Warship surface equipment real-time path planning algorithm based on adjustable fast step leveling method

Abstract

The embodiment of the invention provides a ship surface equipment real-time path planning algorithm based on an adjustable fast step leveling method, and the algorithm comprises the steps: building a kinematics model of a rod traction system and an obstacle constraint model, and generating a planning space based on the models and global environment constraint information; obtaining the optimal cost value of each grid in the planning space in the whole planning space; obtaining an optimal path from the target point to the starting point by solving the optimal cost value; the safety margin diagram is changed by adjusting the extension distance, so that the generated planned path can be balanced between the collision risk degree and the path length according to specific task requirements. On the basis of the multi-wave-source expansion principle, the safety margin diagram is changed by adjusting the expansion distance, the balance of the path length and the risk degree is achieved, and the safety and rationality of the path are improved; by introducing an obstacle constraint model into a planning space, real-time correction of ship surface dispatching is realized, and the traceability and robustness of a path are improved.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent aviation support and intelligent unmanned system, in particular to a real-time path planning algorithm for ship deck equipment based on an adjustable fast step leveling method. Background Art

[0002] With the continuous development of modern science and technology, ships are taking on more and more important tasks on the ocean. However, in the complex ocean environment, the path planning problem of the ship deck becomes particularly important.

[0003] The path planning of the ship deck can be divided into two types: offline path planning and real-time path planning. Offline path planning refers to planning the transportation path of carrier-based aircraft in advance, which is usually solved by using optimization algorithms, such as A * Algorithm, Dijkstra algorithm, etc. However, in the face of complex ocean environment and ship surface environment, offline path planning often cannot meet the real-time requirements. Therefore, real-time path planning technology is particularly important.

[0004] The path planning of the fast stepping method has the advantages of continuity, smoothness and avoiding local optimal values. However, since the fast stepping method is to obtain the shortest path to the maximum extent, it does not fully consider the collision risk of the planned path; the planning path of the fast stepping method solves the problem of high collision risk of the fast stepping method, but its main disadvantage is that the planned path length is long. The planning space of the aircraft carrier deck is limited, and the equipment is crowded. Therefore, the aircraft carrier deck transportation path planning must consider both the path length and the collision safety. In order to achieve a balance between the risk and the path length when planning the path on the deck.

[0005] The real-time path planning algorithm for the deck based on the adjustable fast step-by-step method is a relatively new technology. This algorithm combines the traditional optimization algorithm and the fast step-by-step method, and can achieve real-time performance while ensuring path optimization. The adjustable fast step-by-step method is an efficient search method that can quickly find the optimal solution by stepping in stages and estimating the optimal path. Applying the adjustable fast step-by-step method to deck path planning can effectively improve the efficiency and real-time performance of path planning. Through this algorithm, carrier-based aircraft can plan the optimal path in real time according to the current environmental information and target points, avoid obstacles and dangerous areas, and realize safe and efficient real-time deployment of aircraft carrier decks. Summary of the invention

[0006] In view of the above problems existing in the prior art, the embodiment of the present invention provides a real-time path planning algorithm for ship deck equipment based on an adjustable fast step-in leveling method. The first is to establish a kinematic model of a rod-pulling system and an obstacle constraint model, and generate a planning space based on the model and global environmental constraint information; the second is the exploration process, obtaining the optimal cost value of each grid in the entire planning space, that is, the arrival time; the third is the acquisition process, that is, by solving the optimal cost value, obtaining the optimal path from the target point to the starting point; finally, by adjusting the extension distance to change the safety margin map, the generated planning path can be balanced between the collision risk and the path length according to the specific task requirements. The present invention designs an adjustable fast step-in leveling method. Based on the principle of multi-wave source expansion, the safety margin map is changed by adjusting the extension distance, so as to achieve a balance between the path length and the risk, and improve the safety and rationality of the path; a real-time path planning algorithm for ship deck mobile equipment is proposed. By introducing an obstacle constraint model in the planning space, real-time correction of ship deck transportation is achieved, and the traceability and robustness of the path are improved.

[0007] The embodiment of the present invention provides a real-time path planning algorithm for ship deck equipment based on an adjustable fast step leveling method, comprising:

[0008] S1. Establish a kinematic model of the rod traction system and an obstacle constraint model, and generate a planning space based on the model and global environmental constraint information;

[0009] S2. Obtaining the optimal cost value of each grid in the planning space in the entire planning space, wherein the optimal cost value includes the arrival time;

[0010] S3, by solving the optimal cost value, obtain the optimal path from the target point to the starting point;

[0011] S4. By adjusting the extension distance, the safety margin map is changed so that the generated planning path can balance the collision risk and path length according to the specific task requirements.

[0012] In some embodiments of the present invention, in step S1, establishing a kinematic model of the rod traction system includes:

[0013] The rod-type towing system used on aircraft carriers is set as an off-axis system;

[0014] θ1, θ2 and θ3 represent the direction angles of the carrier aircraft, the traction rod and the traction vehicle respectively;

[0015] (x1, y1), (x2, y2), (x3, y3) and (x4, y4) represent the position coordinates of the carrier aircraft, the hinge point between the carrier aircraft and the traction rod, the hinge point between the traction rod and the tractor, and the tractor respectively;

[0016] β1, β2 and α are the steering angles of the carrier aircraft, the towing rod and the towing vehicle respectively; L1 is the front and rear wheel tracks of the carrier aircraft, L2 is the length of the towing rod, L3 is the front and rear wheel tracks of the towing vehicle, and M is the vertical distance from the articulation point of the towing rod and the towing vehicle to the rear wheel of the towing vehicle; the speeds of the carrier aircraft and the towing vehicle are v1 and v3 respectively;

[0017] Then we get

[0018] β2=θ3-θ2, β1=θ2-θ1;

[0019] Since the ultimate purpose of the rod-pulling system is to transport the aircraft to a specified location, the variables that characterize the aircraft's attitude should be taken as state variables;

[0020] According to the system's degrees of freedom, when the attitude of the carrier-based aircraft is known, β1 and β2 are determined, that is, the position coordinates of the hinge point between the carrier-based aircraft and the traction rod, the hinge point between the traction rod and the tractor, and the tractor are determined;

[0021] Therefore, the state variables of the system are recorded as: X = [x1, y1, θ1, β1, β2] T

[0022] Due to the relationship between the carrier-based aircraft and the tractor, the speed of the carrier-based aircraft is expressed as:

[0023]

[0024] Then the control variables are represented by the speed of the carrier aircraft and the steering angle of the tractor.

[0025] That is, U(t)=[u1,u2] T , where u1 = tanα;

[0026] Therefore, the kinematic model of the rod traction system is described as:

[0027]

[0028] The equation is established based on the assumption that all steering angles are less than 55° and M≤0.5L3;

[0029] The original system is transformed into a virtual rodless traction system on the axis, and its kinematic equation is:

[0030]

[0031] Among them, θ1 and θ2 represent the angles between the axis of the carrier aircraft and the tractor and the horizontal axis respectively;

[0032] x1 and y1 represent the horizontal and vertical coordinates of the carrier-based aircraft respectively;

[0033] β1 and β2 are the steering angles of the carrier aircraft and the tractor respectively;

[0034] L1 is the front and rear wheel track of the carrier-based aircraft, L3 is the front and rear wheel track of the tractor, M o It is the vertical distance between the articulation point of the drawbar and the tractor and the rear wheel of the tractor;

[0035] The speeds of the carrier aircraft and tractor are v1 and v2 respectively;

[0036] The control variables consist of the acceleration of the virtual tractor and the steering angle (u1 = tanβ2);

[0037] Use this system to replace the original system for path planning;

[0038] According to this virtual system, we get [x1,y1,θ1,θ2,β2] T ;

[0039] According to the structural characteristics of the system, the position and posture of the tractor are expressed as:

[0040]

[0041] Therefore, the path of the tractor is also obtained, and then the path and attitude information of the tractor, traction rod and carrier-based aircraft of the system are obtained;

[0042] In addition, the turning angle β1 and speed v1 of the carrier-based aircraft should satisfy:

[0043]

[0044] In addition, the control variables of the tractor should also satisfy the corresponding constraints, namely:

[0045]

[0046] In some embodiments of the present invention, in step S1, the method further includes:

[0047] Taking a circle as an example, O1(x, y) represents the geometric center of the carrier-based aircraft, and r represents its radius; O2(x o ,y o ) represents the position of the geometric center of the obstacle, r o represents its radius;

[0048] The geometric position relationship between the carrier-based aircraft and the obstacle. The gray area represents the area formed by the safe distance, and its radius is dist = r so -r o , then when d≥r+r so When the carrier-based aircraft is considered safe, there is no collision;

[0049] In order to maintain generality, the following formula is used to indicate that there is no collision between the carrier-based aircraft and the obstacle:

[0050]

[0051] Where, 1≤i≤n; x oi and oi are the center coordinates of the ith obstacle; a i and b i are its width and height respectively; dist is the safety distance set according to safety requirements and the shape and structure of the carrier-based aircraft; p i is the shape parameter of the graph;

[0052] When p i =1, the above formula describes a rhombus; when p i =2, the description is a circle or an ellipse; when p i →∞, the description is a rectangle.

[0053] In some embodiments of the present invention, in step S1, generating a planning space based on the model and global environmental constraint information includes:

[0054] Detailed two-dimensional information of the above model constraints and deck environment constraints is obtained by binarizing the image, and the image pixels are used as grid units;

[0055] In the binary image, the black area represents the prohibited area, the pixel value is 0, and the white area represents the feasible area, the pixel value is 1. The processed image is expanded to obtain the planning space H o .

[0056] In some embodiments of the present invention, in step S2, obtaining the optimal cost value of each grid in the entire planning space includes:

[0057] The fastest propagation path of the wave from the source point to the target point in the expansion of the water wave is regarded as the optimal path, and a time function based on wave propagation is established;

[0058] Based on the wave source expansion principle, the following eikonal equation is established in the planning space H o The arrival time T of the wave at each point in space is obtained;

[0059]

[0060] Among them, (x, y) represents the planning space H o One point in is the gradient of the arrival time T(x,y), and V(x,y) is the local propagation velocity of the wave at (x,y).

[0061] In some embodiments of the present invention, in step S3, it includes:

[0062] By using the upwind difference method, the T value of the point (x, y) is solved, and its neighborhood is a point set containing four points (x+Δx, y), (x-Δx, y), (x, y+Δy), (x, y-Δy);

[0063] Based on the gradient discretization method, Discretize and derive the following equation:

[0064]

[0065] in,

[0066]

[0067] The grid spacing in the x and y directions is Δx and Δy, let:

[0068]

[0069] For the discrete two-dimensional space, the eikonal equation can be rewritten as:

[0070]

[0071] Therefore, the equation can be solved as follows:

[0072]

[0073] When there is a single wave source, the minimum arrival time is at the wave source. For multiple wave sources, each wave source corresponds to its own minimum arrival time.

[0074] Select the grid with small replacement value, that is, the point with small arrival time, and connect them to get the optimal path from the target point to the starting point.

[0075] In some embodiments of the present invention, in step S4, it includes:

[0076] By setting a preset distance as a threshold, when obtaining the hazard map, the wave surface with the prohibited area as the source point is only extended to this distance, and the hazard map corresponding to the feasible area that has not been extended is set to the hazard map value corresponding to this distance. This distance is called the extended distance D X , and its corresponding hazard map value is M X ;

[0077] The final updated danger map is H M,DX ; The hazard map value is less than M X The feasible region is called the expansion region;

[0078] When the extended distance DX When it decreases, the expansion area also decreases, and the planned path will be closer to the prohibited area, which reduces the safety margin and the path length;

[0079] On the contrary, when the extension distance D X When it increases, the expansion area increases, and the planned path will be away from obstacles, which increases the safety margin and the path length;

[0080] When the extended distance D X When it is equal to 0, the expansion area is reduced to the minimum, and the adjustable fast stepping method is the fast stepping method;

[0081] When the extended distance D X When it is greater than the maximum distance from the feasible area to the prohibited area, the extended area is expanded to the maximum, and the adjustable fast step leveling method is the fast step leveling method;

[0082] To clarify the extended distance D of the proposed adjustable fast step leveling method X The selection basis is to design a cost function for evaluating the planned path according to the optimization objective function of the intelligent optimization algorithm:

[0083]

[0084] In the formula, and are the path length cost index and collision risk index respectively, ω1 and (1-ω1) are their weight coefficients;

[0085] Cost function J path The smaller the value of, the better the planned path;

[0086] Path length cost metric and collision risk index The calculation formula is as follows:

[0087]

[0088] In the formula, is the planning path length, D s is the Euclidean distance from the starting point to the end point, N is the number of sampling points of the planned path, d SDA For safe meeting distance, D path is the minimum Euclidean distance from the sampling point on the planned path to the prohibited area, and when D pat h>d SDA When D pat h=d SDA .

[0089] Compared with the prior art, the beneficial effects of the real-time path planning algorithm for deck equipment based on the adjustable fast step-by-step leveling method provided by the embodiment of the present invention are as follows: it designs an adjustable fast step-by-step leveling method, which, based on the multi-wave source expansion principle, changes the safety margin diagram by adjusting the expansion distance, thereby achieving a balance between path length and danger, and improving the safety and rationality of the path; it also proposes a real-time path planning algorithm, which, by introducing an obstacle constraint model in the planning space, achieves real-time correction of the deck transportation of carrier-based aircraft, and improves the traceability and robustness of the path. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 A schematic flow chart of a real-time path planning algorithm for ship deck equipment based on an adjustable fast step leveling method provided in an embodiment of the present invention;

[0091] Figure 2 A design flow chart of a real-time path planning algorithm for ship deck equipment based on an adjustable fast step leveling method provided in an embodiment of the present invention;

[0092] Figure 3 A schematic diagram of the composition of a rod traction system in a real-time path planning algorithm for ship deck equipment based on an adjustable fast step leveling method provided by an embodiment of the present invention;

[0093] Figure 4 A schematic diagram of the composition of a virtual on-axis rodless traction system in a real-time path planning algorithm for ship deck equipment based on an adjustable fast step leveling method provided by an embodiment of the present invention;

[0094] Figure 5 A geometric position relationship diagram of a carrier-based aircraft and obstacles in a real-time path planning algorithm for deck equipment based on an adjustable fast step leveling method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0095] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0096] Various aspects and features of the present application are described herein with reference to the accompanying drawings.

[0097] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.

[0098] It should also be understood that, although the present application has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present application that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0099] The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

[0100] Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments applied for are merely examples of the present application, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to determine the true intent based on the user's historical operations and to avoid unnecessary or redundant details that make the present application unclear. Therefore, the specific structural and functional details applied for herein are not intended to be limiting, but are merely used as the basis and representative basis for the claims to teach those skilled in the art to use the present application in a variety of ways with substantially any suitable detailed structure.

[0101] This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present application.

[0102] like Figure 1 As shown, the present invention provides a real-time path planning algorithm for ship deck equipment based on an adjustable fast step-by-step method, and the specific scheme design includes:

[0103] S1, initialization process, establishing the kinematic model of the rod traction system and the obstacle constraint model, and generating the planning space based on the model and the global environment constraint information;

[0104] S2, the exploration process, obtains the optimal cost value of each grid in the planning space, i.e., the arrival time, in the entire planning space;

[0105] S3, the acquisition process, that is, obtaining the optimal path from the target point to the starting point by solving the optimal cost value;

[0106] S4, the optimization process, changes the safety margin map by adjusting the expansion distance, so that the generated planning path can balance the collision risk and path length according to the specific task requirements.

[0107] Specifically, the algorithm design process of the present invention is as follows: Figure 2 shown.

[0108] In specific implementation, as a preferred embodiment of the present invention, the step S1 specifically includes:

[0109] S11. In this embodiment, a kinematic model of a rod traction system is first established. A kinematic model of a rod traction system is established. At present, the rod traction systems used on aircraft carriers are generally off-axis systems, with a structure such as Figure 3 shown.

[0110] exist Figure 3 In the equation, θ1, θ2 and θ3 represent the direction angles of the carrier aircraft, the towing rod and the tractor respectively; (x1, y1), (x2, y2), (x3, y3) and (x4, y4) represent the position coordinates of the carrier aircraft, the hinge point between the carrier aircraft and the towing rod, the hinge point between the towing rod and the tractor and the tractor respectively; β1, β2 and α represent the steering angles of the carrier aircraft, the towing rod and the tractor respectively; L1 represents the front and rear wheel track of the carrier aircraft, L2 represents the length of the towing rod, L3 represents the front and rear wheel track of the tractor, and M represents the vertical distance from the hinge point between the towing rod and the tractor to the rear wheel of the tractor; the speeds of the carrier aircraft and the tractor are v1 and v3 respectively.

[0111] Depend on Figure 3 , we can get β2=θ3-θ2, β1=θ2-θ1. Since the ultimate goal of the rod towing system is to transport the aircraft to a specified location, the variables that characterize the aircraft's attitude should be used as state variables. According to the system's degrees of freedom, when the attitude of the carrier-based aircraft is known, it is only necessary to determine β1 and β2 to determine the position coordinates of the hinge point between the carrier-based aircraft and the towing rod, the hinge point between the towing rod and the tractor, and the position coordinates of the tractor. Therefore, the state variable of the system is recorded as X=[x1,y1,θ1,β1,β2] T .

[0112] Due to the relationship between the carrier-based aircraft and the tractor, the speed of the carrier-based aircraft can be expressed as:

[0113]

[0114] Then the control variables can be expressed as the speed of the carrier aircraft and the steering angle of the tractor,

[0115] That is U(t)=[u1,u2] T , where u1 = tanα.

[0116] Therefore, the kinematic model of the rod traction system can be described as:

[0117]

[0118] It should be noted that the equation is established based on the assumption that all steering angles are less than 55° and M≤0.5L3.

[0119] Although the above model can be directly used for path planning and control design, the system is sensitive to the initial solution and is not conducive to engineering application. Therefore, it is converted into a path planning problem for a rodless system.

[0120] In order to solve the above problems, the original system is transformed into a virtual rodless traction system on the axis, such as Figure 4 As shown, its kinematic equation is:

[0121]

[0122] Among them, θ1 and θ2 represent the angles between the axis and the horizontal coordinate of the carrier-based aircraft and the tractor respectively; x1 and y1 represent the horizontal and vertical coordinates of the carrier-based aircraft respectively; β1 and β2 represent the steering angles of the carrier-based aircraft and the tractor respectively; L1 represents the front and rear wheel track of the carrier-based aircraft, L3 represents the front and rear wheel track of the tractor, and M o is the vertical distance between the traction rod and the tractor hinge point to the tractor rear wheel; the speeds of the carrier-based aircraft and the tractor are v1 and v2 respectively; the control variable is composed of the acceleration of the virtual tractor and the steering angle (u1=tanβ2).

[0123] The kinematic equations of this system are more suitable for engineering applications than the original system, so this system is used to replace the original system for path planning.

[0124] According to this virtual system, we can get [x1,y1,θ1,θ2,β2] T According to the structural characteristics of the system, the position and posture of the tractor can be expressed as:

[0125]

[0126] Therefore, the path of the tractor can also be obtained, and then the path and attitude information of each part of the system (tractor, tractor rod and carrier-based aircraft) can be obtained.

[0127] In addition, the turning angle β1 and speed v1 of the carrier-based aircraft should satisfy:

[0128]

[0129] In addition, the control variables of the tractor should also satisfy the corresponding constraints, namely:

[0130]

[0131] S12. Secondly, a reasonable environmental model representation method is a prerequisite for establishing a path planning method and selecting a suitable search algorithm. Due to the small deck area, carrier-based aircraft often encounter various obstacles during their movement on the deck, and the feasible path connecting the starting point and the end point falls within the space that does not contain obstacles. Therefore, using a mathematical model to reasonably describe the boundary constraints (obstacle constraints) so that the space represented by the boundary constraint model can contain actual obstacles as much as possible without wasting the theoretical feasible space is an important part of path planning.

[0132] Taking a circle as an example, O1(x, y) represents the geometric center of the carrier-based aircraft, and r represents its radius; O2(x o ,y o ) represents the position of the geometric center of the obstacle, r orepresents its radius; the geometric position relationship between the carrier-based aircraft and the obstacle is as follows Figure 5 As shown, the gray area represents the area formed by the safety distance, and its radius is dist = r so -r o , then when d≥r+r so When the carrier-based aircraft is considered safe, no collision occurs.

[0133] In order to maintain generality, the following formula is used to indicate that there is no collision between the carrier-based aircraft and the obstacle:

[0134]

[0135] Where, 1≤i≤n; x oi and oi are the center coordinates of the ith obstacle; a i and b i are its width and height respectively; dist is the safety distance set according to safety requirements and the shape and structure of the carrier-based aircraft; p i is the shape parameter of the graph. i is the shape parameter of the graph. i =1, the above formula describes a rhombus; when p i =2, the description is a circle or an ellipse; when p i →∞, the description is a rectangle.

[0136] S13, generate planning space based on the above model and global environment constraint information. Obtain detailed two-dimensional information of the above model constraints and deck environment constraints by binarizing the image (pixel values ​​are all 0 or 1), and use image pixels as grid units. Among them, the deck environment refers to the deck of the CVN 78 "Gerald R. Ford" aircraft carrier (USS Gerald R. Ford), and its detailed information is as follows:

[0137] (1) The flight deck of the Ford-class aircraft carrier is 333 meters long and 78 meters wide;

[0138] (2) Ford-class aircraft carriers have only three elevators, two in front of the starboard island and one on the port side aft;

[0139] (3) The Ford class has four catapults, the same as the current US aircraft carriers, with a spacing of 18 meters from the deflector and an installation angle of 9.3 degrees. Two are located at the bow and the other two are located on the angled deck. The catapult takeoff runway is 99.13 meters long and occupies a deck of 104 meters.

[0140] (4) The landing runway must be wider than the wingspan of the aircraft. Generally, a width of 5m is reserved on each side. The centerline of the runway is in the middle, which is used for pilots to align when landing. The double white line in the middle is the limit line for fighter landing. The inner width is 20m and the outer width is 25m. The entire landing runway is 240m long. The length from the tail of the Ford-class aircraft carrier flight deck to the front arresting cable is about 80m.

[0141] (5) In terms of the design of the support positions, the Ford-class aircraft carrier is equipped with 24 support positions. Since the algorithm mainly focuses on transportation rather than support, 18 of the support positions and 26 parking positions are selected.

[0142] In the binary image, the black area represents the prohibited area, the pixel value is 0, and the white area represents the feasible area, the pixel value is 1. The processed image is expanded to obtain the planning space H * .

[0143] In specific implementation, as a preferred embodiment of the present invention, step S2 specifically includes:

[0144] S21. The exploration process is very similar to the expansion of water waves. The fastest propagation path of the wave from the source point to the target point can be regarded as the optimal path. Therefore, it is necessary to establish a time function based on wave propagation.

[0145] Based on the wave source expansion principle, the following eikonal equation is established in the planning space H o The arrival time T of the wave at each point in space is obtained.

[0146]

[0147] Among them, (x, y) represents the planning space H o One point in is the gradient of the arrival time T(x,y), and V(x,y) is the local propagation velocity of the wave at (x,y).

[0148] The real-time path planning algorithm for ship deck equipment based on the adjustable fast step leveling method is characterized in that, in step S3, the optimal cost value is solved to obtain the optimal path from the target point to the starting point.

[0149] S31. Use the upwind difference method to solve the T value of point (x, y). Its neighborhood is a point set containing four points (x+Δx, y), (x-Δx, y), (x, y+Δy), (x, y-Δy). Based on the gradient discretization method, Discretize and derive the following equation:

[0150]

[0151] in,

[0152]

[0153] The grid spacing in the x and y directions is Δx and Δy. Let:

[0154]

[0155] For discrete two-dimensional space, the eikonal equation can be rewritten as:

[0156]

[0157] Therefore, the equation can be solved as follows:

[0158]

[0159] When there is a single wave source, the minimum value of the arrival time is at the wave source. For the case of multiple wave sources, each wave source corresponds to its own minimum arrival time. Select the grid with the smallest value, that is, the point with the smallest arrival time, and connect them to get the optimal path from the target point to the starting point.

[0160] In specific implementation, as a preferred embodiment of the present invention, step S4 specifically includes:

[0161] By adjusting the extension distance to change the safety margin map, the planned path generated in step S3 can be balanced between the collision risk and the path length according to the specific mission requirements. Considering that the carrier-based aircraft only has a collision risk in the part close to the forbidden area, it is very safe to carry out real-time shipboard operations in most feasible areas. To this end, by setting a certain distance as a threshold, when obtaining the danger map, the wave surface with the forbidden area as the source point is only extended to this distance, and the danger map corresponding to the feasible area that has not been extended is set to the danger map value corresponding to the distance. This distance is referred to as the extension distance D in the present invention. X , and its corresponding hazard map value is M X The final updated danger map is H M,DX . The hazard map value is less than M X The feasible region is called the expansion region.

[0162] When the extended distance D X When the extended distance D decreases, the extended area also decreases, and the planned path will be closer to the prohibited area, which reduces the safety margin and the path length. On the contrary, when the extended distance D X When the extension distance D increases, the expansion area increases, and the planned path will be away from obstacles, which increases the safety margin and the path length. X When it is equal to 0, the expansion area is reduced to the minimum, and the adjustable fast stepping method is the fast stepping method; when the expansion distance DX When it is greater than the maximum distance from the feasible area to the prohibited area, the extended area is expanded to the maximum. At this time, the adjustable fast step leveling method is the fast step leveling method.

[0163] To clarify the extended distance D of the proposed adjustable fast step leveling method X The selection basis is to design a cost function for evaluating the planned path according to the optimization objective function of the intelligent optimization algorithm:

[0164]

[0165] In the formula, and are the path length cost index and collision risk index, respectively, and ω1 and (1-ω1) are their weight coefficients. Cost function J path The smaller the value, the better the planned path.

[0166] Path length cost metric and collision risk index The calculation formula is as follows:

[0167]

[0168] In the formula, is the planning path length, D s is the Euclidean distance from the starting point to the end point, N is the number of sampling points of the planned path, d SDA For safe meeting distance, D path is the minimum Euclidean distance from the sampling point on the planned path to the prohibited area, and when D pat h>d SDA When D pat h=d SDA .

[0169] Example

[0170] A real-time path planning algorithm for deck equipment based on an adjustable fast step level method was used to verify the real-time transportation path planning of carrier-based aircraft on the deck using MATLAB simulation.

[0171] The real-time transportation path planning of carrier-based aircraft deck is set as follows. Taking the Ford-class aircraft carrier as an example, the size of the flight deck is 333m×78m, and the starting point position of the carrier-based aircraft is set to [35m70m] and the target point position is [240m45m]. First, read the Ford-class aircraft carrier flight deck image into the MATLAB workspace, and then obtain the planning space H by performing binary expansion and corrosion operations on the image. o, and then the adjustable fast step-forward method is applied to plan the real-time transportation path of carrier-based aircraft decks, so that the path generated by it can balance the safety margin and path length. The specific application of the adjustable fast step-forward method to plan the real-time transportation path of carrier-based aircraft decks is as follows: First, based on the multi-wave source expansion principle, the optimal cost value of each grid is obtained in the overall planning space, the shortest distance between each point in the feasible area and the prohibited area is obtained, and the optimal path from the target point to the starting point is generated; then, the safety margin map is adjusted by setting the expansion distance. When the expansion distance is equal to 0, the expansion area is reduced to the minimum, and the adjustable fast step-forward method is the fast step method; when the expansion distance is greater than the farthest distance from the feasible area to the prohibited area, the expansion area is expanded to the maximum, and the adjustable fast step-forward method is the fast step-forward method.

[0172] The present invention achieves a balance between the path length and the degree of danger, thereby improving the safety and rationality of the path.

[0173] It can be seen from the above technical scheme that the real-time path planning algorithm for deck equipment based on the adjustable fast step-by-step leveling method provided in the above embodiment of the present invention designs an adjustable fast step-by-step leveling method, which, based on the multi-wave source expansion principle, achieves a balance between path length and danger by adjusting the expansion distance to change the safety margin diagram, thereby improving the safety and rationality of the path; a real-time path planning algorithm is also proposed, which realizes real-time correction of the deck transportation of carrier-based aircraft by introducing an obstacle constraint model in the planning space, thereby improving the traceability and robustness of the path.

[0174] The above embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention. The protection scope of the present invention is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the essence and protection scope of the present invention, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present invention.

Claims

1. A real-time path planning algorithm for ship deck equipment based on an adjustable fast step leveling method, characterized in that: include: S1. Establish a kinematic model of the rod traction system and an obstacle constraint model, and generate a planning space based on the model and global environmental constraint information; S2. Obtaining the optimal cost value of each grid in the planning space in the entire planning space, wherein the optimal cost value includes the arrival time; S3, by solving the optimal cost value, obtain the optimal path from the target point to the starting point; S4. By adjusting the extension distance, the safety margin map is changed so that the generated planning path can balance the collision risk and path length according to the specific task requirements.

2. The real-time path planning algorithm for ship deck equipment based on the adjustable fast step leveling method according to claim 1 is characterized in that: In step S1, the kinematic model of the rod traction system is established, including: The rod-type towing system used on aircraft carriers is set as an off-axis system; θ1, θ2 and θ3 represent the direction angles of the carrier aircraft, the traction rod and the traction vehicle respectively; (x1, y1), (x2, y2), (x3, y3) and (x4, y4) represent the position coordinates of the carrier aircraft, the hinge point between the carrier aircraft and the traction rod, the hinge point between the traction rod and the tractor, and the tractor respectively; β1, β2 and α are the steering angles of the carrier aircraft, the towing rod and the towing vehicle respectively; L1 is the front and rear wheel tracks of the carrier aircraft, L2 is the length of the towing rod, L3 is the front and rear wheel tracks of the towing vehicle, and M is the vertical distance from the articulation point of the towing rod and the towing vehicle to the rear wheel of the towing vehicle; the speeds of the carrier aircraft and the towing vehicle are v1 and v3 respectively; Then we get β2=θ3-θ2, β1=θ2-θ1; Since the ultimate purpose of the rod-pulling system is to transport the aircraft to a specified location, the variables that characterize the aircraft's attitude should be taken as state variables; According to the system's degrees of freedom, when the attitude of the carrier-based aircraft is known, β1 and β2 are determined, that is, the position coordinates of the hinge point between the carrier-based aircraft and the traction rod, the hinge point between the traction rod and the tractor, and the tractor are determined; Therefore, the state variables of the system are recorded as: X = [x1, y1, θ1, β1, β2] T Due to the relationship between the carrier-based aircraft and the tractor, the speed of the carrier-based aircraft is expressed as: Then the control variables are represented by the speed of the carrier aircraft and the steering angle of the tractor. That is, U(t)=[u1,u2] T , where u1 = tanα; Therefore, the kinematic model of the rod traction system is described as: The equation is established based on the assumption that all steering angles are less than 55° and M≤0.5L3; The original system is transformed into a virtual rodless traction system on the axis, and its kinematic equation is: Among them, θ1 and θ2 represent the angles between the axis of the carrier aircraft and the tractor and the horizontal axis respectively; x1 and y1 represent the horizontal and vertical coordinates of the carrier-based aircraft respectively; β1 and β2 are the steering angles of the carrier aircraft and the tractor respectively; L1 is the front and rear wheel track of the carrier-based aircraft, L3 is the front and rear wheel track of the tractor, M o It is the vertical distance between the articulation point of the drawbar and the tractor and the rear wheel of the tractor; The speeds of the carrier aircraft and tractor are v1 and v2 respectively; The control variables consist of the acceleration of the virtual tractor and the steering angle (u1 = tanβ2); Use this system to replace the original system for path planning; According to this virtual system, we get [x1,y1,θ1,θ2,β2] T ; According to the structural characteristics of the system, the position and posture of the tractor are expressed as: Therefore, the path of the tractor is also obtained, and then the path and attitude information of the tractor, traction rod and carrier-based aircraft of the system are obtained; In addition, the turning angle β1 and speed v1 of the carrier-based aircraft should satisfy: In addition, the control variables of the tractor should also satisfy the corresponding constraints, namely:

3. The real-time path planning algorithm for ship deck equipment based on the adjustable fast step level method according to claim 2 is characterized in that: In step S1, the method further includes: Taking a circle as an example, O1(x, y) represents the geometric center of the carrier-based aircraft, and r represents its radius; O2(x o ,y o ) represents the position of the geometric center of the obstacle, r o represents its radius; The geometric position relationship between the carrier-based aircraft and the obstacle. The gray area represents the area formed by the safe distance, and its radius is dist = r so -r o , then when d≥r+r so When the carrier-based aircraft is considered safe, there is no collision; In order to maintain generality, the following formula is used to indicate that there is no collision between the carrier-based aircraft and the obstacle: Where, 1≤i≤n; x oi and oi are the center coordinates of the ith obstacle; a i and b i are its width and height respectively; dist is the safety distance set according to safety requirements and the shape and structure of the carrier-based aircraft; p i is the shape parameter of the graph; When p i =1, the above formula describes a rhombus; when p i =2, the description is a circle or an ellipse; when p i →∞, the description is a rectangle.

4. The real-time path planning algorithm for ship deck equipment based on the adjustable fast step leveling method according to claim 3 is characterized in that: In step S1, generating a planning space based on the model and global environmental constraint information includes: Detailed two-dimensional information of the above model constraints and deck environment constraints is obtained by binarizing the image, and the image pixels are used as grid units; In the binary image, the black area represents the prohibited area, the pixel value is 0, and the white area represents the feasible area, the pixel value is 1. The processed image is expanded to obtain the planning space H o .

5. The real-time path planning algorithm for ship deck equipment based on the adjustable fast step leveling method according to claim 4 is characterized in that: In step S2, the step of obtaining the optimal cost value of each grid in the entire planning space includes: The fastest propagation path of the wave from the source point to the target point in the expansion of the water wave is regarded as the optimal path, and a time function based on wave propagation is established; Based on the wave source expansion principle, the following eikonal equation is established in the planning space H o The arrival time T of the wave at each point in space is obtained; Among them, (x, y) represents the planning space H o One point in is the gradient of the arrival time T(x,y), and V(x,y) is the local propagation velocity of the wave at (x,y).

6. The real-time path planning algorithm for ship deck equipment based on the adjustable fast step leveling method according to claim 5 is characterized in that: In step S3, it includes: By using the upwind difference method, the T value of the point (x, y) is solved, and its neighborhood is a point set containing four points (x+Δx, y), (x-Δx, y), (x, y+Δy), (x, y-Δy); Based on the gradient discretization method, Discretize and derive the following equation: in, The grid spacing in the x and y directions is Δx and Δy, let: For the discrete two-dimensional space, the eikonal equation can be rewritten as: Therefore, the equation can be solved as follows: When there is a single wave source, the minimum arrival time is at the wave source. For multiple wave sources, each wave source corresponds to its own minimum arrival time. Select the grid with small replacement value, that is, the point with small arrival time, and connect them to get the optimal path from the target point to the starting point.

7. The real-time path planning algorithm for ship deck equipment based on the adjustable fast step leveling method according to claim 6 is characterized in that: In step S4, it includes: By setting a preset distance as a threshold, when obtaining the hazard map, the wave surface with the prohibited area as the source point is only extended to this distance, and the hazard map corresponding to the feasible area that has not been extended is set to the hazard map value corresponding to this distance. This distance is called the extended distance D X , and its corresponding hazard map value is M X ; The final updated danger map is H M,DX ; The hazard map value is less than M X The feasible region is called the expansion region; When the extended distance D X When it decreases, the expansion area also decreases, and the planned path will be closer to the prohibited area, which reduces the safety margin and the path length; On the contrary, when the extension distance D X When it increases, the expansion area increases, and the planned path will be away from obstacles, which increases the safety margin and the path length; When the extended distance D X When it is equal to 0, the expansion area is reduced to the minimum, and the adjustable fast stepping method is the fast stepping method; When the extended distance D X When it is greater than the maximum distance from the feasible area to the prohibited area, the extended area is expanded to the maximum, and the adjustable fast step leveling method is the fast step leveling method; To clarify the extended distance D of the proposed adjustable fast step leveling method X The selection basis is to design a cost function for evaluating the planned path according to the optimization objective function of the intelligent optimization algorithm: In the formula, and are the path length cost index and collision risk index respectively, ω1 and (1-ω1) are their weight coefficients; Cost function J path The smaller the value of, the better the planned path. Path length cost metric and collision risk index The calculation formula is as follows: In the formula, is the planning path length, D s is the Euclidean distance from the starting point to the end point, N is the number of sampling points of the planned path, d SDA For safe meeting distance, D path is the minimum Euclidean distance from the sampling point on the planned path to the prohibited area, and when D pat h>d sDA When D pat h=d SDA .

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