A method and apparatus for selecting local target points in UAV rolling flight path planning

Abstract

This application provides a method and apparatus for selecting local target points in rolling flight path planning for unmanned aerial vehicles (UAVs). The method includes: 1. Establishing and initializing an environment model; 2. Determining the global planning starting point, the global planning final target point, and the pre-planned optimal flight path; 3. Determining the local planning starting point; 4. Updating the environmental threat situation; 5. If an optimal flight path target point selection strategy exists, a local target point is selected on the pre-planned optimal flight path; otherwise, a heuristic target point selection strategy is adopted, and a local target point is generated by a heuristic function; 6. If the generated local target point is a feasible target point, it is adopted; otherwise, a protection strategy is adopted to generate a new feasible local target point; 7. Flight path planning is performed using the local planning starting point and the generated local target point, and the flight path is followed by a step distance to reach the local planning step point; 8. Determining whether the final planning target point has been reached; if reached, the planning ends; otherwise, return to step 3.

Description

Technical Field

[0001] This invention belongs to the field of UAV route planning technology, specifically relating to a method and apparatus for selecting local target points in UAV rolling route planning. Background Technology

[0002] As one of the key technologies in the field of drones, drone route planning mainly includes two parts: offline planning and online planning.

[0003] Offline planning primarily involves global route planning based on predetermined tasks and known environmental information, taking into account the performance constraints of the UAV. Online planning, on the other hand, is performed in real-time during UAV flight based on real-time sensor information. It involves local replanning of the route in response to continuously incoming obstacle information, in order to quickly generate a flyable path and take appropriate obstacle avoidance actions.

[0004] In achieving rolling flight path planning for UAVs in complex environments, the selection of local target points is particularly important, as it not only relates to the optimization of local flight path planning but also has a certain impact on the global optimization.

[0005] However, existing UAV online route planning technologies often suffer from the problem of not being able to simultaneously achieve both global and local optimal indicators. Summary of the Invention

[0006] This invention provides a method and apparatus for selecting local target points in UAV rolling route planning, which can solve the problem that it is difficult to balance global and local optimal indicators in existing UAV online route planning technology; while ensuring real-time online local planning, it considers global optimization indicators, and achieves flexible obstacle avoidance without sacrificing the optimality of the entire route.

[0007] In a first aspect, this application provides a method for selecting local target points in rolling flight path planning for unmanned aerial vehicles (UAVs), the method comprising:

[0008] Step 1: Establish and initialize the environment model. The parameter information of the environment model includes: threat zone, obstacle zone, and no-fly zone;

[0009] Step 2: Determine the starting point of the global planning, the final target point of the global planning, and the pre-planned optimal route;

[0010] Step 3: Determine the starting point of the local planning based on the starting point of the global planning;

[0011] Step 4: Update the environmental threat situation based on the environmental model, the starting point of the local planning, and the situation measurement values ​​within the single-step planning window period;

[0012] Step 5: Determine if there is a pre-planned optimal route. If there is, use the optimal route target point selection strategy to select a local target point on the pre-planned optimal route. If there is no target point selection strategy, use the heuristic target point selection strategy to generate a local target point from the heuristic function.

[0013] Step 6: Based on the pre-set judgment principle, determine whether the generated local target point is a feasible target point. If the generated local target point is a feasible target point, then the generated local target point is adopted; if the generated local target point is not a feasible target point, then a protection strategy is adopted to generate a new feasible local target point.

[0014] Step 7: Perform route planning using the local planning starting point and the generated local target point, and use the point reached by flying only one step distance along the route as the local planning step arrival point;

[0015] Step 8: Determine if the final target point has been reached. If it has, the planning ends; otherwise, return to step 3.

[0016] Furthermore, step 5 includes:

[0017] 501. Establish a local rectangular coordinate system with the starting point of the local planning as the center, the vector pointing from the starting point to the final target point of the planning as the x-axis, and the direction perpendicular to the vector as the y-axis.

[0018] 502, in the local rectangular coordinate system, in K1 points are randomly generated within the range, and the K1 points are arranged in descending order of their corresponding x-coordinates.

[0019] 503. Following the order of steps 502, find the local target points sequentially using the heuristic function f(P) = g(P) + h(P).

[0020] Furthermore, the heuristic target point selection strategy is as follows: after each update of the scrolling window information, the heuristic function f(P) = g(P) + h(P) is used to select a local target point, where P is a point on the scrolling window, g(P) is the cost for the UAV to fly from the current position to the target position, and h(P) is the cost for flying from P to the destination.

[0021] Furthermore, in step 6, a protection strategy is adopted to generate new feasible local target points. The steps are as follows:

[0022] 601. If the local target point generated according to the optimal route target point selection strategy is located in the threat situation detected by the rolling window, then the heuristic target point selection strategy is used to generate the local target point.

[0023] 602. If all k1 points generated according to the heuristic target point selection strategy are infeasible, then in k2 points are randomly generated within the range and arranged in descending order of their corresponding x-coordinates. Local target points are then searched sequentially using the heuristic function f(P) = g(P) + h(P).

[0024] 603. If the k2 points generated in step 602 are also infeasible, reduce the radius of the scrolling window by 10% and return to step 601 until a feasible local target point is found.

[0025] Furthermore, the determination principle in step 6 is: the generated local target point is located in the feasible region.

[0026] Furthermore, the condition for reaching the final target point of the plan in step 8 is: during planning, the final target point of the global plan appears within a circle centered on the starting point of the next round of local planning, with the distance of the scrolling window as the radius.

[0027] Furthermore, in step 3, the starting point of the initial planning is the global planning starting point (x1, y1) of the aircraft, and the starting point of each subsequent planning round is the step size reached in the previous planning round.

[0028] Secondly, this application provides a local target point selection device for UAV rolling route planning, the device being used to implement the aforementioned local target point selection method for UAV rolling route planning.

[0029] Advantages of this invention: This invention proposes a method and apparatus for selecting local target points in UAV rolling route planning. Based on the real-time measured local environmental information of the UAV, it draws on the principles of the A* algorithm, fully utilizes the pre-planned optimal route, and simultaneously considers both speed and optimality indicators. Furthermore, it designs corresponding protection strategies to ensure the feasibility of generating local target points during UAV rolling online planning. Specifically, it includes the following technical effects:

[0030] (1) The method is simple in principle, easy to implement and has a small amount of computation, and can better meet the speed and real-time requirements of online route planning under airborne conditions;

[0031] (2) This method can make full use of the local environmental information detected in real time, effectively combine optimization and feedback mechanisms, and realize the reasonable integration of global environmental information and local information detected in real time.

[0032] (3) This method ensures real-time online local planning while also taking into account global optimization indicators. It flexibly avoids obstacles while always approaching the final target point of the plan, ensuring the correctness of the planning direction and without losing the optimality of the entire route.

[0033] (4) This method is highly flexible. Depending on the focus of the problem to be solved, the generation strategy of the target point can be flexibly selected and the parameters can be flexibly set.

[0034] (5) This method has strong versatility and is applicable to both fixed-wing and rotary-wing UAVs. For different models of UAVs, only the parameters related to UAV performance in the algorithm need to be changed to use it directly. Attached Figure Description

[0035] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of the present invention and do not constitute a limitation on the technical solutions of the present invention.

[0036] Figure 1 Flowchart of the method for selecting local target points;

[0037] Figure 2 Flowchart for protection strategy execution;

[0038] Figure 3 A schematic diagram illustrating the strategy for selecting the optimal route target point;

[0039] Figure 4 A schematic diagram of a heuristic target point selection strategy. Detailed Implementation

[0040] This invention belongs to the field of UAV route planning technology, specifically involving a method for selecting local target points in rolling UAV route planning. First, the starting point of the rolling online planning is determined, and the environmental threat situation within the single-step planning window is updated. Second, it is determined whether a pre-planned optimal route already exists; if so, an optimal route target point selection strategy is adopted; otherwise, a heuristic target point selection strategy is adopted. Next, it is determined whether the generated local target point is a feasible target point; if feasible, the generated local target point is adopted; if not, a protection strategy is adopted to generate a new feasible local target point. Then, route planning is performed using the starting point and the generated local target point, and the flight proceeds along the route in one step. Finally, it is determined whether the final planned target point has been reached; if reached, the planning ends; otherwise, the above-mentioned local route online planning steps are executed continuously.

[0041] This invention provides a method and apparatus for selecting local target points in UAV rolling route planning. Based on the local environmental information measured by the UAV in real time, it draws on the heuristic function principle of the A* algorithm, makes full use of the pre-planned optimal route, takes into account both speed and optimality indicators, and designs corresponding protection strategies to ensure the feasibility of generating local target points during UAV rolling online planning, and realizes rapid adjustment to the dynamic environment.

[0042] The technical solution of this invention is a method for selecting local target points in rolling flight path planning for unmanned aerial vehicles (UAVs). Throughout the rolling update process, a heuristic algorithm is combined with a pre-planned optimal flight path. Based on locally perceived situational information, a heuristic strategy is used to update and search for local target points or select them as pre-planned optimal flight path points. This process continuously advances with the flight, effectively improving the applicability and optimality of local window flight path search. From the initial planning phase to the final phase, the UAV continuously approaches the final planned target point, and the directionality of the planning strengthens accordingly. When the planning window reaches its final state, the local target point is also the final planned target point.

[0043] like Figure 1 As shown, this application provides a method for selecting local target points in UAV rolling path planning, the method comprising:

[0044] Step 1: Establish and initialize the environment model. The parameter information of the environment model includes: threat zone, obstacle zone, and no-fly zone;

[0045] Step 2: Determine the starting point of the global planning, the final target point of the global planning, and the pre-planned optimal route;

[0046] Step 3: Determine the starting point of the local planning based on the starting point of the global planning;

[0047] In step 3, the starting point of the initial planning is the global planning starting point (x1, y1) of the aircraft, and the starting point of each subsequent planning round is the step size reached in the previous planning round.

[0048] Step 4: Update the environmental threat situation based on the environmental model, the starting point of the local planning, and the situation measurement values ​​within the single-step planning window period;

[0049] Step 5: Determine if there is a pre-planned optimal route. If there is, use the optimal route target point selection strategy to select a local target point on the pre-planned optimal route. If there is no target point selection strategy, use the heuristic target point selection strategy to generate a local target point from the heuristic function.

[0050] Optionally, the optimal route target point selection strategy in step 5 is based on the existence of a pre-planned optimal route. In this case, the local target point selection strategy is: the intersection of the circle with the local planning starting point as the center and the rolling window distance as the radius.

[0051] Furthermore, the specific solution method for the local target point selection strategy can be expressed as follows:

[0052] 501. Establish a local rectangular coordinate system with the starting point of the local planning as the center, the vector pointing from the starting point to the final target point of the planning as the x-axis, and the direction perpendicular to the vector as the y-axis.

[0053] 502, in the local rectangular coordinate system, in K1 points are randomly generated within the range, and the K1 points are arranged in descending order of their corresponding x-coordinates.

[0054] 503. Following the order of steps 502, find the local target points sequentially using the heuristic function f(P) = g(P) + h(P).

[0055] Optionally, the heuristic target point selection strategy in step 5 is as follows: after each update of the scrolling window information, the heuristic function f(P) = g(P) + h(P) is used to select a local target point, where P is a point on the scrolling window, g(P) is the cost of the UAV flying from the current position to the target position, and h(P) is the cost of flying from P to the destination.

[0056] It should be noted that g(P) can be estimated based on the position of P and the environmental information within the current window.

[0057] It should be noted that, since information outside the boundary is unavailable, h(P) is estimated using the Euclidean distance from P to the endpoint, and the window boundary point P with the lowest cost is ultimately taken as the local target point P. sub When P is obtained sub If there is more than one, you can choose any one, that is:

[0058] minf(P) = g(P) + h(P)

[0059] To simplify computation, the method reduces the requirements for optimization and focuses on finding feasible algorithms. Therefore, the value of g(P) is defined only by whether P belongs to the feasible region, i.e.:

[0060]

[0061] The feasible region is defined as: the generated local target point is not located in the threat zone, obstacle zone, or no-fly zone.

[0062] It should be noted that the heuristic target point selection strategy is a compromise between the requirements of global optimization and the constraints of limited local information.

[0063] Step 6: Based on the pre-set judgment principle, determine whether the generated local target point is a feasible target point. If the generated local target point is a feasible target point, then the generated local target point is adopted; if the generated local target point is not a feasible target point, then a protection strategy is adopted to generate a new feasible local target point.

[0064] Optionally, the determination principle is: the generated local target point is located in the feasible region.

[0065] Furthermore, a protection strategy is adopted to generate new feasible local target points, the steps of which are as follows:

[0066] 601. If the local target point generated according to the optimal route target point selection strategy is located in the threat situation detected by the rolling window, then the heuristic target point selection strategy is used to generate the local target point.

[0067] 602. If all k1 points generated according to the heuristic target point selection strategy are infeasible, then in k2 points are randomly generated within the range and arranged in descending order of their corresponding x-coordinates. Local target points are then searched sequentially using the heuristic function f(P) = g(P) + h(P).

[0068] 603. If the k2 points generated in step 602 are also infeasible, reduce the radius of the scrolling window by 10% and return to step 601 until a feasible local target point is found.

[0069] Step 7: Perform route planning using the local planning starting point and the generated local target point, and use the point reached by flying only one step distance along the route as the local planning step arrival point;

[0070] The distance between the local planning step-size reach point (x3, y3) and the local planning start point (x1, y1) is the step-size distance.

[0071] Step 8: Determine if the final target point has been reached. If it has, the planning ends; otherwise, return to step 3.

[0072] The condition for reaching the final target point of the plan is: in a certain planning process, the final target point of the global plan appears within a circle centered on the starting point of the next round of local planning, with the scroll window distance as the radius.

[0073] Example 1

[0074] This embodiment provides a method for selecting local target points in UAV rolling flight path planning, combined with Figure 1 , Figure 2 , Figure 3 , Figure 4 As shown, the specific steps include:

[0075] Step 1: Establish and initialize the environment model. The parameter information of the environment model includes: threat zone, obstacle zone, and no-fly zone.

[0076] name shape Center X coordinate (m) Center Y-coordinate (m) Circumcircle radius (m) No-fly zone Prism 1133.3 0.1 179.2 obstacle Prism 3732.9 -81.7 213.7 threaten ellipsoid 2215.8 261.9 198.2

[0077] Step 2: Determine the starting point of the global planning, the final target point of the global planning, and the pre-planned optimal route;

[0078] Specifically, the starting point of the plan is (200m, 0m), the final target point is (5000m, 0m), and the initial pre-planned optimal route is the line connecting the starting point and the final target point.

[0079] Step 3: Determine the starting point of the local planning based on the starting point of the global planning;

[0080] Specifically, the starting point of the first planning is the global planning starting point of the aircraft (200m, 0m), and the starting point of each subsequent planning round is the step size reached in the previous planning round.

[0081] Step 4: Update the environmental threat situation based on the environmental model, the starting point of the local planning, and the situation measurement values ​​within the single-step planning window period;

[0082] Specifically, set the scroll window size to D. w =500m.

[0083] Step 5: Determine if there is a pre-planned optimal route. If there is, use the optimal route target point selection strategy to select a local target point on the pre-planned optimal route. If there is no target point selection strategy, use the heuristic target point selection strategy to generate a local target point from the heuristic function.

[0084] Specifically, there is an initial pre-planned optimal route, which is the line connecting the planning starting point and the planning final target point. Therefore, the optimal route target point selection strategy is adopted to select local target points on the pre-planned optimal route.

[0085] Furthermore, taking the planning starting point (200m, 0m) as the center, and the rolling window distance D... w With a radius of 500m, the coordinates of the intersection point of this circle and the pre-planned optimal route are (700m, 0m).

[0086] Step 6: Based on the pre-set judgment principle, determine whether the generated local target point is a feasible target point. If it is feasible, use the generated local target point; if it is not feasible, adopt the protection strategy to generate a new feasible local target point.

[0087] Specifically, the local target point has coordinates of (700m, 0m). This point is located within the feasible region and is therefore a feasible target point.

[0088] Step 7: Perform route planning using the local planning starting point and the generated local target point, and use the point reached by flying only one step distance along the route as the local planning step arrival point;

[0089] Specifically, the step distance is D. s =200m, the coordinates of the local planning step reach point (400m, 0m).

[0090] Step 8: Determine if the final target point has been reached. If it has, the planning ends; otherwise, return to step 3.

[0091] Specifically, the final target point is located at (5000m, 0m), the starting point for the next round of local planning is (400m, 0m), and the rolling window distance is D. w =500m, the termination condition has not been reached. We need to return to step 3 and repeat the process until the condition is met.

[0092] Example 2

[0093] This embodiment provides a method for selecting local target points in UAV rolling flight path planning, combined with Figure 1 , Figure 2 , Figure 3 , Figure 4 As shown, the specific steps include:

[0094] Step 1: Establish and initialize the environment model. The parameter information of the environment model includes: threat zone, obstacle zone, and no-fly zone.

[0095] name shape Center X coordinate (m) Center Y-coordinate (m) Circumcircle radius (m) No-fly zone Prism 1350 0 100 obstacle Prism 1882.4 104.3 176.6 threaten ellipsoid 3625.8 18.7 162.5 threaten ellipsoid 4678.2 61.9 176.5 threaten ellipsoid 4389.7 53.6 214.4

[0096] Step 2: Determine the starting point of the global planning, the final target point of the global planning, and the pre-planned optimal route;

[0097] Specifically, the starting point of the plan is (350m, 0m), the final target point is (5550m, 0m), and the optimal route initially planned is the line connecting the starting point and the final target point.

[0098] Step 3: Determine the starting point of the local planning based on the starting point of the global planning;

[0099] Specifically, the starting point of the first planning is the global planning starting point of the aircraft (350m, 0m), and the starting point of each subsequent planning round is the step size reached in the previous planning round.

[0100] Step 4: Update the environmental threat situation based on the environmental model, the starting point of the local planning, and the situation measurement values ​​within the single-step planning window period;

[0101] Specifically, set the scroll window size to D. w =1000m.

[0102] Step 5: Determine if there is a pre-planned optimal route. If there is, use the optimal route target point selection strategy to select a local target point on the pre-planned optimal route. If there is no target point selection strategy, use the heuristic target point selection strategy to generate a local target point from the heuristic function.

[0103] Specifically, there is an initial pre-planned optimal route, which is the line connecting the planning starting point and the planning final target point. Therefore, the optimal route target point selection strategy is adopted to select local target points on the pre-planned optimal route.

[0104] Furthermore, taking the planning starting point (350m, 0m) as the center, and the rolling window distance D... w With a radius of 1000m, the coordinates of the intersection point of this circle and the pre-planned optimal route are (1350m, 0m).

[0105] Step 6: Based on the pre-set judgment principle, determine whether the generated local target point is a feasible target point. If it is feasible, use the generated local target point; if it is not feasible, adopt the protection strategy to generate a new feasible local target point.

[0106] Specifically, the local target point has coordinates of (1350m, 0m). This point is in the environmental situation and therefore is not in the feasible domain. It is an infeasible target point and a protection strategy needs to be adopted to generate a new feasible local target point.

[0107] Furthermore, when the target point generated according to the optimal route target point selection strategy is an infeasible point, a heuristic target point selection strategy needs to be used to select a local target point. Specifically, after each scroll window information update, a heuristic function is used to select a local target point, expressed as follows:

[0108] f(P) = g(P) + h(P)

[0109] Where P is a point on the scrolling window, g(P) is the cost for the UAV to fly from its current position to the target position, which can be estimated based on the position of P and the environmental information within the current window, and h(P) is the cost of flying from P to the destination. Since information outside the boundary is unavailable, h(P) is estimated using the Euclidean distance from P to the destination. Finally, the window boundary point P with the minimum cost is taken as the local target point P. sub When P is obtained sub If there is more than one, you can choose any one, that is:

[0110] minf(P) = g(P) + h(P)

[0111] Furthermore, the specific solution method for the local target point selection strategy can be expressed as follows:

[0112] Step 601: Establish a local rectangular coordinate system with the planning starting point (350m, 0m) as the center, the vector pointing from the starting point to the planning final target point (5550m, 0m) as the x-axis, and the direction perpendicular to the vector as the y-axis.

[0113] Step 602, in this local coordinate system, in Four points (k1 = 4) are randomly generated within the range. The four randomly generated points are arranged in descending order of their corresponding x-coordinates as (1350m, 0m), (1334.8m, 178.6m), (1289.7m, 342m), and (1057.1m, 707.6m).

[0114] Step 603: Following the order of steps 602, find the local target points sequentially according to the designed heuristic function.

[0115] Furthermore, through calculation, it can be found that (1057.1m, 707.6m) is within the feasible region and is a feasible target point. Therefore, this point is taken as a local target point.

[0116] Step 7: Perform route planning using the local planning starting point and the generated local target point, and use the point reached by flying only one step distance along the route as the local planning step arrival point;

[0117] Specifically, with the starting coordinates of the local planning as (350m, 0m) and the coordinates of the local target point as (1057.1m, 707.6m), the step size is D. s =500m, the coordinates of the local planning step are (703.55m, 353.8m).

[0118] Step 8: Determine if the final target point has been reached. If it has, the planning ends; otherwise, return to step 3.

[0119] Specifically, the criterion for reaching the final target point of the plan is: during a certain planning process, the final target point of the plan appears within a circle centered on the starting point of the following local planning path, with the distance of the scrolling window as the radius.

[0120] Specifically, the final target point is located at (5550m, 0m), the starting point for the next round of local planning is (703.55m, 353.8m), and the rolling window distance is D. w =1000m, the termination condition has not been reached. We need to return to step 3 and repeat the process until the condition is met.

[0121] In this embodiment, combined with Figure 2 The rolling optimization execution process specifically includes the following steps:

[0122] Step 1: If the local target point generated according to the optimal route target point selection strategy conflicts with the detected threat environment, then a heuristic target point selection strategy needs to be used to generate local target points.

[0123] Step 2, if all four points (k1 = 4) generated according to the heuristic target point selection strategy are infeasible, then in Within the range, k2 = 4 points are randomly generated and arranged in descending order of their corresponding x-coordinates. Local target points are then searched sequentially according to the designed heuristic function.

[0124] Step 3: If the four points (k2 = 4) generated in Step 2 are also infeasible, then reduce the radius of the scrolling window by 10%, i.e., D. w =900m, return to step 1, until a feasible local target point is found.

[0125] This invention proposes a method for selecting local target points in UAV rolling route planning. Based on real-time measured local environmental information from the UAV, it draws on the principles of the A* algorithm, fully utilizes pre-planned optimal routes, and balances speed and optimality indicators. Corresponding protection strategies are designed to ensure the feasibility of generating local target points during online rolling route planning by the UAV. Specifically, the method includes the following technical effects:

[0126] (1) This method is simple in principle, easy to implement and has a small computational load, and can better meet the speed requirements of online route planning;

[0127] (2) This method can make full use of the local environmental information detected in real time, effectively combine optimization and feedback mechanisms, and realize the reasonable integration of global environmental information and local information detected in real time.

[0128] (3) This method ensures real-time online local planning while also taking into account global optimization indicators. It flexibly avoids obstacles while always approaching the final target point of the plan, ensuring the correctness of the planning direction and without losing the optimality of the entire route.

[0129] (4) This method is highly flexible. Depending on the focus of the problem to be solved, the generation strategy of the target point can be flexibly selected and the parameters can be flexibly set.

[0130] (5) This method has strong versatility and is applicable to both fixed-wing and rotary-wing UAVs. For different models of UAVs, only the parameters related to UAV performance in the algorithm need to be changed to use it directly.

[0131] While the embodiments disclosed in this invention are as described above, they are merely illustrative of the embodiments to facilitate understanding of the invention and are not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in the form and details of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for selecting local target points in rolling flight path planning for unmanned aerial vehicles (UAVs), characterized in that, The method includes: Step 1: Establish and initialize the environment model. The parameter information of the environment model includes: threat zone, obstacle zone, and no-fly zone; Step 2: Determine the starting point of the global planning, the final target point of the global planning, and the pre-planned optimal route; Step 3: Determine the starting point of the local planning based on the starting point of the global planning; Step 4: Update the environmental threat situation based on the environmental model, the starting point of the local planning, and the situation measurement values ​​within the single-step planning window period; Step 5: Determine if there is a pre-planned optimal route. If there is, use the optimal route target point selection strategy to select a local target point on the pre-planned optimal route. If there is no target point selection strategy, use the heuristic target point selection strategy to generate a local target point from the heuristic function. Step 6: Based on the pre-set judgment principle, determine whether the generated local target point is a feasible target point. If the generated local target point is a feasible target point, then the generated local target point is adopted; if the generated local target point is not a feasible target point, then a protection strategy is adopted to generate a new feasible local target point. Step 7: Perform route planning using the local planning starting point and the generated local target point, and use the point reached by flying only one step distance along the route as the local planning step arrival point; Step 8: Determine if the final target point has been reached. If it has, the planning ends; otherwise, return to step 3.

2. The local target point selection method according to claim 1, characterized in that, Step 5 includes:

501. Establish a local rectangular coordinate system with the starting point of the local planning as the center, the vector pointing from the starting point to the final target point of the planning as the x-axis, and the direction perpendicular to the vector as the y-axis. 502, in the local rectangular coordinate system, in K1 points are randomly generated within the range, and the K1 points are arranged in descending order of their corresponding x-coordinates.

503. Following the order of steps 502, find the local target points sequentially using the heuristic function f(P) = g(P) + h(P).

3. The local target point selection method according to claim 2, characterized in that, The heuristic target point selection strategy is as follows: after each update of the scrolling window information, the heuristic function f(P) = g(P) + h(P) is used to select a local target point, where P is a point on the scrolling window, g(P) is the cost of the UAV flying from the current position to the target position, and h(P) is the cost of flying from P to the destination.

4. The local target point selection method according to claim 1, characterized in that, Step 6 involves employing a protection strategy to generate new feasible local target points. The steps are as follows:

601. If the local target point generated according to the optimal route target point selection strategy is located in the threat situation detected by the rolling window, then the heuristic target point selection strategy is used to generate the local target point.

602. If all k1 points generated according to the heuristic target point selection strategy are infeasible, then in k2 points are randomly generated within the range and arranged in descending order of their corresponding x-coordinates. Local target points are then searched sequentially using the heuristic function f(P) = g(P) + h(P).

603. If the k2 points generated in step 602 are also infeasible, reduce the radius of the scrolling window by 10% and return to step 601 until a feasible local target point is found.

5. The local target point selection method according to claim 1, characterized in that, The judgment principle in step 6 is: the generated local target point is located in the feasible region.

6. The local target point selection method according to claim 1, characterized in that, The condition for reaching the final target point in step 8 is: during planning, the final target point of the global planning appears within a circle centered on the starting point of the next round of local planning, with the distance of the scroll window as the radius.

7. The local target point selection method according to claim 1, characterized in that, In step 3, the starting point of the initial planning is the global planning starting point (x1, y1) of the aircraft, and the starting point of each subsequent planning round is the step size reached in the previous planning round.

8. A local target point selection device for rolling flight path planning of unmanned aerial vehicles (UAVs), characterized in that, The device is used to implement the local target point selection method for UAV rolling flight path planning as described in claim 1.

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