Three-dimensional unmanned aerial vehicle path planning method and device based on improved A star algorithm

Abstract

The invention provides a three-dimensional unmanned aerial vehicle path planning method and device based on an improved A star algorithm. The method comprises the steps that the actual cost of an unmanned aerial vehicle from a starting node to a current node is determined based on the voyage cost, the height cost and the terrain threat cost of the unmanned aerial vehicle from the starting node to the current node, and the voyage cost is used for evaluating the cost consumption brought by the flight path of the unmanned aerial vehicle; the height cost is used for evaluating the cost consumption caused by the flight height change of the unmanned aerial vehicle, and the terrain threat cost is used for evaluating the cost consumption caused by the risk degree of a mountain and a no-fly airspace to unmanned aerial vehicle path planning; and based on the actual cost, the path of the unmanned aerial vehicle from the starting node to the target node is judged and planned by using an A star algorithm and node redundancy. According to the method, unmanned plane path planning is carried out by integrating height cost, terrain threat cost and the like, a three-dimensional unmanned plane path with less flight energy consumption can be obtained, redundant nodes are deleted, and a smoother path with less energy consumption is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a three-dimensional unmanned aerial vehicle path planning method and device based on an improved A-star algorithm. Background Art

[0002] With the rapid development of drone technology, it has shown great potential in many fields such as logistics distribution, environmental monitoring and disaster relief.

[0003] However, after path planning is extended from two-dimensional space to three-dimensional space, the path search space is significantly expanded, and the factors to be faced or considered in the search process are more complex and diverse. How to efficiently and accurately plan the flight path of drones in a complex three-dimensional space environment has become one of the key technical issues restricting the application of drones. The current three-dimensional drone path planning scheme consumes too much energy for drones, which leads to increased costs. Summary of the invention

[0004] The present invention provides a three-dimensional unmanned aerial vehicle path planning method and device based on an improved A-star algorithm, which are used to solve the technical problem of high cost of three-dimensional unmanned aerial vehicle path planning in the prior art.

[0005] In a first aspect, the present invention provides a three-dimensional UAV path planning method based on an improved A-star algorithm, comprising the following steps.

[0006] The actual cost of the UAV from the starting node to the current node is determined based on the range cost, altitude cost and terrain threat cost of the UAV from the starting node to the current node; the range cost is used to evaluate the cost consumption caused by the flight track of the UAV; the altitude cost is used to evaluate the cost consumption caused by the change of the flight altitude of the UAV; the terrain threat cost is used to evaluate the cost consumption caused by the danger of mountains and no-fly airspace on the path planning of the UAV; Based on the actual cost, the path of the UAV from the starting node to the target node is planned using the A-star algorithm and node redundancy judgment.

[0007] In some embodiments, determining the actual cost of the drone from the starting node to the current node based on the range cost, altitude cost, and terrain threat cost of the drone from the starting node to the current node includes: Determine weight coefficients corresponding to the range cost, the altitude cost, and the terrain threat cost respectively; According to the weight coefficients corresponding to the range cost, the height cost and the terrain threat cost respectively, the range cost, the height cost and the terrain threat cost are weightedly summed to obtain the actual cost of the UAV from the starting node to the current node.

[0008] In some embodiments, the method further comprises: Determine the danger level of the mountain to the drone based on the radius of the mountain and the distance from the drone to the center of the mountain, and determine the danger level of the no-fly airspace to the drone based on the range of the no-fly airspace and the distance from the drone to the center of the no-fly airspace; The terrain threat cost is determined based on the danger level of the mountain to the UAV and the danger level of the no-fly airspace to the UAV.

[0009] In some embodiments, determining a weight coefficient corresponding to the terrain threat cost includes: Based on the danger level of the mountain to the UAV and the danger level of the no-fly airspace to the UAV, an exponential function is used to calculate the weight of the terrain threat cost.

[0010] In some embodiments, the planning of a path of the drone from the starting node to the target node based on the actual cost using the A-star algorithm and node redundancy judgment includes: Based on the actual cost, the A-star algorithm is used to search in nodes to obtain the initial path of the UAV from the initial node to the target node; the nodes in the initial path are stored in the initial path list closelist; Redundancy judgment and deletion are performed on the nodes in the closelist to obtain a target path list finallist.

[0011] In some embodiments, the redundancy determination and deletion of nodes in the closelist includes: When there are three or more consecutive nodes on the same straight line in the closelist, determine the nodes on the same straight line except the nodes at both ends of the straight line as redundant nodes, and delete the redundant nodes from the closelist; and / or

[0012] When there are two discontinuous nodes in the closelist whose connection line does not pass through the no-fly airspace, and the smoothness of the initial path between the two discontinuous nodes does not reach a preset threshold, the node between the two discontinuous nodes is determined to be a redundant node, and the redundant node is deleted from the closelist.

[0013] In some embodiments, the method further comprises: Determine whether the line connecting any two adjacent nodes in the closelist passes through a no-fly airspace; In the case where a line connecting any two adjacent nodes in the closelist passes through a no-fly airspace, it is determined that the initial path is invalid.

[0014] In a second aspect, the present invention provides a three-dimensional UAV path planning device based on an improved A-star algorithm, comprising the following modules.

[0015] The actual cost determination module is used to determine the actual cost of the drone from the starting node to the current node based on the range cost, altitude cost and terrain threat cost of the drone from the starting node to the current node; the range cost is used to evaluate the cost consumption caused by the flight track of the drone; the altitude cost is used to evaluate the cost consumption caused by the change of the flight altitude of the drone; the terrain threat cost is used to evaluate the cost consumption caused by the danger of mountains and no-fly airspace on the drone path planning; The path planning module is used to plan the path of the UAV from the starting node to the target node based on the actual cost by using the A-star algorithm and node redundancy judgment.

[0016] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the three-dimensional unmanned aerial vehicle path planning method based on the improved A-star algorithm as described in the first aspect above is implemented.

[0017] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the three-dimensional unmanned aerial vehicle path planning method based on the improved A-star algorithm as described in the first aspect above.

[0018] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the three-dimensional unmanned aerial vehicle path planning method based on the improved A-star algorithm as described in the first aspect above.

[0019] The three-dimensional unmanned aerial vehicle path planning method and device based on the improved A-star algorithm provided by the present invention take into account the additional cost caused by the altitude change in the three-dimensional environment, and determine the actual cost consumption of the unmanned aerial vehicle by comprehensively considering the altitude cost and the terrain threat cost. Specifically, the actual cost of the unmanned aerial vehicle from the starting node to the current node is determined based on the range cost, altitude cost and terrain threat cost of the unmanned aerial vehicle from the starting node to the current node, wherein the range cost is used to evaluate the cost consumption caused by the flight track of the unmanned aerial vehicle, the altitude cost is used to evaluate the cost consumption caused by the change of the flight altitude of the unmanned aerial vehicle, and the terrain threat cost is used to evaluate the cost consumption caused by the danger of mountains and no-fly airspace on the path planning of the unmanned aerial vehicle; then based on the actual cost, the A-star algorithm and node redundancy judgment are used to plan the path of the unmanned aerial vehicle from the starting node to the target node, so as to obtain a three-dimensional unmanned aerial vehicle path with less flight energy consumption, delete redundant nodes, obtain a smoother and less energy-consuming path, and extend the flight time of the unmanned aerial vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is one of the flow charts of the three-dimensional UAV path planning method based on the improved A-star algorithm provided by the present invention.

[0022] Figure 2 This is the second flow chart of the three-dimensional UAV path planning method based on the improved A-star algorithm provided by the present invention.

[0023] Figure 3 It is one of the initial path schematic diagrams of the example scenarios of the three-dimensional UAV path planning method based on the improved A-star algorithm provided by the present invention.

[0024] Figure 4 This is the second initial path schematic diagram of the example scenario of the three-dimensional unmanned aerial vehicle path planning method based on the improved A-star algorithm provided by the present invention.

[0025] Figure 5 It is a structural schematic diagram of a three-dimensional UAV path planning device based on an improved A-star algorithm provided by the present invention.

[0026] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0027] Currently, the A-star algorithm (i.e. A-star algorithm is used to plan the path of a 3D UAV. A-star algorithm is a heuristic search algorithm that heuristically searches for nodes and constructs the optimal path by continuously evaluating the evaluation function value of the path in the search space. The commonly used evaluation function of A-star algorithm is:

[0028] in, n is the current node; is the total cost of the UAV from the initial node to the target node; For the drone from the starting node to the current node n the actual cost of For the drone from the current node n The estimated cost to reach the target node.

[0029] As the cornerstone of the path planning field, the classic A-star algorithm has been widely studied and applied due to its high efficiency and scalability. However, when it is directly applied to three-dimensional space, it faces the following challenges: First, the dimension increases. The path search space is expanded from two dimensions to three dimensions, and the path search space is significantly expanded, resulting in a sharp increase in computational complexity; second, the dynamic environment. The dynamic changes of obstacles in the real environment require the algorithm to have the ability to respond quickly and adjust the path in time; third, energy efficiency. The endurance of drones is limited, and path planning needs to consider flight energy consumption to reduce unnecessary energy consumption.

[0030] The three-dimensional path planning method based on the traditional A-star algorithm does not fully consider the three-dimensional space characteristics and the operation limitations of the UAV, and has the following main defects: Underestimation: Simple 3D distance estimates may ignore the additional costs of altitude changes, resulting in uneconomical paths.

[0031] Rigid obstacle handling: The response to dynamic obstacles is not flexible enough, which may easily make the planned path infeasible.

[0032] Poor path quality: There is a lack of effective path smoothing mechanism, and the generated path may have too many sharp turns, affecting flight stability and energy consumption.

[0033] Based on the above technical problems, the present invention proposes a three-dimensional UAV path planning method based on an improved A-star algorithm, which takes into account the additional cost brought by the altitude change in the three-dimensional environment, and determines the actual cost consumption of the UAV by comprehensively considering the altitude cost and terrain threat cost. Specifically, the actual cost of the UAV from the starting node to the current node is determined based on the range cost, altitude cost and terrain threat cost of the UAV from the starting node to the current node, wherein the range cost is used to evaluate the cost consumption brought by the flight track of the UAV, the altitude cost is used to evaluate the cost consumption brought by the change of the flight altitude of the UAV, and the terrain threat cost is used to evaluate the cost consumption brought by the danger of mountains and no-fly airspace on the UAV path planning; then based on the actual cost, the A-star algorithm and node redundancy judgment are used to plan the path of the UAV from the starting node to the target node, so as to obtain a three-dimensional UAV path with less flight energy consumption, and delete redundant nodes to obtain a smoother and less energy-consuming path, thereby extending the flight time of the UAV and bringing a better user experience.

[0034] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Figure 1 This is one of the flow charts of the three-dimensional UAV path planning method based on the improved A-star algorithm provided by the present invention, such as Figure 1 As shown, the present invention provides a three-dimensional UAV path planning method based on an improved A-star algorithm. The method includes the following steps 101 and 102.

[0036] Step 101: When planning the path of a UAV, there is often a requirement for the height of the flight path. In order to cope with the path planning of different tasks, height evaluation is introduced into the path evaluation function.

[0037] The actual cost of the UAV from the starting node to the current node is determined based on the range cost, altitude cost and terrain threat cost of the UAV from the starting node to the current node; the range cost is used to evaluate the cost consumption caused by the flight track of the UAV; the altitude cost is used to evaluate the cost consumption caused by the change of the flight altitude of the UAV; the terrain threat cost is used to evaluate the cost consumption caused by the danger of mountains and no-fly airspace on the UAV path planning.

[0038] Specifically, when performing three-dimensional UAV path planning from the starting node to the target node, the goal is to reduce the UAV flight energy consumption, and the UAV flight energy consumption can be evaluated using the actual cost (function). In the embodiment of the present application, the actual cost of the UAV from the starting node to the current node is determined based on the UAV's range cost, altitude cost, and terrain threat cost from the starting node to the current node.

[0039] Among them, the range cost is used to evaluate or reflect the cost consumption brought by the UAV's flight track (or distance), the altitude cost is used to evaluate the cost consumption brought by the change of the UAV's flight altitude, and the terrain threat cost is used to evaluate the cost consumption brought by the danger of mountains and no-fly airspace on the UAV's path planning.

[0040] Step 102: Based on the actual cost, the path of the UAV from the starting node to the target node is planned using the A-star algorithm and node redundancy judgment.

[0041] Specifically, after obtaining the actual cost, the path of the UAV from the starting node to the target node is planned based on the actual cost and the A-star algorithm and node redundancy judgment. That is, the total cost of the UAV from the starting node to the target node is first determined based on the actual cost, and then a UAV path with less energy consumption and smoother is planned based on the total cost combined with the A-star algorithm and node redundancy judgment.

[0042] In the A-star algorithm, the total cost of the drone from the starting node to the target node is determined based on the actual cost and the estimated cost. The actual cost is the actual flight cost of the drone from the starting node to the current node, and the estimated cost is the estimated flight cost of the drone from the current node to the target node.

[0043] In the embodiment of the present application, the estimated cost (i.e., the heuristic function) can be the Euclidean distance from the current node to the target node of the drone, and its expression is:

[0044] in, is the estimated cost of the UAV from the current node to the target node; is the three-dimensional coordinate of the current node; is the 3D coordinate of the target node.

[0045] In the embodiment of the present application, taking into account that during the path search process, the actual cost and estimated cost of the drone have different effects on path planning, a weighted evaluation method is selected to calculate the total cost.

[0046] For example, let the current node be n, and the total cost of the current node in the A-star algorithm is:

[0047] in, is the total cost of the UAV from the initial node to the target node; is the actual cost of the drone from the starting node to the current node; is the estimated cost of the UAV from the current node to the target node; is the weight of the actual cost, is the weight of the estimated cost and satisfies .

[0048] Furthermore, the weight of the actual cost is adjusted as follows:

[0049] in, is the weight of the actual cost; for The initial value of for The maximum value of To estimate the cost, the value is the Euclidean distance from the current node to the target node; is the hyperbolic tangent function, which is an activation function used to convert the input x Compression conversion to the interval (-1,1), the formula is: ; D is the Euclidean distance from the starting point to the target point. By adjusting, when the drone approaches the target, The weight of Become larger, so as to ensure the rationality of the path.

[0050] Based on total cost Combining the A-star algorithm and node redundancy judgment, a low-energy and relatively smooth drone path is planned. First, based on the total cost The A-star algorithm is used to search for paths in nodes, and then the redundant nodes in the searched path are judged, and the redundant nodes in the path are deleted to obtain the final UAV flight path.

[0051] In the embodiment of the present application, when the A-star algorithm is used for path search, it is subject to the maneuverability constraints of the UAV, which are mainly limited by multiple conditions such as maximum range constraint, maximum turning angle constraint, maximum pitch angle constraint and minimum track segment length constraint.

[0052] Modeling the maximum range constraint: According to the requirements of the flight mission, the UAV needs to reach the target point within the specified time and complete the flight mission before the power is exhausted. Therefore, the length L of the three-dimensional track obtained by the final plan cannot exceed the maximum range limit. The three-dimensional track of the UAV is represented by connecting adjacent track points (i.e. nodes) through straight line segments. S represents the starting node and G represents the target node. The entire track is divided into N-1 parts (N is a positive integer) by N track points, and N-1 track segments are obtained. Track points The coordinates are expressed as The total length L of the UAV flight track and the maximum range The relationship is expressed as follows:

[0053] Where L is the total length of the UAV flight track; is the length of the kth track segment; N is the total number of nodes; The maximum flight range of the drone.

[0054] Modeling the maximum turning angle constraint: The UAV needs to adjust its heading according to the track during flight. It is constrained by the performance of the UAV itself and at each track point There is a limit value for the turning angle at , and this maximum allowed turning angle is called the maximum turning angle constraint. Project the three-dimensional trajectory of the drone in space onto the XOY plane, then represents the actual turning angle of the drone, represents the maximum turning angle of the drone. The relationship between the two can be expressed as:

[0055] in, represents the actual turning angle of the UAV entering the i-th track segment (i=1, 2, …, L, …, N+1); parameter Represents the direction vector of the i-th track segment in the entire track, expressed as , X iis the direction vector of the ith track segment in the x-axis direction, Y i is the direction vector of the ith track segment in the y-axis direction, Z i is the direction vector of the ith track segment in the z-axis direction; parameter A i+1 Represents the direction vector of the i+1th track segment in the entire track; Indicates the maximum turning angle of the drone flight; T indicates the transposition operation.

[0056] Modeling the maximum pitch angle constraint: When the drone adjusts its heading in three-dimensional space, it also needs to consider the pitch angle in the vertical direction. and are two adjacent track points, the actual pitch angle The calculation method and the maximum pitch angle The relationship is as follows:

[0057] in, Indicates the actual pitch angle of the drone entering the i-th track segment (i=1, 2, …, L, …, N+1); Track point The three-dimensional coordinates of Track point The three-dimensional coordinates of and are two adjacent track points; Indicates the maximum pitch angle of the drone.

[0058] Modeling the minimum track segment length constraint: When the track segment length is less than the minimum track segment length, the drone cannot successfully turn at this track point due to the limitation of the fuselage performance, but must continue to fly a distance along the original heading. The length of the i-th track segment should satisfy the following formula:

[0059] in, represents the length of the i-th track segment; Indicates the minimum track length of the drone flight.

[0060] In the embodiment of the present application, when the A-star algorithm is used for path search, the search direction is also dynamically adjusted in the following manner: The search direction is set to expand the search of the remaining area from the forward, pitch and yaw directions respectively. In the pitch and yaw directions, the maneuverability constraints of the UAV are used as restriction conditions to search in the pitch and yaw directions to the maximum extent.

[0061] In the forward search, in order to improve the search efficiency, a variable step search is proposed. The search step is adjusted within a certain range, and the search is adjusted between the minimum turning radius and the maximum step.

[0062] The search area of ​​the current node is the feasible search space of the current position. According to the gridding idea, the area is divided into some nodes, and the nodes are divided equally in the pitch direction, yaw direction and forward direction. Specifically, the nodes are divided equally in the pitch direction. p The segments are divided into equal distances in the yaw direction. q The segments are divided into t part( are all positive integers), forming Search nodes. Perform heuristic search evaluation on each node, select the best node as the next node, and continue until the target node is reached. Then, perform redundant node judgment on the searched path, delete the redundant nodes in the path, and obtain the final flight path of the drone.

[0063] The three-dimensional UAV path planning method based on the improved A-star algorithm provided in the embodiment of the present application takes into account the additional cost brought by the altitude change in the three-dimensional environment, and determines the actual cost consumption of the UAV by comprehensively considering the altitude cost and the terrain threat cost. Specifically, the actual cost of the UAV from the starting node to the current node is determined based on the range cost, altitude cost and terrain threat cost of the UAV from the starting node to the current node; then, based on the actual cost, the A-star algorithm and node redundancy judgment are used to plan the path of the UAV from the starting node to the target node, so as to obtain a three-dimensional UAV path with less flight energy consumption, delete redundant nodes, and obtain a smoother and less energy-consuming path, thereby extending the flight time of the UAV.

[0064] In some embodiments, determining the actual cost of the drone from the starting node to the current node based on the range cost, altitude cost, and terrain threat cost of the drone from the starting node to the current node includes: Determine weight coefficients corresponding to the range cost, the altitude cost, and the terrain threat cost respectively; According to the weight coefficients corresponding to the range cost, the height cost and the terrain threat cost respectively, the range cost, the height cost and the terrain threat cost are weightedly summed to obtain the actual cost of the UAV from the starting node to the current node.

[0065] Specifically, the actual cost is determined by weighted summing the range cost, altitude cost, and terrain threat cost. The expression of the actual cost is:

[0066] in, The UAV moves from the starting node to the current node (i.e., node n )’s actual cost; is the cost of the UAV’s flight from the starting node to the current node, is the weight coefficient of the voyage cost; is the height cost of the drone from the starting node to the current node, is the weight coefficient of height cost; is the terrain threat cost of the UAV from the starting node to the current node, is the weight coefficient of terrain threat cost.

[0067] The three-dimensional UAV path planning method based on the improved A-star algorithm provided in the embodiment of the present application comprehensively considers the influence of range cost, altitude cost and terrain threat cost on path planning, and adopts weighted summation to integrate various costs to obtain the actual cost, and the weight coefficient can be adjusted dynamically in real time, so as to obtain a more accurate actual cost of UAV flight. The highly sensitive heuristic valuation ensures that the algorithm can accurately reflect the flight cost in three-dimensional space, thereby comprehensively considering flight efficiency and energy consumption and optimizing path selection.

[0068] In some embodiments, the method further comprises: Determine the danger level of the mountain to the drone based on the radius of the mountain and the distance from the drone to the center of the mountain, and determine the danger level of the no-fly airspace to the drone based on the range of the no-fly airspace and the distance from the drone to the center of the no-fly airspace; The terrain threat cost is determined based on the danger level of the mountain to the UAV and the danger level of the no-fly airspace to the UAV.

[0069] Specifically, the terrain threat cost is based on the danger of the mountain to the drone. And the danger level of no-fly airspace to drones is determined , for example, the terrain threat cost of the drone from the starting node to the current node (node ​​n) can be expressed as:

[0070] in, is the terrain threat cost of the UAV from the starting node to the current node (node ​​n); The sum of the dangers of the mountains that the drone passes through from the starting node to the current node to the drone; It is the sum of the danger levels of the no-fly airspaces that the drone passes through from the starting node to the current node; max() means taking the maximum value.

[0071] The terrain for drone flight mainly includes suitable airspace and prohibited airspace. Suitable airspace refers to the airspace suitable for drone flight, which needs to set topographic information, airspace altitude, longitude and latitude, etc.; prohibited airspace refers to the airspace that drones cannot fly through, such as no-fly zones and abnormal weather zones.

[0072] In the flight path planning of UAV, terrain is mainly a description of the topography, and the terrain is approximately expressed as:

[0073] in, is the coordinate value of a point on the mountain surface projected on the horizontal plane, z is the height of the point, Indicates the topography of the point; is the parameter used to control the height; and They represent the projection coordinate value of the center of the mountain in the x direction and the projection coordinate value in the y direction respectively; is the parameter used to control the terrain slope; () is an exponential function.

[0074] In the embodiment of the present application, the danger degree of the mountain to the drone is determined based on the radius of the mountain and the distance from the drone to the center of the mountain. Optionally, when the distance between the drone and the edge of the mountain exceeds 10m, it is considered safe and the danger degree is 0; when the distance between the drone and the edge of the mountain is between 4m and 10m, the closer the distance, the greater the danger degree; when the distance between the drone and the edge of the mountain is less than 4m, the danger degree is infinite. The danger degree of the mountain to the drone can be expressed as:

[0075] in, Indicates the danger level of the mountain to the drone; is the distance from the drone to the center of the mountain; is the radius of the mountain; inf means infinity.

[0076] In the embodiment of the present application, the danger level of the no-fly airspace to the drone is determined based on the range of the no-fly airspace and the distance from the drone to the center of the no-fly airspace. The no-fly airspace can be set as a cylinder, an elliptical cylinder, or a polygonal cylinder. Here, it is assumed that the no-fly airspace area is represented as a cylinder, and the danger level of the no-fly airspace to the drone is represented as:

[0077] in, Indicates the danger level of no-fly airspace to drones; is the maximum radius of the cylinder; is the distance from the drone to the center of the cylinder. When the drone is outside the cylinder (i.e. the distance from the drone to the center of the cylinder is greater than or equal to the maximum radius of the cylinder), the danger level is 0; when the drone is inside the cylinder (i.e. the distance from the drone to the center of the cylinder is less than the maximum radius of the cylinder), it is related to the distance to the center of the cylinder. The closer the distance, the more dangerous it is.

[0078] The three-dimensional UAV path planning method based on the improved A-star algorithm provided in the embodiment of the present application determines the danger of the mountain to the UAV based on the radius of the mountain and the distance from the UAV to the center of the mountain, and determines the danger of the no-fly airspace to the UAV based on the scope of the no-fly airspace and the distance from the UAV to the center of the no-fly airspace. Then, the maximum value of the danger of the mountain to the UAV and the danger of the no-fly airspace to the UAV in the UAV navigation map is used as the terrain threat cost, and the mountain and the no-fly airspace are dynamically avoided, which effectively reduces the collision risk during the flight and the risk of entering the no-fly airspace, and improves the safety of the UAV's three-dimensional planned path.

[0079] In some embodiments, determining a weight coefficient corresponding to the terrain threat cost includes: Based on the danger level of the mountain to the UAV and the danger level of the no-fly airspace to the UAV, an exponential function is used to calculate the weight of the terrain threat cost.

[0080] Specifically, when the actual cost is obtained by the weighted summation method, an exponential function is introduced to dynamically adjust the weight coefficient of the terrain threat cost based on the danger of the mountain to the drone and the danger of the no-fly airspace to the drone. The weight coefficient of the terrain threat cost can be expressed as:

[0081] in, is the weight coefficient of terrain threat cost; The sum of the dangers of the mountains that the drone passes through from the starting node to the current node to the drone; The sum of the danger levels of the no-fly airspaces that the drone passes through from the starting node to the current node; is a linear rectification function, ; d is the distance from the drone to the center of the mountain or no-fly airspace at the current node; R is the maximum radius of the mountain or no-fly airspace at the current node.

[0082] The three-dimensional UAV path planning method based on the improved A-star algorithm provided in the embodiment of the present application is based on the danger level of mountains to UAVs and the danger level of no-fly airspace to UAVs, and uses an exponential function to calculate the weight coefficient of the terrain threat cost. The weight of the terrain threat cost can be quickly increased as the approach to the danger zone is approached, thereby accelerating the adjustment of the flight trajectory and reducing the risk of collision during flight and entering no-fly airspace.

[0083] In some embodiments, the step of planning a path of the drone from the starting node to the target node using the A-star algorithm based on the actual cost includes: Based on the actual cost, the A-star algorithm is used to search in nodes to obtain the initial path of the UAV from the initial node to the target node; the nodes in the initial path are stored in the initial path list closelist; Redundancy judgment and deletion are performed on the nodes in the closelist to obtain a target path list finallist.

[0084] Specifically, based on the actual cost of dynamic adjustment, the A-star algorithm is used to search in nodes to obtain the initial path of the UAV from the initial node to the target node, and the nodes in the initial path are stored in the initial path list closelist.

[0085] For example, Figure 2 This is the second flow chart of the three-dimensional UAV path planning method based on the improved A-star algorithm provided by the present invention, such as Figure 2 As shown, the process of searching using the A-star algorithm is as follows: Step 1: Put the starting node into the current node table openlist.

[0086] Step 2: Determine whether openlist is empty. If it is empty, the output search fails and the starting point is adjusted to restart the search. If it is not empty, go to step 3.

[0087] Step 3: Take the node in openlis as the current node and determine whether the end point has been reached. If not, go to step 4; if reached, go to step 8.

[0088] Step 4: If the end point is not reached, the current node is stored in the closelist and deleted from the openlist.

[0089] Step 5: Determine whether the adjacent node is passable. If so, go to step 6; if not, go to step 2.

[0090] Step 6: If the adjacent nodes are passable, add the adjacent nodes to the openlist, calculate the minimum estimated cost of the adjacent nodes, and find the node with the minimum estimated cost function.

[0091] Step 7: Return to the track route and go to step 2.

[0092] Step 8: Return to the track route, make a redundant judgment on the nodes in the closelist, and delete the redundant nodes.

[0093] Step 9, end.

[0094] In some embodiments, the redundancy determination and deletion of nodes in the closelist includes: When there are three or more consecutive nodes on the same straight line in the closelist, determine the nodes on the same straight line except the nodes at both ends of the straight line as redundant nodes, and delete the redundant nodes from the closelist; and / or

[0095] When there are two discontinuous nodes in the closelist whose connection line does not pass through the no-fly airspace, and the smoothness of the initial path between the two discontinuous nodes does not reach a preset threshold, the node between the two discontinuous nodes is determined to be a redundant node, and the redundant node is deleted from the closelist.

[0096] Specifically, a redundancy judgment is performed on the nodes in the closelist, that is, whether two discontinuous nodes in the initial path can pass directly between them is judged. If so, unnecessary turning points between the two nodes are deleted.

[0097] Furthermore, to determine whether there is a redundant node between two discontinuous nodes in the closelist, it is possible to determine whether three or more consecutive nodes are located on the same straight line (the straight line refers to a straight line that coincides with a certain initial path). If they are located on the same straight line, then the middle node among the multiple consecutive nodes on the same straight line is determined to be a redundant node, that is, the nodes on the straight line except the nodes at both ends of the straight line are determined to be redundant nodes, and the redundant nodes are deleted from the closelist.

[0098] If three or more consecutive nodes are not on the same straight line, then determine whether the line connecting any two discontinuous nodes among these nodes passes through the no-fly airspace: if not, it means that the line connecting the two discontinuous nodes can pass safely, and further determine the smoothness of the initial path between the two discontinuous nodes (which can be determined by calculating the turning angle) and compare it with the preset threshold. If the smoothness does not reach the preset threshold, it means that the initial path between the two discontinuous nodes is not smooth enough, delete other nodes between the two discontinuous nodes (i.e. redundant nodes) from the closelist, use the line between the two discontinuous nodes as the flight path, and improve the smoothness of the path; if the smoothness reaches the preset threshold, it means that the initial path between the two discontinuous nodes is relatively smooth, and no modification is made to this section of the path.

[0099] If three or more consecutive nodes are not on the same line, and the line connecting two of these discontinuous nodes passes through the no-fly airspace, it means that the line connecting these two discontinuous nodes is not safe to pass. Then further narrow the scope and determine whether the nodes between these two discontinuous nodes are on the same line. Repeat the above judgment process until all nodes in the closelist are judged, and the closelist with all redundant nodes deleted is obtained, and the finallist is obtained.

[0100] For example, suppose the searched initial path list closelist contains n nodes, denoted as P i , i=1, 2, …, L, …, n, where L is the length of the UAV flight track. The process of redundancy judgment of nodes in the closelist is as follows: Step 1: Determine the closelist , , Are they on the same straight line? If so, go to step 2; if not, go to step 3.

[0101] Step 2 , , In a straight line, and Return the path and delete it from the closelist .

[0102] Step 3 , , Not in a straight line, judge and Check whether the line connecting the two passes through the no-fly airspace. If not, go to step 4; if so, go to step 8. and Similarly, start judging from step 3.

[0103] Step 4 and The line connecting the two does not pass through the no-fly airspace, indicating and The connection can be safely passed, returning to the path and ,Will Set as the turning point Add to finallist and go to step 5.

[0104] Step 5: Import ,calculate , and The turning angle of this path , and compare it with the preset threshold For comparison. Less than or equal to , go to step 6; if Greater than , go to step 7.

[0105] Step 6 Less than or equal to ,show , and This path is not smooth enough. and Return path, delete from closelist .

[0106] Step 7 Greater than ,show , and This path meets the smoothness requirement. , and Return path, Set as the turning point Add to finallist.

[0107] Step 8 and The line passes through the no-fly airspace, indicating and The connection is not accessible, so , starting from step 1 , and Make judgment until the end.

[0108] Step 9. Repeat steps 1 and 2 until the closelist is searched.

[0109] The following is a further description of the three-dimensional UAV path planning method based on the improved A-star algorithm provided in the above embodiments through specific examples: Example 1: Figure 3 is one of the initial path diagrams of the example scenarios of the three-dimensional UAV path planning method based on the improved A-star algorithm provided by the present invention, such as Figure 3 As shown, the searched initial path includes P1, P2, P3, P4 and P5 (where P1 is the initial node and P5 is the target node) and is stored in the initial path list closelist. The specific steps for redundancy judgment of the nodes in closelist are as follows: Step 1: Determine through judgment that P1, P3, and P5 in the closelist are not on the same straight line.

[0110] Step 2: Confirm that the line connecting P1 and P3 does not pass through the no-fly airspace and can be safely passed, return the path P1 and P3, set P3 as the turning point, and add P3 to the finallist. Confirm that the line connecting P3 and P5 does not pass through the no-fly airspace and can be safely passed, return the path P3 and P5.

[0111] Step 3, introduce P2, make sure P1, P2, and P3 are on the same straight line, return the paths P1 and P3, and delete P2 from the closelist.

[0112] Step 4: Introduce P4 and make sure that P3, P4, and P5 are not on the same straight line. Since the line connecting P3 and P5 is safe to pass, determine the turning angle of the path between P3, P4, and P5. ,Compare With preset threshold size.

[0113] Step 5: If Less than or equal to , not smooth enough, return P3 and P5 to the path, and delete P4 from the closelist. After the closelist search is completed, the final path P1-P3-P5 is obtained.

[0114] Step 6: If Greater than , indicating that the path of P3, P4, and P5 meets the smoothness requirement, P4 is set as a turning point, and P4 is added to the finallist. After the closelist search is completed, the final path P1-P3-P4-P5 is obtained.

[0115] Example 2: Figure 4 This is the second initial path schematic diagram of the example scenario of the three-dimensional UAV path planning method based on the improved A-star algorithm provided by the present invention, such as Figure 4 As shown, the searched initial path includes P1, P2, P3, P4 and P5 (where P1 is the initial node and P5 is the target node) and is stored in the initial path list closelist. The specific steps for redundancy judgment of the nodes in closelist are as follows: Step 1: Determine through judgment that P1, P3, and P5 in the closelist are not on the same straight line.

[0116] Step 2: Determine that the line connecting P1 and P3 does not cross the no-fly airspace and can be safely passed, return to the path P1 and P3, set P3 as a turning point, and add P3 to the finallist. Determine that the line connecting P3 and P5 crosses the no-fly airspace and cannot be safely passed.

[0117] Step 3, introduce P2, make sure P1, P2, and P3 are on the same straight line, return the paths P1 and P3, and delete P2 from the closelist.

[0118] Step 4: Introduce P4 and make sure that P3, P4, and P5 are not on the same straight line.

[0119] Step 5: Confirm that the line connecting P3 and P4 does not pass through the no-fly airspace and can pass safely, and return the paths P3 and P4. Confirm that the line connecting P4 and P5 does not pass through the no-fly airspace and can pass safely, and return the paths P4 and P5, set P4 as the turning point, and add P4 to the finallist.

[0120] Step 6. The closelist search is completed and the final path P1-P3-P4-P5 is obtained.

[0121] The three-dimensional UAV path planning method based on the improved A-star algorithm provided in the embodiment of the present application performs node redundancy judgment on the initial path obtained by searching using the A-star algorithm, deletes unnecessary turning points in the initial path, and obtains a smoother and shorter path, thereby improving the stability of the UAV flight and further reducing the UAV flight energy consumption.

[0122] In some embodiments, the method further comprises: Determine whether a line connecting two adjacent nodes in the closelist passes through a no-fly airspace; In the case where a line connecting two adjacent nodes in the closelist passes through a no-fly airspace, it is determined that the initial path is invalid.

[0123] Specifically, if it is determined that the line connecting two adjacent nodes in the closelist passes through a no-fly airspace, the entire initial path is determined to be invalid and the path needs to be searched again.

[0124] The three-dimensional UAV path planning method based on the improved A-star algorithm provided in the embodiment of the present application effectively handles the complexity of three-dimensional space while maintaining computational efficiency. In a complex three-dimensional environment, the planning speed is improved compared with the traditional method, and the path length is reduced. The adaptability to dynamic environments is enhanced through the prediction model, that is, the path is dynamically adjusted to cope with environmental changes. The dynamic avoidance mechanism effectively reduces the risk of collision during flight and ensures flight safety. The path is optimized by comprehensively considering factors such as flight altitude and speed. The optimized path reduces the average energy consumption of the UAV and extends the flight time.

[0125] In a mountain forest fire monitoring mission, the drone needs to fly autonomously in a complex terrain full of trees, and may encounter temporary smoke areas during the flight. Using the three-dimensional drone path planning method based on the improved A-star algorithm provided in the embodiment of the present application, the drone successfully planned an efficient path that avoids dense trees and bypasses the smoke area, without any collision risk, and completed the task within the specified time. Compared with the traditional method, the remaining battery power of the drone increased by 10%, verifying the effectiveness of the present invention in practical applications.

[0126] Figure 5 is a schematic diagram of the structure of a three-dimensional UAV path planning device based on an improved A-star algorithm provided by the present invention. Figure 5 As shown, the present invention provides a three-dimensional unmanned aerial vehicle path planning device based on the improved A-star algorithm, including an actual cost determination module 501 and a path planning module 502.

[0127] The actual cost determination module 501 is used to determine the actual cost of the drone from the starting node to the current node based on the range cost, altitude cost and terrain threat cost of the drone from the starting node to the current node; the range cost is used to evaluate the cost consumption caused by the flight track of the drone; the altitude cost is used to evaluate the cost consumption caused by the change in the flight altitude of the drone; the terrain threat cost is used to evaluate the cost consumption caused by the danger level of mountains and no-fly airspace on the drone path planning.

[0128] The path planning module 502 is used to plan the path of the drone from the starting node to the target node based on the actual cost using the A-star algorithm and node redundancy judgment.

[0129] In some embodiments, the actual cost determination module 501 includes: A first determining unit, used to determine weight coefficients corresponding to the range cost, the altitude cost and the terrain threat cost respectively; The first acquisition unit is used to perform weighted summation of the range cost, the altitude cost and the terrain threat cost according to the weight coefficients corresponding to the range cost, the altitude cost and the terrain threat cost respectively, so as to obtain the actual cost of the UAV from the starting node to the current node.

[0130] In some embodiments, it also includes: A second determination unit is used to determine the danger degree of the mountain to the drone based on the radius of the mountain and the distance from the drone to the center of the mountain, and to determine the danger degree of the no-fly airspace to the drone based on the range of the no-fly airspace and the distance from the drone to the center of the no-fly airspace; The third determining unit is used to determine the terrain threat cost based on the danger level of the mountain to the UAV and the danger level of the no-fly airspace to the UAV.

[0131] In some embodiments, the first determining unit includes: The calculation subunit is used to calculate the weight of the terrain threat cost using an exponential function based on the danger level of the mountain to the UAV and the danger level of the no-fly airspace to the UAV.

[0132] In some embodiments, the path planning module 502 includes: The second acquisition unit is used to search in nodes based on the actual cost using the A-star algorithm to obtain an initial path of the UAV from the initial node to the target node; the nodes in the initial path are stored in an initial path list closelist; The third acquisition unit is used to perform redundancy judgment and deletion on the nodes in the closelist to obtain a target path list finallist.

[0133] In some embodiments, the third acquisition unit includes: A first determining subunit is configured to, when there are three or more consecutive nodes on the same straight line in the closelist, determine that the nodes on the same straight line except the nodes at both ends of the straight line are redundant nodes, and delete the redundant nodes from the closelist; and / or

[0134] The second determination subunit is used to determine that the node between two discontinuous nodes in the closelist is a redundant node and delete the redundant node from the closelist when the line connecting the two discontinuous nodes does not pass through the no-fly airspace and the smoothness of the initial path between the two discontinuous nodes does not reach a preset threshold.

[0135] In some embodiments, it also includes: A judging subunit, used for judging whether a line connecting any two adjacent nodes in the closelist passes through a no-fly airspace; The third determining subunit is used to determine that the initial path is invalid when a line connecting any two adjacent nodes in the closelist passes through a no-fly airspace.

[0136] Specifically, the three-dimensional UAV path planning device based on the improved A-star algorithm provided by the present invention can implement all the method steps implemented by the three-dimensional UAV path planning method embodiment based on the improved A-star algorithm, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0137] It should be noted that the division of units / modules in the above-mentioned embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, the functional units in the various embodiments of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units.

[0138] Figure 6 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 6 As shown, the electronic device may include: a processor 601, a communications interface 602, a memory 603 and a communication bus 604, wherein the processor 601, the communications interface 602 and the memory 603 communicate with each other through the communication bus 604. The processor 601 may call the logic instructions in the memory 603 to execute a three-dimensional UAV path planning method based on the improved A-star algorithm, the method comprising: The actual cost of the UAV from the starting node to the current node is determined based on the range cost, altitude cost and terrain threat cost of the UAV from the starting node to the current node; the range cost is used to evaluate the cost consumption caused by the flight track of the UAV; the altitude cost is used to evaluate the cost consumption caused by the change of the flight altitude of the UAV; the terrain threat cost is used to evaluate the cost consumption caused by the danger of mountains and no-fly airspace on the path planning of the UAV; Based on the actual cost, the path of the UAV from the starting node to the target node is planned using the A-star algorithm and node redundancy judgment.

[0139] Specifically, the processor 601 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or a complex programmable logic device (CPLD), and the processor may also adopt a multi-core architecture.

[0140] The logic instructions in the memory 603 can be implemented in the form of software functional units and can be stored in a processor-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0141] In some embodiments, a computer program product is also provided, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the three-dimensional drone path planning method based on the improved A-star algorithm provided in the above-mentioned method embodiments.

[0142] Specifically, the above-mentioned computer program product provided in the embodiment of the present application can implement all the method steps implemented by the above-mentioned method embodiments, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0143] In some embodiments, a computer-readable storage medium is also provided, wherein the computer-readable storage medium stores a computer program, and the computer program is used to enable a computer to execute the three-dimensional drone path planning method based on the improved A-star algorithm provided in the above-mentioned method embodiments.

[0144] Specifically, the above-mentioned computer-readable storage medium provided by the present invention can implement all the method steps implemented by the above-mentioned method embodiments, and can achieve the same technical effects. The parts and beneficial effects that are the same as the method embodiments in this embodiment will not be described in detail here.

[0145] It should be noted that the computer-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0146] It should also be noted that the terms "first", "second", etc. in the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are usually of the same type, and the number of objects is not limited. For example, the first object can be one or more.

[0147] In the present invention, the term "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0148] In the present invention, the term "plurality" refers to two or more than two, and other quantifiers are similar to them.

[0149] In the present invention, "determine B based on A" means that the factor A should be considered when determining B. It is not limited to "B can be determined based on A alone", but should also include: "determine B based on A and C", "determine B based on A, C and E", "determine C based on A, and further determine B based on C", etc. In addition, it can also include taking A as a condition for determining B, for example, "when A meets the first condition, use the first method to determine B"; for another example, "when A meets the second condition, determine B", etc.; for another example, "when A meets the third condition, determine B based on the first parameter", etc. Of course, it can also be a condition that takes A as a factor for determining B, for example, "when A meets the first condition, use the first method to determine C, and further determine B based on C", etc.

[0150] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0151] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer executable instructions. These computer executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0152] These processor executable instructions may also be stored in a processor readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the processor readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0153] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0154] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A three-dimensional UAV path planning method based on an improved A-star algorithm, characterized in that: include: Determine the actual cost of the UAV from the starting node to the current node based on the range cost, altitude cost and terrain threat cost of the UAV from the starting node to the current node; The range cost is used to evaluate the cost consumption caused by the flight track of the drone; the altitude cost is used to evaluate the cost consumption caused by the change of the flight altitude of the drone; the terrain threat cost is used to evaluate the cost consumption caused by the danger of mountains and no-fly airspace on the drone path planning; Based on the actual cost, the path of the UAV from the starting node to the target node is planned using the A-star algorithm and node redundancy judgment.

2. The three-dimensional UAV path planning method based on the improved A-star algorithm according to claim 1 is characterized in that: The actual cost of the UAV from the starting node to the current node is determined based on the range cost, altitude cost, and terrain threat cost of the UAV from the starting node to the current node, including: Determine weight coefficients corresponding to the range cost, the altitude cost, and the terrain threat cost respectively; According to the weight coefficients corresponding to the range cost, the height cost and the terrain threat cost respectively, the range cost, the height cost and the terrain threat cost are weightedly summed to obtain the actual cost of the UAV from the starting node to the current node.

3. The three-dimensional UAV path planning method based on the improved A-star algorithm according to claim 1 or 2, characterized in that: The method further comprises: Determine the danger level of the mountain to the drone based on the radius of the mountain and the distance from the drone to the center of the mountain, and determine the danger level of the no-fly airspace to the drone based on the range of the no-fly airspace and the distance from the drone to the center of the no-fly airspace; The terrain threat cost is determined based on the danger level of the mountain to the UAV and the danger level of the no-fly airspace to the UAV.

4. The three-dimensional UAV path planning method based on the improved A-star algorithm according to claim 3 is characterized in that: Determining a weight coefficient corresponding to the terrain threat cost includes: Based on the danger level of the mountain to the UAV and the danger level of the no-fly airspace to the UAV, an exponential function is used to calculate the weight of the terrain threat cost.

5. The three-dimensional UAV path planning method based on the improved A-star algorithm according to claim 1 is characterized in that: The method of planning a path of the drone from the starting node to the target node based on the actual cost and using the A-star algorithm and node redundancy judgment includes: Based on the actual cost, the A-star algorithm is used to search in nodes to obtain the initial path of the UAV from the initial node to the target node; the nodes in the initial path are stored in the initial path list closelist; Redundancy judgment and deletion are performed on the nodes in the closelist to obtain a target path list finallist.

6. The three-dimensional UAV path planning method based on the improved A-star algorithm according to claim 5 is characterized in that: The redundancy determination and deletion of nodes in the closelist includes: When there are three or more consecutive nodes on the same straight line in the closelist, determine the nodes on the same straight line except the nodes at both ends of the straight line as redundant nodes, and delete the redundant nodes from the closelist; and / or When there are two discontinuous nodes in the closelist whose connection line does not pass through the no-fly airspace, and the smoothness of the initial path between the two discontinuous nodes does not reach a preset threshold, the node between the two discontinuous nodes is determined to be a redundant node, and the redundant node is deleted from the closelist.

7. The three-dimensional UAV path planning method based on the improved A-star algorithm according to claim 5 or 6, characterized in that: The method further comprises: Determine whether the line connecting any two adjacent nodes in the closelist passes through a no-fly airspace; In the case where a line connecting any two adjacent nodes in the closelist passes through a no-fly airspace, it is determined that the initial path is invalid.

8. A three-dimensional UAV path planning device based on an improved A-star algorithm, characterized in that: include: The actual cost determination module is used to determine the actual cost of the drone from the starting node to the current node based on the range cost, altitude cost and terrain threat cost of the drone from the starting node to the current node; the range cost is used to evaluate the cost consumption caused by the flight track of the drone; the altitude cost is used to evaluate the cost consumption caused by the change of the flight altitude of the drone; the terrain threat cost is used to evaluate the cost consumption caused by the danger of mountains and no-fly airspace on the drone path planning; The path planning module is used to plan the path of the UAV from the starting node to the target node based on the actual cost by using the A-star algorithm and node redundancy judgment.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the three-dimensional unmanned aerial vehicle path planning method based on the improved A-star algorithm as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a three-dimensional unmanned aerial vehicle path planning method based on the improved A-star algorithm is implemented as described in any one of claims 1 to 7.

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