;

[0040] Figure 6 is a schematic diagram of a second ship formation path planning method provided by an embodiment of the present invention;

[0041] Figure 7 is a schematic diagram of a third ship formation path planning method provided by an embodiment of the present invention;

[0042] Figure 8 is a schematic diagram of a fourth ship formation path planning method provided by an embodiment of the present invention;

[0043] Fig. 9 is a schematic diagram of a fifth ship formation path planning method provided by an embodiment of the present invention;

[0044] Fig.10 is a schematic diagram of a sixth ship formation path planning method provided by an embodiment of the present invention;

[0045] Fig.11 is a schematic diagram of a seventh ship formation path planning method provided by an embodiment of the present invention;

[0046] Fig.12 It is a schematic diagram of the architecture of a ship formation path planning system provided by an embodiment of the present invention;

[0047] Fig.13 It is a schematic diagram of the architecture of another device for planning a path for a ship formation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] The terms "first", "second", etc. in the present invention are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0050] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0051] Embodiment 1:

[0052] In the prior art, a single unmanned ship is often used to perform a corresponding task. However, in actual use, the path planning of a single unmanned ship is prone to limitations such as limited working range and inability to continue the task if a failure occurs. In order to solve this problem, Example 1 of the present invention provides a method for using a ship formation to perform a task, including n formations, each formation including one or more ships, and the n formations jointly perform corresponding tasks, thereby expanding the working range and improving the success rate of the task. However, there is no path planning method for multiple formations in the prior art. In order to solve this problem, Example 1 of the present invention provides a path planning method for a ship formation, such as Figure 1 As shown, the method includes:

[0053] In step 201, a formation path planning model is established; wherein the formation path planning model includes an objective function and a constraint function, and the objective function is minP=P safe (w L L+w E E+w J J+w T T), the constraint function is Among them, P safe is the obstacle avoidance cost of the ship, d is the distance between the ship and the obstacle,safe is the preset safety distance; L is the sailing length of the ship, L i,i+1 is the length of each driving section, m represents the number of track segments (i.e. driving sections), L max is the preset maximum length; E is the track energy consumption; E i,i+1 is the work done by the ship itself to overcome the environmental interference resistance during navigation; J is the turning cost, α represents the real-time horizontal turning angle of the ship, and α max is the maximum horizontal turning angle limit of the ship; T is the total time difference between the formations, the total time difference between the formations T max is the preset maximum time difference, ΔT i =T k,max -T i，min , T k,max is the time it takes for the longest formation to reach the target point; w L represents the path constraint weight, w E represents the energy constraint weight, w J represents the rotation constraint weight, w T represents the time constraint weight; v is the speed of the ship, which is usually the average speed between the node i and the node i+1 of the section, v min is the preset minimum speed, v max is the preset maximum speed; the preset safety distance, preset maximum length, preset maximum time difference, preset minimum speed, and preset maximum speed are all obtained by those skilled in the art based on empirical analysis. The path constraint weight, energy constraint weight, corner constraint weight, and time constraint weight are obtained by those skilled in the art using the hierarchical analysis method to satisfy w L +w E +w J =1 constraint.

[0054] Since multiple formations perform the same task, they should arrive at the destination within the same time range. However, different formations may have different sailing speeds and different departure times. Therefore, in order to ensure that each formation can arrive within the same time range, the above T≤T is proposed. max The time constraint condition is, let the speed of each formation be [v min ,v max ], a path of length L1 is planned for formation 1. The time interval to reach the target point is The shortest time to arrive and the longest time Then we get Figure 2The time coordination strategy shown in the figure sorts the formations according to the sailing time. The formation with the longest sailing time sets off first. After T1 time, the formation with the second longest sailing time sets off, and so on until the formation with the shortest sailing time sets off. All formations arrive at the target point at the same time. Through this time coordination, multiple formations can reach the designated target point at the same time in the shortest time. In this process, the formation with the longest sailing time is recorded as k, and the longest time required for it to reach the target point is T k,max , then the time difference between each formation i and formation k to reach the target point is calculated as ΔT i =T k,max -T i，min , indicating that formation i must be at T k,max Depart before T k,max When , the kth formation can reach the target point at the same time. Therefore, the total time difference T between the formations is calculated, which is equal to the sum of the absolute values ​​of the time differences between all formations. The value of T can determine the departure interval between formations. If the departure interval is too large, it means that the time coordination effect between formations is poor. Therefore, it is hoped that the T value is as small as possible to strengthen the time coupling between formations. Under ideal conditions, T max =0, that is, all formations arrive at the target location at the same time.

[0055] In step 202, the formation path planning model is solved to obtain the planned path of each formation; wherein the solving method includes ant algorithm, particle swarm algorithm, whale algorithm and the like.

[0056] In step 203, a ship dynamics model Mv+C(v)v+D(v)v=τ is established. w +τ thr ; where τ w =τ wind +τ wave +τ current , τ wind represents the force of wind on the ship, τ wave represents the force of waves on the ship, τ current represents the force of the flow on the ship, τ t hr represents the ship's own propulsion force, v is the velocity vector of the ship acting on the environment and the propulsion system, M is the inertia matrix that measures the difficulty of the ship's state change during navigation, C(v) is the centripetal force matrix; D(v) is the damping matrix, expressed as D(v)∈R 2×2 .

[0057] In step 204, the propulsion force required by the ship is calculated based on the planned path of each formation and combined with the ship dynamics model, so that each ship in the formation moves along the planned path under the action of the propulsion force. That is, the required velocity vector v is first calculated according to the planned path, and then the velocity vector v is substituted into the ship dynamics model to calculate the propulsion force of the ship itself. Among them, the ship is a general term for water-based mobile tools, including one or more of ships, boats, and ships. The said making each ship in the formation move along the planned path under the action of the propulsion force actually makes all ships in the formation move along the planned path under the action of the propulsion force.

[0058] In actual use, when a formation contains only one ship, the position of the ship is used as the position of the formation for formation path planning. When a formation contains multiple ships, the position of one of the ships in the formation can be selected as the formation position for formation path planning. Considering that in actual application scenarios, ships in the formation may also be abnormal, causing some transmissions to be out of the formation, in order to avoid the formation falling into chaos, this embodiment also provides a preferred implementation method, that is, selecting the mathematical center of the formation as the formation position for formation path planning.

[0059] This embodiment models and solves multiple formations, thereby enabling path planning for multiple formations. In addition, this embodiment also introduces time constraints during modeling, making it possible for multiple formations to perform tasks together.

[0060] Among them, the wind force on the ship is τ wind is the first force τ windx and the second force τ windy The combined force.

[0061] Among them, the first force τ windx is the force acting on the ship in the first direction from the wind, The second force τ windy is the force exerted on the ship in the second direction from the wind, ρ a Represents the air density, A f is the orthographic projection area of ​​the ship above the waterline, A s is the side projection area of ​​the ship above the waterline, C wx (α R ) represents the wind pressure coefficient in the first direction, C wy (α R ) represents the wind pressure coefficient in the second direction, V w Represents wind speed.

[0062] In actual use, the first direction is horizontal, and the second direction is vertical.

[0063] Wave force on the shipτ wave is the third force τ wavex and the fourth force τ wavey The third force τ wavex is the force of the waves on the ship in the first direction, The fourth force τ wavey is the force of the waves on the ship in the second direction, ρ is the density of seawater; ζ D is the average amplitude during the voyage, L is the length of the ship; χ is the encounter angle during the voyage; C XD (λ) is the wave drift coefficient in the first direction, C YD (λ) is the wave drift force coefficient in the second direction.

[0064] The force of the current on the ship τ current is the fifth force τ currentx and the sixth force τ currenty The fifth force τ currentx is the force exerted on the ship by upstream in the first direction, The sixth force τ cuurenty is the force exerted on the ship by the upstream in the second direction, A f is the orthographic projection area above the waterline of the ship, As is the lateral projection area above the waterline of the ship, V c and β are the velocity and encounter angle of the flow relative to the ship, C X (β) is the wave force coefficient in the first direction, C Y (β) is the wave force coefficient in the second direction.

[0065] When solving the formation path planning model using the existing technology, it is found that there are problems such as slow convergence speed, poor global optimization ability, and low convergence accuracy. In order to solve this problem, this embodiment also provides a preferred implementation method, that is, solving the formation path planning model to obtain the planned path of each formation, such as Figure 3 As shown, specifically including:

[0066] In step 301, multiple iterations are performed until the number of iterations reaches a preset number, and the path of the ship is determined based on the positions obtained in the last iteration; the preset number is obtained by those skilled in the art based on empirical analysis.

[0067] In step 302, in each iteration, it is determined whether a preset condition is satisfied; wherein the preset condition is p <rand(0,1)。

[0068] In step 303, if the preset condition is not met, the first predator-prey model is used to determine the next position; wherein the first predator-prey model is

[0069] In step 304, if the preset condition is met, it is determined Is it greater than or equal to 1? is greater than or equal to 1, a random search model is used to determine the next position; wherein the random search model is

[0070] In step 305, if If it is less than 1, the second predator-prey model is used to determine the next position; wherein the predator-prey model is

[0071] In the first predator-prey model and the second predator-prey model, t is the current number of iterations, b is a constant that defines the shape of the logarithmic rejection, is the position vector of the global optimal solution, is the position vector of the current solution, and is the coefficient vector,

[0072] The above preferred implementation can be understood as an improvement on the existing whale algorithm. Specifically, there are three positioning methods of the whale algorithm in the prior art. The first method is shrinking and surrounding, the second method is random search, and the third method is spiral update. The models corresponding to shrinking and surrounding and spiral update in the prior art are: Among them, when p>rand(0,1), the spiral update is performed, that is, when the spiral update In this embodiment, the sine is introduced into the spiral update formula. As interference terms and auxiliary terms, we get the formula This improves the convergence accuracy.

[0073] In actual use, it is also found that the late iteration of solving the formation path planning model is prone to fall into the local optimum, resulting in the inability to find the global optimal solution. In order to solve this problem, this embodiment also provides a preferred implementation method, that is, solving the formation path planning model to obtain the planned path of each formation, further comprising: in each iteration, the individual is also updated, specifically including:

[0074] Generate test vectors Update the individual using the test vector Among them, i, j, and t represent the individual subscript, dimension subscript, and iteration number respectively; among them, CR∈(0,1), x i,j,t+1is the crossover rate, x i,j,t is the position of the individual in the previous iteration.

[0075] The above implementation can be understood as introducing the crossover and selection operations of the Differential Evolution Algorithm (DE) into the whale algorithm. This can give full play to the advantages of the global optimization ability of the traditional whale algorithm, and can also actively adapt to the coupling constraints and protect the dominant individuals. By analyzing the solution space of the traditional algorithm, it can be found that the crossover operation in DE can reduce the possibility of pushing individuals into infeasible areas, making the search frequency higher in each iteration, thereby making the search results diverse, increasing the chances of searching in other areas, and avoiding falling into local optimality.

[0076] When solving the formation path planning model, the minimum value P of the comprehensive function and the corresponding length cost, turning cost, energy consumption cost and coordination cost can be obtained. At the same time, the position coordinates of each step are calculated, and these coordinates are smoothed and connected to obtain the planned path of each formation.

[0077] Embodiment 2:

[0078] The present invention is based on the method described in Example 1, combined with specific application scenarios, and uses technical descriptions in related scenarios to illustrate the implementation process of the present invention in characteristic scenarios.

[0079] For example, there are multiple formations, each of which includes multiple unmanned boats (i.e., ships in Example 1). Figure 4 The water area shown is a two-dimensional square water area of ​​40×40km for formation path planning. When planning the formation path, the following conditions must be met:

[0080] 1) Obstacles and threat areas can be regarded as various irregular obstacles composed of square areas.

[0081] 2) When making global planning for the unmanned boat, we should first ensure that there is no collision between the unmanned boat and obstacles, and then consider whether its planned path can achieve the global optimum.

[0082] Figure 4 The black blocks in the figure represent obstacles. The ship formation path planning method provided in this embodiment includes two parts: model establishment and model solving.

[0083] The process of building the model includes:

[0084] For the path planning modeling of a single boat, the obstacle avoidance factor is considered first, and the obstacle avoidance cost function of the unmanned boat is established. d is the distance between the unmanned boat and the obstacle, and d safeIt is the pre-set safe distance between the unmanned boat and obstacles.

[0085] Set the flight length Can be viewed i,i+1 is the length of each driving segment, and m represents the number of track segments.

[0086] Set corner cost Where α represents the real-time horizontal turning angle of the unmanned boat, and α max It is the maximum horizontal turning angle limit of the unmanned boat.

[0087] Setting track energy consumption v is the average speed of the unmanned boat between nodes i and i+1, E i,i+1 It is the work done by the unmanned boat to overcome the environmental interference resistance during navigation.

[0088] Determine the weight w according to the analytic hierarchy process L 、w E 、w J , thereby establishing the comprehensive cost of path planning for a single boat

[0089] minP=P safe (w L L+w E E+w J J)

[0090] function

[0091] Among them, the analytic hierarchy process Figure 5 As shown, including:

[0092] In step 401, a hierarchical structure of a reasonable system is established.

[0093] In step 402, a judgment matrix is ​​constructed and assigned values.

[0094] In step 403, consistency check is performed.

[0095] Among them, w L 、w E 、w J The weights representing path length, energy consumption, and corner cost are obtained by the analytic hierarchy process to satisfy w L +w E +w J = 1, and ensure that the solution of the objective function must be carried out under the premise of ensuring obstacle avoidance to ensure the safe navigation of the unmanned boat. At the same time, it should be satisfied that the length of the unmanned boat’s driving path L cannot be greater than the maximum length L in the driving sea area. max , the speed v of the unmanned boat during driving shall not be less than its own minimum speed v min(i.e. the preset minimum speed), nor can it be greater than its own maximum speed v max (i.e. preset maximum speed).

[0096] Establish the single boat dynamics model Mv+C(v)v+D(v)v=τ w +τ thr (i.e., ship dynamics model) and τ w =τ wind +τ wave +τ current M is the inertia matrix that measures the difficulty of the state change of the unmanned boat during navigation; C(v) is the centripetal force matrix; D(v) is the damping matrix, expressed as D(v)∈R 2×2 ; τ wind , τ wave and τ current Represents the force of ocean environment such as wind, waves, and current on the unmanned boat; τ thr It is represented as the driving force of the unmanned system’s own propulsion system, and v is the velocity vector of the environment and the propulsion system acting on the unmanned boat.

[0097] And model the ocean factors such as wind, waves, currents, etc. for a single boat, including:

[0098] Wind Model where ρ a Represents the air density, A f is the orthographic projection area of ​​the unmanned boat above the waterline, A s is the side projection area of ​​the unmanned boat above the waterline, and C wx (α R ) and C wy (α R ) represents the longitudinal and lateral wind pressure coefficients, V w Represents wind speed.

[0099] Wave Model where τ wavex is the lateral wave disturbance force, τ wavey represents the longitudinal disturbance force; ρ is the density of seawater; ζ D is the average amplitude during the voyage, L is the length of the unmanned boat; χ is the encounter angle during the voyage; C XD (λ) and C YD (λ) is the wave drift force coefficient of the wave in the transverse direction (ie, the first direction in Example 1) and the longitudinal direction (ie, the second direction in Example 1).

[0100] And the flow model τ currentx and τ currenty They represent the lateral and longitudinal disturbance forces on the unmanned boat, and A fis the orthographic projection area of ​​the unmanned boat above the waterline, As is the lateral projection area of ​​the unmanned boat above the waterline, V c and β are the velocity and encounter angle of the flow relative to the ship, C X (β) and C Y (β) is the wave force coefficient in the lateral and longitudinal directions.

[0101] After establishing the comprehensive cost function of the path planning of a single boat and the single boat dynamics model, the control mode between the individuals in the formation is modeled, that is, the position of each unmanned boat is used as a virtual point to establish a virtual navigation point model. The virtual navigation point model can lead the unmanned boat formation to navigate according to the actual situation. Just pay attention to the movement of the virtual navigator to master the track of the unmanned boat formation. There are two ways to select the virtual navigation center point of the unmanned boat formation:

[0102] The first method is to select one of the virtual points in the unmanned boat formation as the virtual navigation center point, and use the center point to calculate the individual positions and related information of the surrounding unmanned boats, so as to ensure the navigation and related activities of the unmanned boats.

[0103] The second method is to select the mathematical center of the unmanned boat formation as the virtual navigation point model, such as Figure 6 shown.

[0104] In the first method, although a small amount of data of the virtual navigation point model can be obtained to determine the formation information and navigation information, once the individual of the virtual navigation point model is damaged or abnormal, the unmanned boat formation will fall into chaos. Therefore, the selection of the mathematical center point in the second method is not only simple and fast in logic, but also easy to operate in practical applications. In the subsequent implementation methods, the second method will also be specifically explained, that is, the virtual navigation point model of the unmanned boat formation is regarded as its mathematical center for analysis.

[0105] After determining the internal structure model of the unmanned boat formation, a time coupling model between unmanned boat formations is established, assuming that the speed of each formation is [v min ,v max ], a path of length L1 is planned for the unmanned boat formation 1. The time interval to reach the target point is The shortest time to arrive and the longest time Then we get Figure 4 The time coordination strategy shown in the figure sorts the unmanned boat formations according to the sailing time. The formation with the longest sailing time sets off first, and after T1 time, the formation with the second longest sailing time sets off, until the formation with the shortest sailing time sets off. Each formation arrives at the target point at the same time. Through this time coordination, multiple formations of unmanned boats can reach the designated target point at the same time in the shortest time. In this process, the formation with the longest sailing time is recorded as k, and the longest time required for it to reach the target point is Tk,max , then the time difference between each formation i and formation k to reach the target point is calculated as ΔT i =T k,max -T i，min , indicating that formation i must be at T k,max Depart before T k,max When the kth formation can reach the target point at the same time, the total time difference T between the formations is calculated, which is equal to the sum of the absolute values ​​of the time differences between all formations. The T value can determine the departure interval between the formations. If the departure interval is too large, it means that the time coordination effect between the unmanned boat formations is poor. Therefore, it is hoped that the T value is as small as possible to strengthen the time coupling between the formations.

[0106] Based on the above time coordination and the path planning objective function of a single boat in the ocean environment, the formation path planning model is determined as follows: And w L +w E +w J +w T =1.

[0107] Based on the above model, the model is solved. This embodiment uses the improved whale algorithm to solve the model. The solution idea of ​​the improved whale algorithm is as follows: Figure 7 and Figure 8 As shown, the improved whale algorithm makes the following improvements compared to the existing whale algorithm:

[0108] The crossover and selection operations of the differential evolution algorithm are introduced to give full play to the global optimization ability of the traditional whale algorithm, and can also actively adapt to the coupling constraints and protect the dominant individuals. By analyzing the solution space of the traditional algorithm, it can be found that the crossover operation in DE can reduce the possibility of pushing individuals into infeasible areas. Therefore, according to the crossover operation in the DE algorithm, the test vector is generated. Among them, i, j, t represent the individual subscript, dimension subscript and iteration number respectively; CR∈(0,1), x i,j,t+1 is the crossover rate, which is calculated using the various models of the improved whale algorithm, including the first predator model, the second predator model, and the random search model. It can be understood as the iterative result (i.e., position ) and then, according to the location Calculate x i,j,t+1 ;x i,j,t is the position of the individual in the previous iteration. To prevent individuals from moving to the position where fitness crosses, the hybrid algorithm can introduce the selection operation in DE The individuals are updated after each iteration, so that the search frequency is higher in each iteration, which makes the search results more diverse, increases the chances of searching in other areas, and avoids falling into the local optimum.

[0109] In addition, this embodiment also improves the model of the whale algorithm used in the prior art for spiral updating and searching for prey, namely That is, by adding the sine As interference and auxiliary terms, it can improve the convergence accuracy. According to the above formula, when If no better solution is found, keep Otherwise, seek a better solution with diversity. To sum up, the improved whale algorithm flowchart is as follows Figure 8 shown.

[0110] For ease of understanding, the improved whale algorithm proposed in this embodiment is compared with the existing whale algorithm.

[0111] The existing whale algorithm constructs and solves mathematical models based on the hunting behavior of humpback whales. The formula summarizes the behavior of humpback whales in locating prey during predation, where t is the current iteration number, and is the coefficient vector, is the position vector of the global optimal solution, and is the position vector of the current solution. If a better solution appears during the iteration, it should be updated immediately and The expression is During the iteration, a decreases linearly from 2 to 0. and is a random vector between [0, 1].

[0112] In addition, whale falls will also use bubble net attack mode, which is divided into contraction and encirclement mechanism and spiral upward update. This mechanism is essentially derived from the encirclement and predation activities of humpback whale groups. Spiral update position is another behavior of humpback whales. Its process can be summarized as a mathematical model, that is, when the whale position (X, Y) is in contact with the prey position (X * ,Y * ), the position model between the whale and the prey can be expressed as Where b is a constant that defines the shape of the logarithmic elimination. It can be seen that the humpback whales swim around their prey in a shrinking circle while swimming along a spiral path. For the humpback whale's predation, the two types of encirclement occur simultaneously, so it is necessary to consider the mathematical modeling of both predation behaviors at the same time. The value of the whale's position is calculated to calculate the optimal position for each step in the iterative process. During the predation process, the whale may take two actions at the same time, so the formula is used Describe the probability of choosing the contraction and encirclement mechanism and the spiral model mechanism during its predation process, thereby updating the position of the whale in its predation behavior.

[0113] In addition to the bubble net attack mode, whale groups also use random search methods to find prey, and individuals in the group will also conduct random searches based on the locations of their companions. Greater than 1 or When it is equal to 1, it will randomly select a value to keep itself away from prey and find more suitable prey. This search mechanism is different from the bubble net attack stage. In the whale fall algorithm, the position of the search stage is updated by randomly selecting a search agent to update the position of the current search agent, thereby improving the position of the entire whale fall and enhancing the search ability. Its mathematical model is Where is a position vector randomly selected in the current population, representing a random whale in the population during the predation process. Therefore, the above formula can be obtained. The WOA algorithm starts with a set of random solutions, and in each iteration, its position is updated according to the randomly selected search agent or the current optimal solution.

[0114] That is, in the existing whale algorithm, the solution process can be understood as:

[0115] Multiple iterations are performed until the number of iterations reaches a preset number, and the path of the ship is determined based on the positions obtained in the last iteration.

[0116] In each iteration, it is determined whether the preset condition is met; wherein the preset condition is p <rand(0,1)。

[0117] If the preset conditions are not met, use Determine the next position (i.e., perform spiral update); where,

[0118] If the preset conditions are met, then Is it greater than or equal to 1? If greater than or equal to 1, use (ie, the random search model in Example 1) determines the next position (ie, searches for prey).

[0119] like If less than 1, use (i.e., the predation model in Example 1) determines the next position (i.e., performs shrinkage and encirclement); wherein, t is the current number of iterations, b is a constant that defines the shape of the logarithmic rejection, is the position vector of the global optimal solution, is the position vector of the current solution, and is the coefficient vector,

[0120] The improved whale algorithm proposed in this embodiment introduces the first predation model The first predator model is used for spiral updating to improve convergence accuracy.

[0121] When the improved whale algorithm is used to solve the formation path planning model, the minimum value P of the comprehensive function and the corresponding length cost, corner cost, energy consumption cost and coordination cost can be obtained. At the same time, the position coordinates of each step are obtained, and these coordinates are smoothed and connected to obtain the planned paths of each formation. The specific ideas of path planning and solution are as follows: Fig. 9 shown.

[0122] Assuming that the coordinates of the starting point of the unmanned boat are (0, 0) and the end point is (29, 29), the solution of the single boat modeling is as follows: Fig.10 As shown in the figure, the solution of the multi-unmanned boat formation is as follows Fig.11 As shown in the figure, the coordinates obtained are actually the coordinates of the unmanned boat, and these coordinates are connected in a straight line. In order to truly express the nature of its route, the fitting technology is used to make it a smooth curve, thus obtaining the following: Fig.10 He Ru Fig.11 The planned path is shown in the figure. Based on the single boat dynamics model and the planned path, the propulsion force required by each unmanned boat can be obtained.

[0123] This embodiment considers global optimization as well as obstacle avoidance, energy and turning angle constraints, and uses global path planning as the final generated path, which can more realistically reflect the trajectory characteristics of the unmanned boat formation. Path planning based on the improved whale algorithm can better complete the time coordination of the unmanned boat formation, and can be robust and superior in experimental results in complex sea areas and formation experiments.

[0124] Embodiment 3:

[0125] Based on Example 1 and Example 2, this embodiment also provides a ship formation path planning system, such as Fig.12 As shown, it includes a modeling module and a calculation module.

[0126] The modeling module is used to establish a formation path planning model; wherein the formation path planning model includes an objective function and a constraint function, and the objective function is minP=P safe (w L L+w E E+w J J+w T T), the constraint function is Among them, P safe is the obstacle avoidance cost of the ship, d is the distance between the ship and the obstacle, safe is the preset safety distance; L is the sailing length of the ship, L max is the preset maximum length; T is the total time difference between formations, T max is the preset maximum time difference; w L represents the path constraint weight, w E represents the energy constraint weight, w J represents the rotation constraint weight, w T represents the time constraint weight; v is the speed of the ship, v min is the preset minimum speed, v max The maximum speed is preset.

[0127] The calculation module is used to solve the formation path planning model to obtain the planned path of each formation.

[0128] The modeling module is also used to establish a ship dynamics model Mv+C(v)v+D(v)v=τ w +τ thr ; where τ w =τ wind +τ wave +τ current , τ wind represents the force of wind on the ship, τ wave represents the force of waves on the ship, τ current represents the force of the flow on the ship, τ thr represents the ship's own propulsion force, v is the velocity vector of the ship acting on the environment and the propulsion system, M is the inertia matrix that measures the difficulty of the ship's state change during navigation, C(v) is the centripetal force matrix; D(v) is the damping matrix, expressed as D(v)∈R 2×2 .

[0129] The calculation module is also used to calculate the propulsion force required for the ship according to the planned path of each formation in combination with the ship dynamics model, so that each ship in the formation moves along the planned path under the action of the propulsion force.

[0130] This embodiment also provides a ship formation path planning device, such as Fig.13 , which is a schematic diagram of the architecture of a ship formation path planning device according to an embodiment of the present invention. The ship formation path planning device according to this embodiment includes one or more processors 21 and a memory 22. Fig.13 A processor 21 is taken as an example.

[0131] The processor 21 and the memory 22 may be connected via a bus or other means. Fig.13 The example of connecting through bus is taken in the following.

[0132] The memory 22 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs and non-volatile computer executable programs, such as the ship formation path planning method in Example 1. The processor 21 executes the ship formation path planning method by running the non-volatile software programs and instructions stored in the memory 22.

[0133] The memory 22 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 22 may optionally include a memory remotely arranged relative to the processor 21, and these remote memories may be connected to the processor 21 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0134] The program instructions / modules are stored in the memory 22, and when executed by the one or more processors 21, the ship formation path planning method in the above-mentioned embodiment 1 is executed.

[0135] It is worth noting that the information interaction, execution process, etc. between the modules and units within the above-mentioned devices and systems are based on the same concept as the processing method embodiment of the present invention. The specific contents can be found in the description of the method embodiment of the present invention and will not be repeated here.

[0136] A person skilled in the art may understand that all or part of the steps in the various methods of the embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A ship formation path planning method, characterized in that: Including n formations, methods include: Establish a formation path planning model; wherein the formation path planning model includes an objective function and a constraint function, and the objective function is min P = P safe (w L L+w E E+w J J+w T T), the constraint function is Among them, P safe is the obstacle avoidance cost of the ship, d is the distance between the ship and the obstacle, safe is the preset safety distance; L is the sailing length of the ship, L max is the preset maximum length; E is the track energy consumption; J is the turning cost; T is the total time difference between the formations, T max is the preset maximum time difference; w L represents the path constraint weight, w E represents the energy constraint weight, w J represents the rotation constraint weight, w T represents the time constraint weight; v is the speed of the ship, v min is the preset minimum speed, v max is the preset maximum speed; Solving the formation path planning model to obtain a planning path for each formation; Establish the ship dynamics model Mv+C(v)v+D(v)v=τ w +τ thr ; where τ w =τ wind +τ wave +τ current , τ wind represents the force of wind on the ship, τ wave represents the force of waves on the ship, τ current represents the force of the flow on the ship, τ thr represents the ship's own propulsion force, v is the velocity vector of the ship acting on the environment and the propulsion system, M is the inertia matrix that measures the difficulty of the ship's state change during navigation, C(v) is the centripetal force matrix; D(v) is the damping matrix, expressed as D(v)∈R 2 ×2 ; According to the planned path of each formation, combined with the ship dynamics model, the propulsion force required by the ship is calculated, so that each ship in the formation moves along the planned path under the action of the propulsion force.

2. The method for ship formation path planning according to claim 1, characterized in that: Wind force on the shipτ wind is the first force τ windx and the second force τ windy The combined force of Among them, the first force τ windx is the force acting on the ship in the first direction from the wind, The second force τ windy is the force exerted on the ship in the second direction from the wind, ρ a Represents the air density, A f is the orthographic projection area of ​​the ship above the waterline, A s is the side projection area of ​​the ship above the waterline, C wx (α R ) represents the wind pressure coefficient in the first direction, C wy (α R ) represents the wind pressure coefficient in the second direction, V w Represents wind speed.

3. The method for ship formation path planning according to claim 1, characterized in that: Wave force on the shipτ wave is the third force τ wavex and the fourth force τ wavey The third force τ wavex is the force of the waves on the ship in the first direction, The fourth force τ wavey is the force of the waves on the ship in the second direction, ρ is the density of seawater; ζ D is the average amplitude during the voyage, L is the length of the ship; χ is the encounter angle during the voyage; C XD (λ) is the wave drift coefficient in the first direction, C YD (λ) is the wave drift force coefficient in the second direction.

4. The method for ship formation path planning according to claim 1, characterized in that: The force of the current on the ship τ current is the fifth force τ currentx and the sixth force τ currenty The fifth force τ currentx is the force exerted on the ship by upstream in the first direction, The sixth force τ cuurenty is the force exerted on the ship by the upstream in the second direction, A f is the orthographic projection area of ​​the ship above the waterline, As is the lateral projection area of ​​the ship above the waterline, V c and β are the velocity and encounter angle of the flow relative to the ship, C X (β) is the wave force coefficient in the first direction, C Y (β) is the wave force coefficient in the second direction.

5. The method for ship formation path planning according to claim 1, characterized in that: Total time difference between formations Where, ΔT i =T k,max -T i，min , T k,max It is the time it takes for the longest formation to reach the target point.

6. The method for ship formation path planning according to claim 1, characterized in that: Solving the formation path planning model to obtain the planned path of each formation specifically includes: Perform multiple iterations until the number of iterations reaches a preset number, and determine the path of the ship based on the positions obtained in the last iteration; In each iteration, it is determined whether the preset condition is met; wherein the preset condition is p <rand(0,1); If the preset condition is not met, the first predator-prey model is used to determine the next position; wherein the first predator-prey model is If the preset conditions are met, then Is it greater than or equal to 1? is greater than or equal to 1, a random search model is used to determine the next position; wherein the random search model is like If it is less than 1, the second predator-prey model is used to determine the next position; wherein the predator-prey model is In the first predator-prey model and the second predator-prey model, t is the current number of iterations, b is a constant that defines the shape of the logarithmic rejection, is the position vector of the global optimal solution, is the position vector of the current solution, and is the coefficient vector, 7. The method for ship formation path planning according to claim 1, characterized in that: The step of solving the formation path planning model to obtain the planning path of each formation also includes: in each iteration, updating the individuals, specifically including: Generate test vectors Update the individual using the test vector Among them, i, j, and t represent the individual subscript, dimension subscript, and iteration number respectively; among them, CR∈(0,1), x i,j,t+1 is the crossover rate, x i,j,t is the position of the individual in the previous iteration.

8. A ship formation path planning device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the ship formation path planning method described in any one of claims 1-7.

9. A non-volatile computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, which are executed by one or more processors to complete the ship formation path planning method described in any one of claims 1-7.

10. A ship formation path planning system, characterized in that: Includes modeling module and calculation module; The modeling module is used to establish a formation path planning model; wherein the formation path planning model includes an objective function and a constraint function, and the objective function is min P = P safe (w L L+w E E+w J J+w T T), the constraint function is Among them, P sa fe is the obstacle avoidance cost of the ship, d is the distance between the ship and the obstacle, safe is the preset safety distance; L is the sailing length of the ship, L max is the preset maximum length; T is the total time difference between formations, T max is the preset maximum time difference; w L represents the path constraint weight, w E represents the energy constraint weight, w J represents the rotation constraint weight, w T represents the time constraint weight; v is the speed of the ship, v min is the preset minimum speed, v max is the preset maximum speed; The calculation module is used to solve the formation path planning model to obtain the planned path of each formation; The modeling module is also used to establish a ship dynamics model Mv+C(v)v+D(v)v=τ w +τ thr ; where τ w =τ wind +τ wave +τ current , τ wind represents the force of wind on the ship, τ wave represents the force of waves on the ship, τ current represents the force of the flow on the ship, τ thr represents the ship's own propulsion force, v is the velocity vector of the ship acting on the environment and the propulsion system, M is the inertia matrix that measures the difficulty of the ship's state change during navigation, C(v) is the centripetal force matrix; D(v) is the damping matrix, expressed as D(v)∈R 2×2 ; The calculation module is also used to calculate the propulsion force required for the ship according to the planned path of each formation in combination with the ship dynamics model, so that each ship in the formation moves along the planned path under the action of the propulsion force.

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