Path searching method and device for unmanned aerial vehicle

Abstract

The invention provides a path searching method and device for an unmanned aerial vehicle. The method comprises the following steps: acquiring a terrain elevation threat, a radar detection threat and a firepower threat suffered by the unmanned aerial vehicle in any space coordinate in a flight area; constructing a dynamic threat model based on terrain elevation threats, radar detection threats and firepower threats to determine a comprehensive threat at any space coordinate; determining a punishment value at any space coordinate according to the comprehensive threat and a preset threat punishment coefficient; and performing path search on the flight area according to the maximum flight angle and the penalty value of the unmanned aerial vehicle to obtain a target path. According to the scheme, the problem of dynamic path searching of the unmanned aerial vehicle in a complex scene is solved, and the path searching capability and searching efficiency of the unmanned aerial vehicle in a complex environment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle route search, and particularly to a path search method and device for an unmanned aerial vehicle. Background Art

[0002] With the development of unmanned aerial vehicle technology, the flight autonomous planning ability of unmanned aerial vehicles has attracted more and more attention from scientific research personnel. However, as the complex environment faced by unmanned aerial vehicles becomes more complex and changeable, comprehensively evaluating the threats to the flight area of unmanned aerial vehicles and determining the safe flight area of unmanned aerial vehicles based on the evaluation results is one of the important means to improve flight safety.

[0003] In a real environment, different factors have different degrees of influence on the flight safety of unmanned aerial vehicles. In addition, with the change of flight position, state, and tasks, the influence degree and importance of different factors on unmanned aerial vehicles are also constantly changing accordingly. However, the traditional path search methods for unmanned aerial vehicles mainly have two deficiencies. One is that the threat factors such as terrain elevation, radar detection, and firepower threat that may be encountered on the flight path of the aircraft are not fully considered. The other is that the fixed weight algorithm is adopted and will not change with the change of the scene, which is not applicable to the scenes where the threat is closely related to the flight state of the aircraft; moreover, it is difficult to meet the requirements for the planning of three-dimensional trajectories in actual scenes. Summary of the Invention

[0004] Embodiments of the present invention provide a path search method and device for an unmanned aerial vehicle. The method comprehensively considers threat factors such as terrain elevation, radar detection, and firepower threat that may be encountered during the flight of the unmanned aerial vehicle, and can adjust the threat weights in real time under different flight states and task conditions in a three-dimensional scene, improving the dynamic perception ability of the threat area during the path search of the unmanned aerial vehicle.

[0005] In a first aspect, embodiments of the present invention provide a path search method for an unmanned aerial vehicle, including:

[0006] Obtaining the terrain elevation threat, radar detection threat, and firepower threat received by any spatial coordinate of the unmanned aerial vehicle in the flight area;

[0007] Based on the terrain elevation threat, the radar detection threat, and the firepower threat, constructing a dynamic threat model to determine the comprehensive threat at any spatial coordinate;

[0008] Determining the penalty value at any spatial coordinate according to the comprehensive threat and a preset threat penalty coefficient;

[0009] Performing path search on the flight area according to the maximum flight angle of the unmanned aerial vehicle and the penalty value to obtain a target path.

[0010] Optionally, the terrain elevation threat is determined by the following method:

[0011] For any spatial coordinate, the following operations are performed: obtain the highest elevation and the lowest elevation at this spatial coordinate, determine the first difference between the highest elevation and the lowest elevation, determine the second difference between the height of this spatial coordinate and the highest elevation, and use the ratio of the first difference to the second difference as the terrain elevation threat of this spatial coordinate.

[0012] Optionally, the radar detection threat is determined by the following method:

[0013] For any spatial coordinate, the following operations are performed:

[0014] Obtain the maximum visible slant range of the radar at the current scanning angle and the first slant range between this spatial coordinate and the radar;

[0015] When the maximum visible slant range is greater than the first slant range, determine the radar detection threat of the radar to this spatial coordinate according to the maximum visible slant range and the first slant range.

[0016] Optionally, the fire threat is determined by the following method:

[0017] For any spatial coordinate, the following operations are performed:

[0018] Obtain the effective strike range of the ground fire equipment and the second slant range between this spatial coordinate and the ground fire equipment;

[0019] When the second slant range is within the effective strike range, determine the fire threat of the ground fire equipment to this spatial coordinate according to the second slant range and the upper limit value of the effective strike range.

[0020] Optionally, based on the terrain elevation threat, the radar detection threat, and the fire threat, a dynamic threat model is constructed to determine the comprehensive threat at any spatial coordinate, including:

[0021] Construct a hybrid variable weight function for characterizing the threat evaluation value of threat factors; wherein, the threat factors include the terrain elevation threat, the radar detection threat, and the fire threat;

[0022] Determine the state variable weight vector based on the hybrid variable weight function;

[0023] Obtain the initial weight value of each threat factor;

[0024] Use the state variable weight vector and the initial weight value to determine the corrected weight value of each threat factor;

[0025] Sum the product of each of the said threat factors and its correction weight value to obtain the comprehensive threat at any spatial coordinate, thereby completing the construction of the dynamic threat model.

[0026] Optionally, determining the penalty value at any spatial coordinate according to the comprehensive threat and a preset threat penalty coefficient includes:

[0027] For any spatial coordinate, sum the product of the comprehensive threat at this spatial coordinate and the preset threat penalty coefficient and a heuristic factor to obtain the penalty value at this spatial coordinate.

[0028] Optionally, searching for a target path in the flight area according to the maximum flight angle of the unmanned aerial vehicle and the penalty value includes:

[0029] S1. Initialize a starting point, an ending point, the heuristic function value of the starting point, and the path cost value in the flight area, and assign an initial value to the heuristic factor;

[0030] S2. Put the starting point into the first list; and determine an objective function for calculating the comprehensive priority of a node according to the path cost value from the starting point to the node in the flight area, the heuristic function value from this node to the ending point, and the penalty value of the spatial coordinate of this node; wherein, the spatial coordinates of different nodes are different;

[0031] S3. Use the objective function to calculate the comprehensive priority of each node in the first list, and delete the current node corresponding to the minimum comprehensive priority from the first list and add it to the second list;

[0032] S4. Traverse the adjacent nodes of the current node in the flight area. When the difference in the path cost value between the adjacent node and the current node is less than the path length between the two, determine whether the adjacent node is in the second list. If so, add this adjacent node to the third list; otherwise, add the adjacent node to the first list; wherein, the adjacent node and the current node satisfy the limitation of the maximum flight angle of the unmanned aerial vehicle;

[0033] S5. Determine whether the comprehensive priority of the ending point is greater than the minimum comprehensive priority in the current first list; if so, return to step S3, otherwise execute step S6;

[0034] S6. Determine whether the heuristic factor does not exceed 1 or the path converges; if so, end the current process and output the target path; otherwise, execute step S7;

[0035] S7. Call the descent function to update the heuristic factor, obtain the updated heuristic factor, incorporate the third list into the first list, update the penalty values of the nodes in the first list according to the updated heuristic factor, and return to step S3.

[0036] Second aspect, an embodiment of the present invention further provides a path search device for an unmanned aerial vehicle, including:

[0037] A threat acquisition module, configured to acquire the terrain elevation threat, radar detection threat, and fire threat suffered by any spatial coordinate of the unmanned aerial vehicle in the flight area;

[0038] A model construction module, configured to construct a dynamic threat model based on the terrain elevation threat, the radar detection threat, and the fire threat to determine the comprehensive threat at any spatial coordinate;

[0039] A search module, configured to determine the penalty value at any spatial coordinate according to the comprehensive threat and a preset threat penalty coefficient; and perform path search on the flight area according to the maximum flight angle of the unmanned aerial vehicle and the penalty value to obtain a target path.

[0040] Third aspect, an embodiment of the present invention further provides a computing device, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the path search method for the unmanned aerial vehicle described in any one of the above is implemented.

[0041] Fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed on a computer, the computer is made to execute the path search method for the unmanned aerial vehicle described in any one of the above.

[0042] Fifth aspect, an embodiment of the present invention further provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the steps of the method described in any first aspect of this specification are implemented.

[0043] An embodiment of the present invention provides a path search method and device for an unmanned aerial vehicle. First, the terrain elevation threat, radar detection threat, and fire threat are comprehensively considered, and a dynamic threat model of the unmanned aerial vehicle is established based on these three threat factors. Finally, an algorithm is used to perform path search on the threat map of the flight area, and the flight performance of the unmanned aerial vehicle is incorporated into the path search constraints, enabling it to plan a path that conforms to the actual trajectory of the unmanned aerial vehicle. In this way, the present invention solves the problem of dynamic path search of unmanned aerial vehicles in complex scenarios, improves the path search ability and search efficiency of unmanned aerial vehicles in complex environments, significantly reduces the threatened degree of the planned path, and provides guarantee for the aerial protection of unmanned aerial vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 is a flowchart of a path search method for an unmanned aerial vehicle provided by an embodiment of the present invention;

[0046] Figure 2 is a two-dimensional threat numerical map of a flight area provided by an embodiment of the present invention;

[0047] Figure 3 is a hardware architecture diagram of a computing device provided by an embodiment of the present invention;

[0048] Figure 4 is a structural diagram of a path search device for an unmanned aerial vehicle provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0050] The following is the concept of the present invention. As Figure 1 shown, an embodiment of the present invention provides a path search method for an unmanned aerial vehicle. The method includes:

[0051] Step 100: Obtain the terrain elevation threat, radar detection threat, and fire threat suffered by the unmanned aerial vehicle at any spatial coordinate in the flight area;

[0052] Step 102: Based on the terrain elevation threat, radar detection threat, and fire threat, construct a dynamic threat model to determine the comprehensive threat at any spatial coordinate;

[0053] Step 104: Determine the penalty value at any spatial coordinate according to the comprehensive threat and the preset threat penalty coefficient;

[0054] Step 106: Perform path search on the flight area according to the maximum flight angle of the unmanned aerial vehicle and the penalty value to obtain the target path.

[0055] In this embodiment of the present invention, terrain elevation threats, radar detection threats, and firepower threats are comprehensively considered. A dynamic threat model for the UAV is established based on these three threat factors. Finally, an algorithm is used to perform path search on a threat map of the flight area. The UAV's flight performance is also incorporated into the path search constraints, enabling the planning of a path that conforms to the actual UAV trajectory. In this way, the present invention solves the problem of dynamic path search for UAVs in complex scenarios, improves the path search capability and efficiency of UAVs in complex environments, significantly reduces the threat level of the planned path, and provides assurance for UAV aerial protection.

[0056] Described below Figure 1 How to perform the steps shown.

[0057] First, for step 100, the terrain elevation threat is determined by the following method:

[0058] For any spatial coordinate, the following steps are performed: obtaining the highest altitude and the lowest altitude at the spatial coordinate, determining a first difference between the highest altitude and the lowest altitude, determining a second difference between the height of the spatial coordinate and the highest altitude, and taking the ratio of the first difference to the second difference as the terrain elevation threat of the spatial coordinate.

[0059] Specifically, the terrain elevation threat is determined by the following formula:

[0060]

[0061] Where, T g (X, Y, Z) represents the terrain elevation threat to the UAV at the spatial coordinates X, Y, Z; h max (X,Y) represents the highest altitude at X,Y, h min (X, Y) represents the lowest altitude at X, Y. In the present invention, the numerator in formula (1) represents the maximum altitude difference at X, Y, i.e., the first difference. When the terrain is steep and the altitude difference is large, the terrain elevation threat also increases accordingly.

[0062] For step 100, radar detection threats are determined by the following method:

[0063] For any spatial coordinate, execute:

[0064] Obtain the maximum visible slant range of the radar at the current scanning angle and the first slant range between the spatial coordinate and the radar;

[0065] When the maximum visual slant range is greater than the first slant range, the radar detection threat to the spatial coordinate is determined according to the maximum visual slant range and the first slant range.

[0066] Specifically, assume that the maximum visible slant range of the radar at the current scanning angle θ is d max (X, Y, Z),

[0067]

[0068] wherein, T R (X, Y, Z) represents the radar detection threat of the unmanned aerial vehicle at the spatial coordinates X, Y, Z; L r and L a are respectively the distances from the radar and the unmanned aerial vehicle to the center of the Earth ellipsoid; β 障 represents the angle formed by the straight line from the top of the radar to the top of the obstacle and the line connecting the radar to the center of the Earth; d R (X, Y, Z) is the first slant range between the unmanned aerial vehicle and the radar at the spatial coordinates X, Y, Z. In the present invention, when the unmanned aerial vehicle is within the visible range of the radar, the radar detection threat value exponentially decays with the increase of the distance; when the unmanned aerial vehicle exceeds the maximum visible distance of the radar, the radar detection threat value is zero, reflecting the protective effect of terrain occlusion.

[0069] In a preferred embodiment, if the spatial coordinates are within the visible ranges of several radars, the radar detection threat of the spatial coordinates is the sum of the radar detection threats of several radars to the spatial coordinates.

[0070] For step 100, the fire threat is determined by the following method:

[0071] For any spatial coordinates, the following operations are performed:

[0072] Obtain the effective strike range of the ground fire equipment and the second slant range between the spatial coordinates and the ground fire equipment; [[ID=^{33}]]

[0073] When the second slant range is within the effective strike range, determine the fire threat of the ground fire equipment to the spatial coordinates according to the second slant range and the upper limit value of the effective strike range.

[0074] Specifically, the parameters of the ground fire equipment include its coordinate position and range, and its effective strike range has an upper limit and a lower limit, denoted by d min and d max respectively. Assume that the coverage range of the fire threat is a hollow sphere, with its coordinate as the center of the sphere, and the radius between the minimum range and the maximum range forms a hemisphere. The points within this range are fire threat points, and their threat levels are modeled as follows:

[0075]

[0076] wherein, T AA(X, Y, Z) represents the fire threat value of the unmanned aerial vehicle at spatial coordinates X, Y, Z, d AA (X, Y, Z) is the second oblique distance between the unmanned aerial vehicle and the ground fire equipment at spatial coordinates X, Y, Z.

[0077] In a preferred embodiment, if the spatial coordinates are within the effective strike range of several ground fire equipment, the fire threat at the spatial coordinates is the sum of the fire threats of several ground fire equipment to the spatial coordinates.

[0078] In a preferred embodiment, step 102 constructs a dynamic threat model based on terrain elevation threat, radar detection threat, and fire threat to determine the comprehensive threat at any spatial coordinate, including:

[0079] Construct a hybrid variable weight function for characterizing the threat evaluation value of threat factors; wherein, the threat factors include terrain elevation threat, radar detection threat, and fire threat;

[0080] Determine the state variable weight vector based on the hybrid variable weight function;

[0081] Obtain the initial weight value of each threat factor;

[0082] Use the state variable weight vector and the initial weight value to determine the corrected weight value of each threat factor;

[0083] Sum the product of each threat factor and its corrected weight value to obtain the comprehensive threat at any spatial coordinate, and complete the construction of the dynamic threat model.

[0084] Specifically, when the unmanned aerial vehicle actually performs tasks, the main threats faced by different types and different stages of tasks are often different. Therefore, the present invention uses the variable weight theory to comprehensively model the above terrain elevation threat, radar detection threat, and fire threat, and gives their initial weight values w g 、w R 、w AA , and the weight factor w j ' after variable weight correction is calculated as follows:

[0085]

[0086] Where, B represents the hybrid variable weight function; S X is the state variable weight vector; T g is the terrain elevation threat; T R is the radar detection threat; T AA is the fire threat; ⊙ is the Hadamard product; I = 3, i, j ∈ (g, R, AA), and i ≠ j. For example, the corrected weight value w g' is:

[0087]

[0088] The dynamic threat model is determined by the following formula:

[0089] T 总 = w' g T g + w' R T R + w' AA T AA (9)

[0090] Wherein, T 总 is the comprehensive threat at any spatial coordinate.

[0091] In a preferred embodiment, step 104 determines the penalty value at any spatial coordinate according to the comprehensive threat and the preset threat penalty coefficient, including:

[0092] For any spatial coordinate, the sum of the product of the comprehensive threat of this spatial coordinate and the preset threat penalty coefficient and the heuristic factor is obtained to get the penalty value at this spatial coordinate.

[0093] Specifically, in order to avoid the unmanned aerial vehicle flying in a high-threat area, a threat-based position penalty mechanism is introduced in the search process of the ARA* algorithm. Specifically, each spatial coordinate point will obtain a penalty value due to the comprehensive threat at this point, and this penalty value will increase the heuristic factor when calculating the comprehensive priority in the path search process. Let the penalty value ε s at the spatial coordinate s be:

[0094] ε s = ε * 1 + k * T 总 (s) (10)

[0095] Wherein, ε s is the penalty value at the spatial coordinate s; T 总 (s) is the comprehensive threat at the spatial coordinate s; k is the preset threat penalty coefficient; ε is the heuristic factor. Therefore, the comprehensive threat is positively correlated with the penalty value, and the subsequent calculated comprehensive priority is also positively correlated with the penalty value, so as to avoid path search in areas with greater threats as much as possible.

[0096] In a preferred embodiment, step 106 performs path search on the flight area according to the maximum flight angle of the unmanned aerial vehicle and the penalty value to obtain the target path, including:

[0097] S1, initialize the starting point, ending point, the heuristic function value of the starting point and the path cost value in the flight area, and assign an initial value to the heuristic factor;

[0098] S2, put the starting point into the first list; and determine an objective function for calculating the comprehensive priority of a node based on the path cost value from the starting point to the node in the flight area, the heuristic function value from the node to the end point, and the penalty value of the spatial coordinates of the node; wherein, the spatial coordinates of different nodes are different;

[0099] S3, use the objective function to calculate the comprehensive priority of each node in the first list, delete the current node corresponding to the minimum comprehensive priority from the first list, and add it to the second list;

[0100] S4, traverse the adjacent nodes of the current node in the flight area. When the difference in the path cost value between the adjacent node and the current node is less than the path length between the two, determine whether the adjacent node is in the second list. If so, add the adjacent node to the third list; otherwise, add the adjacent node to the first list; wherein, the adjacent node and the current node satisfy the maximum flight angle limit of the unmanned aerial vehicle;

[0101] S5, determine whether the comprehensive priority of the end point is greater than the minimum value of the comprehensive priority in the current first list; if so, return to step S3, otherwise execute step S6;

[0102] S6, determine whether the heuristic factor does not exceed 1 or the path converges; if so, end the current process and output the target path; otherwise, execute step S7;

[0103] S7, call the descent function to update the heuristic factor to obtain the updated heuristic factor, merge the third list into the first list, update the penalty value of each node in the first list according to the updated heuristic factor, and return to step S3.

[0104] Specifically, in step S2, the objective function is:

[0105] f(s) = g(s) + ε s *h(s) (11)

[0106] wherein, f(s) is the comprehensive priority of node s; g(s) is the path cost value from the starting point to node s; h(s) is the heuristic function value (i.e., the estimated cost value) from node s to the end point; ε s is the penalty value at the spatial coordinate s (i.e., for node s).

[0107] It should be noted that the first list is the OPEN list; the second list is the CLOSE list; the third list is the INCONS list. In step S4, traverse the adjacent nodes y around the current node x that meet the maximum flight angle limit. If the adjacent node y satisfies g(y) < g(x) + c(x, y) (where c(x, y) is the path length between the current node x and the adjacent node y; g(y) is the path cost from the starting point to node y; g(x) is the path cost from the starting point to node x), then it is necessary to determine whether the adjacent y is in the CLOSE list. If so, add it to the INCONS list; otherwise, add it to the OPEN list. In step S5, determine whether the f(t) value of the end point t is greater than the minimum f value in the OPEN list. If it is, return to step S3; otherwise, proceed to step S6. In step S6, if the heuristic factor ε ≤ 1 or g(t) is less than or equal to the nodes with g(s) + h(s) in all INCONS lists and OPEN lists, then output g(s). In step S7, call the descent function for the heuristic factor ε, merge the INCONS list into the OPEN list, and for the updated heuristic factor ε, calculate the penalty value ε of each point according to the threat coefficient T of each point 总 , and then update the f value in the OPEN list. s

[0108] Although there are also methods for dynamically modeling the threats to unmanned aerial vehicles based on variable weight methods, they greatly simplify the real scenario and can only perform threat calculations in a two-dimensional plane, making it difficult to meet the requirements for three-dimensional trajectory planning in actual scenarios. In the embodiments of the present invention, by comprehensively considering threat factors such as terrain elevation, radar detection, and firepower threat, dynamic path search of unmanned aerial vehicles in a three-dimensional scenario is realized, and by introducing a penalty value in combination with the maximum flight angle of the unmanned aerial vehicle, the efficiency of path search of the unmanned aerial vehicle in a complex environment is improved.

[0109] In a specific embodiment, the path search algorithm of step 106 of the present invention is compared with BFS, DFS, Dijkstra, and A* algorithms, and the comparison verification is carried out by comparing the success rate of path finding, the average path length, and the average running time index of the algorithms. Specifically, three sets of sub-experiments will be set up: easy, medium, and difficult. Among them, in the easy group sub-experiment, the Manhattan distance from the starting point to the ending point grid is [1, 10], and the maximum search time limit is set to 1 minute; in the medium group sub-experiment, the Manhattan distance from the starting point to the ending point grid is [11, 50], and the maximum search time limit is set to 2 minutes; in the difficult group sub-experiment, the Manhattan distance from the starting point to the ending point grid is [50, ∞], and the maximum search time limit is set to 5 minutes. In order to eliminate the random errors caused by factors such as hardware, this experiment conducts 100 repeated trials on the five algorithms in three cases, and the obtained data are as follows: For the easy group sub-experiment: The DFS algorithm did not give relevant data; the planning success rate of the BFS algorithm is 97%, the average search time consumption is 30.30 s, and the average search distance is 7979 m; the planning success rate of the Dijkstra algorithm is 100%, the average search time consumption is 21.71 s, and the average search distance is 8013 m; the planning success rate of the A* algorithm is 100%, the average search time consumption is 1.07 s, and the average search distance is 6967 m; the planning success rate of the algorithm of the present invention is 100%, the average search time consumption is 0.41 s, and the average search distance is 6215 m. For the medium group sub-experiment: The DFS algorithm did not give relevant data; the planning success rate of the BFS algorithm is 4%, the average search time consumption is 88.60 s, and the average search distance is 12041 m; the planning success rate of the Dijkstra algorithm is 3%, the average search time consumption is 88.59 s, and the average search distance is 14457 m; the planning success rate of the A* algorithm is 100%, the average search time consumption is 19.63 s, and the average search distance is 35323 m; the planning success rate of the algorithm of the present invention is 100%, the average search time consumption is 1.72 s, and the average search distance is 34765 m. For the difficult group sub-experiment: The DFS algorithm and the BFS algorithm did not give relevant data; the planning success rate of the A* algorithm is 25%, the average search time consumption is 80.65 s, and the average search distance is 85384 m; the planning success rate of the algorithm of the present invention is 98%, the average search time consumption is 10.50 s, and the average search distance is 128660 m. Obviously, it can be seen from the simulation results that the improved ARA* algorithm of the present invention has significant advantages in the path planning task, especially when dealing with complex tasks, its performance far exceeds other algorithms.

[0110] In order to more intuitively display the improved ARA* algorithm based on comprehensive threat assessment in the track planning effect, this article adopts a two-dimensional map for visual display. Figure 2A two-dimensional threat numerical map of the flight area is shown, where the starting coordinate of the trajectory planning is (109, 526) and the ending coordinate is (816, 120). In the figure, the fan-shaped area represents the radar detection threat, and the threat level gradually decreases from the center to the outside. Beyond the radius or angle range, the threat is zero. The color from dark brown to light yellow represents the size of the threat. The darker the color, the greater the threat. The circular area represents the fire threat, and the position of its center point is marked by a blue circle. The threat at the center of this threat area is the largest and gradually decreases outward until the threat is zero beyond the radius. The polygon shaded area represents the terrain elevation threat. There are three different polygon areas, and each polygon represents a mountainous area. The points within the polygon area are always regarded as areas with a threat of 1.

[0111] Figure 2 The two paths respectively represent the paths obtained by using the improved ARA* algorithm (orange line) and the ordinary ARA* algorithm (green line) proposed by the present invention. By comparison, it can be found that although the ordinary ARA* algorithm can plan a shorter path, this path passes through two fire threat areas and one radar detection threat area, and the risk level of this path is relatively high. The path planned by the improved ARA* algorithm proposed by the present invention can successfully avoid the threats of ground fire and radar detection, and at the same time can avoid flying over areas with relatively high altitudes to avoid the impact of terrain threats. At the same time, the rotation angles in this path are all less than 90°, which conforms to the movement trajectory of the unmanned spacecraft. Therefore, it can be proved that the present invention can plan a path with a smaller threat level.

[0112] As Figure 3 , Figure 4 shown, the embodiment of the present invention provides a path search device for an unmanned aircraft. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, as Figure 3 shown, it is a hardware architecture diagram of a computing device where the path search device for an unmanned aircraft provided by the embodiment of the present invention is located. In addition to Figure 4 the processor, memory, network interface, and non-volatile memory shown, the computing device where the device is located in the embodiment usually may also include other hardware, such as a forwarding chip responsible for processing packets, etc. Taking software implementation as an example, as Figure 4 shown, as a logically meaningful device, it is formed by the CPU of its corresponding computing device reading the corresponding computer program in the non-volatile memory into the memory and running. The path search device for an unmanned aircraft provided by this embodiment includes:

[0113] A threat acquisition module 400, configured to acquire the terrain elevation threat, radar detection threat, and fire threat suffered by an unmanned aircraft at any spatial coordinate in the flight area;

[0114] The model construction module 402 is used to construct a dynamic threat model based on terrain elevation threat, radar detection threat, and fire threat to determine the comprehensive threat at any spatial coordinate.

[0115] The search module 404 is used to determine the penalty value at any spatial coordinate according to the comprehensive threat and the preset threat penalty coefficient; and perform path search on the flight area according to the maximum flight angle of the unmanned aerial vehicle and the penalty value to obtain the target path.

[0116] In some specific embodiments, the threat acquisition module 400 can be used to execute the above-mentioned step 100, the model construction module 402 can be used to execute the above-mentioned step 102, and the search module 404 can be used to execute the above-mentioned steps 104 and 106.

[0117] In some specific embodiments, the threat acquisition module 400 is further used to perform the following operations:

[0118] For any spatial coordinate, the following operations are performed: obtain the highest altitude and the lowest altitude at this spatial coordinate, determine the first difference between the highest altitude and the lowest altitude, determine the second difference between the altitude of this spatial coordinate and the highest altitude, and use the ratio of the first difference to the second difference as the terrain elevation threat of this spatial coordinate.

[0119] In some specific embodiments, the threat acquisition module 400 is further used to perform the following operations:

[0120] For any spatial coordinate, the following operations are performed:

[0121] Obtain the maximum visible slant range of the radar at the current scanning angle and the first slant range between this spatial coordinate and the radar;

[0122] When the maximum visible slant range is greater than the first slant range, determine the radar detection threat of the radar to this spatial coordinate according to the maximum visible slant range and the first slant range.

[0123] In some specific embodiments, the threat acquisition module 400 is further used to perform the following operations:

[0124] For any spatial coordinate, the following operations are performed:

[0125] Obtain the effective strike range of the ground fire equipment and the second slant range between this spatial coordinate and the ground fire equipment;

[0126] When the second slant range is within the effective strike range, determine the fire threat of the ground fire equipment to this spatial coordinate according to the second slant range and the upper limit value of the effective strike range.

[0127] In some specific embodiments, the model construction module 402 is further configured to perform the following operations:

[0128] Construct a hybrid variable weight function for characterizing the threat evaluation value of threat factors; wherein, the threat factors include terrain elevation threat, radar detection threat, and firepower threat;

[0129] Determine a state variable weight vector based on the hybrid variable weight function;

[0130] Obtain the initial weight value of each threat factor;

[0131] Use the state variable weight vector and the initial weight value to determine the corrected weight value of each threat factor;

[0132] Sum the products of each threat factor and its corrected weight value to obtain the comprehensive threat at any spatial coordinate, and complete the construction of the dynamic threat model.

[0133] In some specific embodiments, the search module 404 is further configured to perform the following operations:

[0134] For any spatial coordinate, sum the product of the comprehensive threat at this spatial coordinate and the preset threat penalty coefficient and the heuristic factor to obtain the penalty value at this spatial coordinate.

[0135] In some specific embodiments, the search module 404 is further configured to perform the following operations:

[0136] S1. Initialize the starting point, ending point, the heuristic function value of the starting point, and the path cost value in the flight area, and assign an initial value to the heuristic factor;

[0137] S2. Put the starting point into the first list; and determine an objective function for calculating the comprehensive priority of this node according to the path cost value from the starting point to the node in the flight area, the heuristic function value from this node to the ending point, and the penalty value of the spatial coordinate of this node; wherein, the spatial coordinates of different nodes are different;

[0138] S3. Calculate the comprehensive priority of each node in the first list using the objective function, and delete the current node corresponding to the minimum comprehensive priority from the first list and add it to the second list;

[0139] S4. Traverse the adjacent nodes of the current node in the flight area. When the difference between the path cost values between the adjacent node and the current node is less than the path length between the two, determine whether the adjacent node is in the second list. If so, add this adjacent node to the third list; otherwise, add the adjacent node to the first list; wherein, the adjacent node and the current node satisfy the maximum flight angle limit of the unmanned aerial vehicle;

[0140] S5. Determine whether the comprehensive priority of the end point is greater than the minimum value of the comprehensive priority in the current first list. If it is, return to step S3; otherwise, execute step S6.

[0141] S6. Determine whether the heuristic factor does not exceed 1 or the path converges. If it is, end the current process and output the target path; otherwise, execute step S7.

[0142] S7. Call the descent function to update the heuristic factor to obtain the updated heuristic factor, incorporate the third list into the first list, update the penalty values of each node in the first list according to the updated heuristic factor, and return to step S3.

[0143] It can be understood that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation on a path search device for an unmanned aerial vehicle. In other embodiments of the present invention, a path search device for an unmanned aerial vehicle may include more or fewer components than those illustrated, or combine certain components, or split certain components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0144] Regarding the information interaction, execution process, etc. between the various modules within the above-mentioned device, since it is based on the same concept as the method embodiments of the present invention, the specific content can be referred to the description in the method embodiments of the present invention and will not be elaborated here.

[0145] The embodiments of the present invention also provide a computing device, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it implements a path search method for an unmanned aerial vehicle in any embodiment of the present invention.

[0146] The embodiments of the present invention also provide a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, the processor is enabled to execute a path search method for an unmanned aerial vehicle in any embodiment of the present invention.

[0147] The embodiments of the present application also provide a computer program product. The computer program product includes a computer program. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes a path search method for an unmanned aerial vehicle described in any of the above embodiments.

[0148] Specifically, a system or device equipped with a storage medium can be provided. A software program code for implementing the functions in any of the above embodiments is stored on the storage medium, and the computer (or CPU or MPU) of the system or device reads and executes the program code stored on the storage medium.

[0149] In this case, the program code read from the storage medium itself can implement the functions of any one of the above-described embodiments. Therefore, the program code and the storage medium storing the program code constitute a part of the present invention.

[0150] Examples of the storage medium for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as a CD-ROM, a CD-R, a CD-RW, a DVD-ROM, a DVD-RAM, a DVD-RW, a DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Optionally, the program code can be downloaded from a server computer via a communication network.

[0151] Furthermore, it should be clear that not only can the functions of any one of the above-described embodiments be achieved by executing the program code read by a computer, but also by causing an operating system or the like operating on the computer based on the instructions of the program code to complete part or all of the actual operations.

[0152] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in an expansion board inserted into the computer or into the memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU or the like installed on the expansion board or the expansion module is caused to execute part or all of the actual operations, thereby implementing the functions of any one of the above-described embodiments.

[0153] It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0154] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes various media such as a ROM, a RAM, a magnetic disk, or an optical disk that can store program code.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A path search method for an unmanned aerial vehicle, characterized in that, Including: Obtaining the terrain elevation threat, radar detection threat, and fire threat suffered by any spatial coordinate in the flight area of the unmanned aerial vehicle; Based on the terrain elevation threat, the radar detection threat, and the fire threat, constructing a dynamic threat model to determine the comprehensive threat at any spatial coordinate; Determining the penalty value at any spatial coordinate according to the comprehensive threat and a preset threat penalty coefficient; Performing path search on the flight area according to the maximum flight angle of the unmanned aerial vehicle and the penalty value to obtain a target path.

2. The method according to claim 1, wherein The terrain elevation threat is determined by the following method: For any spatial coordinate, perform: obtaining the highest altitude and the lowest altitude at this spatial coordinate, determining the first difference between the highest altitude and the lowest altitude, determining the second difference between the altitude of this spatial coordinate and the highest altitude, and taking the ratio of the first difference to the second difference as the terrain elevation threat of this spatial coordinate; And / or The radar detection threat is determined by the following method: For any spatial coordinate, perform: Obtaining the maximum visible slant range of the radar at the current scanning angle and the first slant range between this spatial coordinate and the radar; When the maximum visible slant range is greater than the first slant range, determining the radar detection threat of the radar to this spatial coordinate according to the maximum visible slant range and the first slant range.

3. The method according to claim 1, characterized in that, The fire threat is determined by the following method: For any spatial coordinate, perform: Obtaining the effective strike range of the ground fire equipment and the second slant range between this spatial coordinate and the ground fire equipment; When the second slant range is within the effective strike range, determining the fire threat of the ground fire equipment to this spatial coordinate according to the second slant range and the upper limit value of the effective strike range.

4. The method according to claim 1, wherein The constructing the dynamic threat model based on the terrain elevation threat, the radar detection threat, and the fire threat to determine the comprehensive threat at any spatial coordinate includes: Constructing a hybrid variable weight function for characterizing the threat evaluation value of threat factors; wherein the threat factors include the terrain elevation threat, the radar detection threat, and the fire threat; Determining a state variable weight vector based on the hybrid variable weight function; Obtaining the initial weight value of each threat factor; Using the state variable weight vector and the initial weight value to determine the corrected weight value of each threat factor; Performing a summation operation on the product of each threat factor and its corrected weight value to obtain the comprehensive threat at any spatial coordinate, and completing the construction of the dynamic threat model.

5. The method according to claim 1, wherein The determining the penalty value at any spatial coordinate according to the comprehensive threat and a preset threat penalty coefficient includes: For any spatial coordinate, summing the product of the comprehensive threat of this spatial coordinate and the preset threat penalty coefficient and a heuristic factor to obtain the penalty value at this spatial coordinate.

6. The method according to any one of claims 1 to 5, characterized in that The performing path search on the flight area according to the maximum flight angle of the unmanned aerial vehicle and the penalty value to obtain a target path includes: S1, initializing a starting point, an ending point, the heuristic function value of the starting point, and the path cost value in the flight area, and assigning an initial value to the heuristic factor; S2, put the starting point into the first list; and determine an objective function for calculating the comprehensive priority of a node based on the path cost value from the starting point to the node, the heuristic function value from the node to the end point, and the penalty value of the spatial coordinates of the node in the flight area; wherein, the spatial coordinates of different nodes are different; S3, use the objective function to calculate the comprehensive priority of each node in the first list, and delete the current node corresponding to the minimum comprehensive priority value from the first list and add it to the second list; S4, traverse the adjacent nodes of the current node in the flight area. When the difference in the path cost value between the adjacent node and the current node is less than the path length between the two, determine whether the adjacent node is in the second list. If so, add the adjacent node to the third list; otherwise, add the adjacent node to the first list; wherein, the adjacent node and the current node satisfy the limit of the maximum flight angle of the unmanned aerial vehicle; S5, determine whether the comprehensive priority of the end point is greater than the minimum value of the comprehensive priority in the current first list; if so, return to step S3, otherwise execute step S6; S6, determine whether the heuristic factor does not exceed 1 or the path converges; if so, end the current process and output the target path; otherwise execute step S7; S7, call the descent function to update the heuristic factor to obtain an updated heuristic factor, merge the third list into the first list, update the penalty value of each node in the first list according to the updated heuristic factor, and return to step S3.

7. A path search device for an unmanned aerial vehicle, characterized in that, Comprising: A threat acquisition module, configured to acquire the terrain elevation threat, radar detection threat, and fire threat suffered by the unmanned aerial vehicle at any spatial coordinate in the flight area; A model construction module, configured to construct a dynamic threat model based on the terrain elevation threat, the radar detection threat, and the fire threat to determine the comprehensive threat at any spatial coordinate; A search module, configured to determine the penalty value at any spatial coordinate according to the comprehensive threat and a preset threat penalty coefficient; And perform path search on the flight area according to the maximum flight angle of the unmanned aerial vehicle and the penalty value to obtain a target path.

8. A computing device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method according to any one of claims 1-6 is implemented.

9. A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed in a computer, the computer is made to execute the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Comprising computer instructions, and when the computer instructions are executed by a processor, the steps of the method according to any one of claims 1-6 are implemented.

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