Low-energy-consumption unmanned aerial vehicle path planning method for urban low-altitude penetration scene

Abstract

The application discloses a kind of low-energy consumption unmanned aerial vehicle path planning methods of urban low-altitude crossing scene, and this method completes path planning in three stages: first, build the three-dimensional safety buffer zone of obstacle that fuses unmanned aerial vehicle size and safety distance, generate initial straight line path from starting point to terminal point;Second, collision risk section and key obstacle are identified based on spatial geometric relationship, four-direction deterministic local obstacle avoidance strategy is designed, and the shortest detour path is selected to form preliminary obstacle avoidance flight path;Finally, build double-layer optimization framework inside and outside, to construct multi-constraint energy consumption evaluation function with path safety cost, voyage, heading deflection, height change, complete local path point redundancy elimination using three-point sliding window, and complete flight path comprehensive optimization through traversal global iteration, output optimal low-energy consumption flight path;Effectively solve the problem that existing unmanned aerial vehicle path planning algorithm has low search efficiency, poor obstacle avoidance stability, difficult to balance flight safety and low energy consumption under the complex obstacle environment of urban low-altitude.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) technology, specifically a low-energy UAV path planning method for low-altitude urban crossing scenarios. Background Technology

[0002] Path planning, as a key component of autonomous flight of unmanned aerial vehicles (UAVs), directly affects the efficiency of UAV mission execution, flight safety, and energy consumption.

[0003] In the three-dimensional urban low-altitude environment, obstacles are diverse in type and complex in spatial distribution, and passable areas are often characterized by local narrowness, dispersion, and irregularity. Therefore, in such scenarios, drones not only need to fly safely from the starting point to the target point, but also need to meet multiple requirements such as obstacle avoidance, path length control, flight stability, and energy consumption constraints.

[0004] Currently, UAV path planning has developed into a relatively rich technological system, mainly including traditional path planning methods, intelligent optimization methods, and machine learning methods. These methods can all achieve path generation and optimization within a certain application range in different scenarios, and have been widely researched and applied in the field of autonomous UAV flight.

[0005] However, existing path planning technologies still face certain limitations in complex three-dimensional low-altitude scenarios. On the one hand, complex obstacle environments significantly increase the search range and constraint handling difficulty in the path planning process; on the other hand, to ensure flight safety, planned paths usually need to increase obstacle avoidance margins or detours, which in turn increases path complexity, making it difficult to simultaneously achieve path safety and low-energy performance.

[0006] Furthermore, existing path planning methods in complex obstacle environments typically focus more on path feasibility or search mechanism optimization, while failing to fully utilize the spatial geometry of obstacles, obstacle boundary relationships, and potential passable areas. In urban low-altitude environments with dense obstacles and limited flight space, relying solely on global search or stochastic optimization methods can easily lead to problems such as insufficient computational efficiency, numerous redundant path points, and low stability of planning results. Summary of the Invention

[0007] The purpose of this invention is to provide a low-energy UAV path planning method for low-altitude urban crossing scenarios, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a low-energy UAV path planning method for urban low-altitude flight scenarios, comprising the following steps: S1: Construct a 3D model of the obstacle scene and set a regular-shaped safety buffer zone outside the 3D model; S2: Connect the starting point of the drone's flight path in the model. ,end Build the initial path ; S3: Based on obstacle location and initial path , the initial path The path intersecting with the obstacle safety buffer zone is defined as the critical path segment. The spatial relationship between the straight line in space and the geometry of the obstacle 3D model is used to detect, identify and extract the position information of the obstacle and the critical path segment. S4: Introduce a four-directional obstacle avoidance strategy, perform local obstacle avoidance for key obstacles, compare the path lengths in the four directions, and select the shortest obstacle avoidance path among the four directions as the local obstacle avoidance path segment. S5: Connect all local obstacle avoidance path segments and non-critical path segments to form an obstacle avoidance path. ; S6: Combining multiple optimization objectives, plan the optimal path. The optimization framework is divided into two levels: outer optimization and inner optimization. The inner optimization includes the following steps: S61: Decompose the calculation methods for each optimization objective, which includes security threats, path length, turning angle, and elevation difference changes; S611: Safety is quantified by determining the geometric intersection relationship between the obstacle avoidance path and the safety buffer. The threat cost is defined as a penalty function based on the number of intersections between the line segment and the 3D model. The penalty function is as follows: ; ; In the formula, To optimize the cost of the preceding path threat, To optimize the path threat cost, The number of intersections between the obstacle avoidance path and the safety buffer zone; S612: Calculate the path length using the Euclidean distance formula; the path length cost of the original obstacle avoidance path segment. And the cost of optimizing path segment path length The calculation is as follows: ; in, Indicates path length. , , Indicates the path point currently being calculated; S613: Defines heading deflection as the angle between continuous path vectors, and improves path smoothness by selecting a smaller deflection angle; the heading angle cost of the original obstacle avoidance path segment. The cost of yaw angle for optimizing the path segment The calculation is as follows: ; in, This represents the included angle of the vectors of the original obstacle avoidance path segment. This represents the angle between vectors on the optimized path segments. This indicates the number of optimizations for the current path. Indicates the number of groups currently being optimized; S614: Obtain the height increment of the corresponding path segment by subtracting the height coordinates of two adjacent path points. The pitch angle between the path segment and the horizontal plane Calculated using the line-plane angle formula The positive and negative signs represent the drone's ascent and descent, respectively; an energy consumption coefficient is also introduced. To quantify the energy consumption changes caused by changes in elevation; Energy consumption coefficient Piecewise linear function mapping is used: ; in, The baseline energy consumption coefficient is represented when the pitch angle is 0°. This represents the energy consumption coefficient when the descent angle is 90°. This represents the energy consumption coefficient when the climbing angle is 90°. Cost of elevation change in the original obstacle avoidance path segment Cost of elevation change in optimized path segment They are respectively: ; in, Indicates changes in altitude. , , This represents the path points within the current sliding window. express Click Energy consumption coefficient of the path segment between points. express Click Energy consumption coefficient of the path segment between points. express Click Changes in the height of the point express Click Changes in the height of the point; S62: Constructing the energy consumption calculation function during the optimization process: S621: Constructing the path cost function Quantify the overall energy consumption level of the original path point combination: ; S622: Constructing the Optimization Cost Function Quantify the overall energy consumption level of the path after removing intermediate path points: ; S623: Define the global fitness function This intuitively reflects the iterative process of global optimization: ; Where k represents the kth path point as the starting point; This represents the cost function value of the path from the starting point to the k-th path node; n represents the total number of path nodes that need to be optimized; j represents the j-th group on the current optimization path; m represents the total number of groups in the current optimization calculation. This indicates the calculation of the cost function value for the current combination; This indicates the calculation of the optimization cost value for the current combination; S63: Starting from the beginning of the obstacle avoidance path, perform local optimization: S631: Set a sliding window to select three adjacent path points starting from the origin. , , Calculation of local path segments is performed within a window; S632: Calculate the original path cost function value within the current three-point sliding window. and optimization cost function value And compare, when When, remove intermediate path points; when If so, then the intermediate points of the original path are retained; S633: After the current window is optimized, it moves sequentially to the next window position and repeats the same optimization calculation process. To ensure that all path points participate in the optimization process, the previous window... The point is used as the next window point; S634: After the sliding window traverses all path points on the current path, calculate the total cost function value for each path. and optimize value And compare, when Then, repeat the local optimization on the path that has already undergone one local optimization, until... This ends the local optimization of the path from the current starting point; S64: To ensure that inner-layer optimization can effectively extend to every path, outer-layer optimization optimizes the starting point. The inner-layer optimization is driven by local optimization, and the specific steps are as follows: Starting from the path origin, the optimization begins... After performing local optimization on the path, the optimization starting point will be... Move sequentially to the next position until the optimization starting point is met. Traversal After considering the conditions for each path point, the outer optimization process is complete, and the optimal path is obtained.

[0009] Preferably, in step S4, the directions of the four-directional obstacle avoidance strategy include: the shortest path from point A to point B when going up around the top surface of the 3D model, the shortest path from point A to point B when going down around the bottom surface of the 3D model, the shortest path from point A to point B when going left around the left side of the 3D model, and the shortest path from point A to point B when going right around the right side of the 3D model.

[0010] Preferably, in step S614, , , .

[0011] Compared with the prior art, the beneficial effects of the present invention are: (1) The path optimization framework of this invention reduces path complexity while ensuring safety, thereby obtaining a reliable path with lower energy consumption. This improves the safety of the planned path and reduces the flight energy consumption of the path. (2) In this invention, a planar obstacle avoidance strategy is used to transform the uncertain optimization process into a deterministic local obstacle avoidance plan on the basis of establishing a safety buffer zone. This avoids the uncertainty of path planning when avoiding obstacles, reduces the complexity of the obstacle avoidance process, and improves the safety of the path. (3) In the face of complex obstacle environments, this invention uses the geometric relationships of graphics in space to perform calculations in the planning process, which reduces the computational complexity of the algorithm, improves the planning efficiency and robustness of the method, and improves the determinism of path planning. Attached Figure Description

[0012] Figure 1 This is a flowchart of a low-energy UAV path planning method for urban low-altitude crossing scenarios provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the security buffer provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the initial path provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the identification of critical path segments and critical obstacle buffer zones provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of obstacle avoidance in the key obstacle buffer zone provided in an embodiment of the present invention; Figure 6This is a schematic diagram of the calculation process of the four-directional obstacle avoidance strategy provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the obstacle avoidance path provided in an embodiment of the present invention; Figure 8 This is a schematic diagram showing the number of intersections between path segments and obstacle buffer zones provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of yaw angle calculation provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of a three-point sliding window provided in an embodiment of the present invention; Figure 11 This is the optimized starting point provided in the embodiments of the present invention. Schematic diagram of the movement process; Figure 12 This is a schematic diagram of the optimization process provided in an embodiment of the present invention; Figure 13 This is a schematic diagram of the optimal path provided in the embodiments of the present invention; Figure 14 This is a path planning result diagram for a scenario involving traversing a building corridor, provided in an embodiment of the present invention. Figure 15 This is a path optimization curve diagram for a scenario involving traversing a building corridor, provided in an embodiment of the present invention. Figure 16 This is a path planning result diagram for a scenario involving crossing an overpass, provided in an embodiment of the present invention. Figure 17 This is a path optimization curve diagram for a scenario involving crossing an overpass, provided in an embodiment of the present invention. Figure 18 These are 3D views, top views, and side views of path planning results in three general scenarios provided in this embodiment of the invention; Figure 19 This is a curve showing the variation of the comprehensive energy consumption evaluation function value of different algorithms in a low-complexity scenario, as provided in the embodiments of the present invention. Figure 20 This is a curve showing the variation of the comprehensive energy consumption evaluation function value of different algorithms in a moderately complex scenario, as provided in this embodiment of the invention. Figure 21 This is a curve showing the variation of the comprehensive energy consumption evaluation function value of different algorithms in a highly complex scenario, as provided in this embodiment of the invention. Figure 22 This is a statistical chart showing the results of 50 comparative experiments of the best path fitness function of different algorithms in a low-complexity scenario provided by an embodiment of the present invention. Figure 23 This is a statistical graph showing the results of 50 comparative experiments of different algorithms in a moderately complex scenario provided by an embodiment of the present invention, which yielded the optimal path fitness function. Figure 24This is a statistical chart showing the results of 50 comparative experiments of different algorithms in a highly complex scenario provided by an embodiment of the present invention, illustrating the optimal path fitness function. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Please see Figures 1-24 This invention provides a technical solution: a low-energy UAV path planning method for low-altitude urban flight scenarios. This method mainly consists of three stages: The first stage involves constructing a 3D model of the obstacle scenario and planning an initial path; the second stage involves detecting and identifying obstacles that may lead to collisions, performing local obstacle avoidance on threatening path segments, and planning obstacle avoidance paths; the third stage involves optimizing the obstacle avoidance path based on multiple constraints and with low energy consumption as the objective, to obtain a safe, stable, and low-energy optimal path. The detailed process of each part is described below, and the algorithm flowchart is shown below. Figure 1 As shown.

[0015] Example 1: (1) Planning the initial path S1: Construct a 3D model of the obstacle scene, and build a regular-shaped obstacle safety buffer zone (e.g., outside the 3D model) Figure 2 As shown in the blue semi-transparent cuboid, its width is composed of the drone size (2m) and the safety distance (5m). All calculations in the subsequent planning process are based on this buffer.

[0016] S2: Connect the starting point of the drone's flight path in the model. ,end , construct as Figure 3 The initial path shown .

[0017] (2) Plan obstacle avoidance routes S3: Based on obstacle location and initial path , the initial path Paths intersecting with the obstacle safety buffer zone are defined as critical path segments. The spatial relationship between straight lines and the 3D geometry of the obstacle is used for detection, identification, and extraction of the location information of the obstacle and the critical path segment; the results are shown below. Figure 4 The orange line segment in (a) and Figure 4 The orange obstacle in (b).

[0018] S4: Introduces a four-directional obstacle avoidance strategy for localized obstacle avoidance of critical obstacles. For example... Figure 5 As shown, taking a certain obstacle as an example, the two intersection points A and B between the initial path and the obstacle are taken as critical path points.

[0019] S41: Calculate the shortest path from point A to point B along this direction, bypassing the top surface. And calculate the shortest path from point A to point B along this direction, bypassing the bottom surface. ,like Figure 6 As shown in (a) and (b), the coordinates of the critical path point are: F i (x) A ,y A , z H ), F i+1 (x) B, y B , z H ); where H represents the height of the obstacle. Then, calculate the length of each path segment, as shown in equations (1)-(3): (1) (2) (3) Then the path is calculated using equations (4) and (5). and Total length: (4) (5) S42: Calculate the shortest path from point A to point B along the left side, bypassing the left side face. And calculate the shortest path from point A to point B along this direction, bypassing the right side. ,like Figure 6 As shown in (c), (d), and (e); After unfolding along the side, take a point D at the same height as point A on the edge of the buffer zone between points A and B. i A point whose x and y coordinates are the same as the x and y coordinates of the corresponding buffer vertex. First, calculate the length of line segment AC, as shown in equations (5)-(8): (5) (6) (7) (8) (9) Calculate using equation (10) and Total length: (10) Then the critical path points are calculated using equation (11). The coordinates of the point: (11).

[0020] S5: Connect all local obstacle avoidance path segments and non-critical path segments to form an obstacle avoidance path. ,like Figure 7 As shown by the blue line segment.

[0021] S6: Combine multiple optimization objectives to plan the optimal path. The obstacle avoidance path is optimized using a fitness function, and redundant path points are removed from the largest set of obtained path points to obtain a safe, efficient, and low-energy optimal path. The optimization framework consists of two layers. The outer layer optimizes the global path to ensure the global optimality of the algorithm's planned path; the inner layer mainly optimizes local path points to ensure the energy efficiency of the local path. S61: Decompose the calculation methods for each optimization objective, which includes security threats, path length, turning angle, and elevation difference changes; S611: Security is quantified by determining the geometric intersection between a path segment and a security buffer. The threat cost is defined as a penalty function based on the number of intersections between the line segment and the 3D model. The penalty function is as follows: ; ; In the formula, To optimize the cost of the preceding path threat, To optimize the path threat cost, The number of intersections between the obstacle avoidance path and the safety buffer zone; The definition and illustration of path threat cost are shown in Table 1 below. Figure 8 As shown: Table 1. Path Threat Cost Comparison Table

[0022] S612: Calculate the path length using the Euclidean distance formula; the path length cost of the original obstacle avoidance path segment. And the cost of optimizing path segment path length The calculation is as follows: (12) (13) in, Indicates path length. , , This indicates the path point currently being calculated.

[0023] S613: Heading deflection is defined as the angle between continuous track vectors. By selecting a smaller deflection angle, path smoothness can be improved. A diagram illustrating the deflection angle calculation is shown below. Figure 9 As shown; The cost of the yaw angle of the original obstacle avoidance path segment The cost of yaw angle for optimizing the path segment The calculation is as follows: (14) (15) in, This represents the included angle of the vectors of the original obstacle avoidance path segment. The vector angle between the segments of the optimized path is represented by , k represents the number of times the current path has been optimized, and j represents the number of groups being optimized. S614: The height increment h of the corresponding path segment is obtained by subtracting the height coordinates of two adjacent path points; the pitch angle between the path segment and the horizontal plane. Calculated using the line-plane angle formula The positive and negative signs represent the drone's ascent and descent, respectively; an energy consumption coefficient is also introduced. To quantify the energy consumption changes caused by changes in elevation; Energy consumption coefficient Piecewise linear function mapping is used: (16) in, The reference energy consumption coefficient, which represents the pitch angle at 0°, has a value of 1. The energy consumption coefficient represents the energy consumption when the descent angle is 90°, and its value is 0.8. This represents the energy consumption coefficient when the climbing angle is 90°, and its value is 1.2. Cost of elevation change in the original obstacle avoidance path segment Cost of elevation change in optimized path segment They are respectively: (17) (18) in, Indicates changes in altitude. , , This represents the path points within the current sliding window. express Click Energy consumption coefficient of the path segment between points. express Click Energy consumption coefficient of the path segment between points. express Click Changes in the height of the point express Click Changes in the height of a point.

[0024] S62: Constructing the energy consumption calculation function during the optimization process: S621: Constructing the path cost function Quantify the overall energy consumption level of the original path point combination: (19) S622: Constructing the Optimization Cost Function Quantify the overall energy consumption level of the path after removing intermediate path points: (20) S623: Define the global fitness function This intuitively reflects the iterative process of global optimization: (twenty one) Where k represents the kth path point as the starting point; This represents the cost function value of the path from the starting point to the k-th path node; n represents the total number of path nodes that need to be optimized; j represents the j-th group on the current optimization path; m represents the total number of groups in the current optimization calculation. This indicates the calculation of the cost function value for the current combination; This indicates the calculation of the optimization cost value for the current combination.

[0025] S63: Starting from the beginning of the obstacle avoidance path, perform local optimization, such as... Figure 10 As shown: S631: Set a sliding window to select three adjacent path points starting from the origin. , , Calculation of local path segments is performed within a window; S632: Calculate the original path cost function value within the current three-point sliding window. and optimization cost function value And compare, when When, remove intermediate path points; when If so, then the intermediate points of the original path are retained; S633: After the current window is optimized, it moves sequentially to the next window position and repeats the same optimization calculation process. To ensure that all path points participate in the optimization process, the previous window... The point is used as the next window Point; the direction of sliding window movement is as follows Figure 10 As shown, in which, Figure 10 (a) represents the position of the previous sliding window. Figure 10 (b) indicates the position of the next sliding window; S634: After the sliding window traverses all path points on the current path, calculate the total cost function value for each path. and optimize value And compare, when Then, repeat the local optimization on the path that has already undergone one local optimization, until... This ends the local optimization of the path from the current starting point; S64: To ensure that inner-layer optimization can effectively extend to every path, outer-layer optimization optimizes the starting point. The inner-layer optimization is driven by local optimization, and the specific steps are as follows: Starting from the path origin, the optimization begins... After performing local optimization on the path, the optimization starting point will be... Move sequentially to the next position, such as Figure 11 As shown, Figure 11 Figure (a) shows the current optimization starting point, and Figure (b) shows the next optimization starting point; this continues until the optimization starting point is met. Traversal After considering the condition of each path point, since the outer optimization layer ensures that the inner optimization layer can traverse all path points, and the sliding window in the inner optimization layer contains three path points, when the optimization starting point is at the position of n-2 path points, it is exactly the width of the last sliding window. Traversal After the conditions for each path point are met, it indicates that the outer optimization process has ended; the complete optimization process is as follows: Figure 12 As shown, where, Figure 12 (a) is a schematic diagram of the obstacle avoidance path. Figure 12 (b) is a schematic diagram of the optimization process. Figure 12 (c) is a schematic diagram of the optimal path; finally, the entire path planning process is completed, and the optimal path is obtained. ,like Figure 13 As shown by the red line segment.

[0026] Example 2: like Figure 14-17 As shown, Figure 14 This is a path planning result diagram of the method of the present invention in the scenario of traversing a building corridor. Figure 16 This is a diagram showing the path planning results of the method of the present invention in the scenario of crossing an overpass; Figure 15The graph shows the path optimization curve for a scenario involving a connecting corridor through a building. The curve represents the convergence process of the algorithm in optimizing the path by analyzing the changes in the fitness function value. The graph shows that the fitness function value decreases rapidly with increasing iterations, indicating that the method described in this invention can effectively reduce the overall energy consumption of the path. Figure 17 The graph shows the path optimization curve for the scenario of crossing a pedestrian overpass. The curve of the fitness function value represents the convergence process of the algorithm in optimizing the path. As can be seen from the graph, in the scenario of crossing a pedestrian overpass, the fitness function value generally decreases with the increase of the number of iterations. This indicates that the method described in this invention can effectively optimize the obstacle avoidance path and reduce the overall path cost. It further shows that the method has good path planning ability in the scenario of crossing obstacles.

[0027] Example 3: In this embodiment, the path planning method of the present invention was tested in three scenarios: low complexity, medium complexity, and high complexity. The planning results are as follows: Figures 18-21 As shown, where, Figure 18 In the diagram, (1), (2), and (3) represent the 3D view, top view, and side view of the path planning results of this method in three general scenarios, respectively. The path planning method (GET) of this invention is compared with existing UAV path planning methods: RFO and PSO. The results are shown below: Table 2 Comparison of Algorithm Evaluation Metrics in Different Scenarios .

[0028] Table 2 shows that 50 repeated experiments were conducted on different algorithms in three scenarios. The average values ​​of various indicators of the optimal path planned by the three algorithms were statistically analyzed. The statistical results show that the method of the present invention (GET) is superior to the existing algorithms RFO and PSO in all indicators.

[0029] Table 3. Optimal fitness function values ​​of the best paths planned by each algorithm in different scenarios. .

[0030] like Figures 19-21 As shown, Figures 19-21 The curves showing the fitness function values ​​of the three algorithms in low-complexity, medium-complexity, and high-complexity scenarios characterize the optimization process of the three algorithms in the corresponding scenarios. As can be seen from the figure, the convergence value of the GET algorithm is smaller than that of the other two algorithms, indicating that the optimal path planned by the GET algorithm is better.

[0031] in, Figure 19The graph represents the fitness function values ​​of the three algorithms in a low-complexity scenario, illustrating the optimization process of each algorithm in that scenario. As can be seen from the graph, the convergence value of the GET algorithm is smaller than that of the other two algorithms, indicating that the path planned by the GET algorithm is superior in this scenario.

[0032] Figure 20 The graph represents the fitness function values ​​of the three algorithms in a moderately complex scenario, illustrating their optimization process. As can be seen from the graph, the convergence value of the GET algorithm is lower than that of the other two algorithms, indicating that the path planned by the GET algorithm is superior in this scenario.

[0033] Figure 21 The graph represents the fitness function values ​​of the three algorithms in a highly complex scenario, illustrating their optimization process. As can be seen from the graph, the convergence value of the GET algorithm is lower than that of the other two algorithms, indicating that the path planned by the GET algorithm is superior in this scenario.

[0034] like Figures 22-24 As shown, 50 repeated experiments were conducted on different algorithms in three scenarios, and the average of the obtained optimal fitness function values ​​was plotted statistically. The graph shows that the GET algorithm's values ​​are more concentrated, with the peak located on the far left, indicating that the GET algorithm's planning results are more stable and the planned path is better. The PSO algorithm's peak is in the middle, while the RFO algorithm's peak is on the far right. Relatively speaking, the PSO algorithm has the second best stability, while the RFO algorithm has the worst stability. Combining this with the optimal fitness function values ​​in Table 3 yields even clearer results.

[0035] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A low-energy UAV path planning method for low-altitude urban crossing scenarios, characterized in that, Includes the following steps: S1: Construct a 3D model of the obstacle scene and set a regular-shaped safety buffer zone outside the 3D model; S2: Connect the starting point of the drone's flight path in the model. ,end Build the initial path ; S3: Based on obstacle location and initial path , the initial path The path intersecting with the obstacle safety buffer zone is defined as the critical path segment. The spatial relationship between the straight line in space and the geometry of the obstacle 3D model is used to detect, identify and extract the position information of the obstacle and the critical path segment. S4: Introduce a four-directional obstacle avoidance strategy, perform local obstacle avoidance for key obstacles, compare the path lengths in the four directions, and select the shortest obstacle avoidance path among the four directions as the local obstacle avoidance path segment. S5: Connect all local obstacle avoidance path segments and non-critical path segments to form an obstacle avoidance path. ; S6: Combining multiple optimization objectives, plan the optimal path. The optimization framework is divided into two levels: outer optimization and inner optimization. The inner optimization includes the following steps: S61: Decompose the calculation methods for each optimization objective, which includes security threats, path length, turning angle, and elevation difference changes; S611: Safety is quantified by determining the geometric intersection relationship between the obstacle avoidance path and the safety buffer. The threat cost is defined as a penalty function based on the number of intersections between the line segment and the 3D model. The penalty function is as follows: ； ； In the formula, To optimize the cost of threats along the previous path, To optimize the path threat cost, The number of intersections between the obstacle avoidance path and the safety buffer zone; S612: Calculate the path length using the Euclidean distance formula; the path length cost of the original obstacle avoidance path segment. And the cost of optimizing path segment path length The calculation is as follows: ； in, Indicates the path length. , , Indicates the path point currently being calculated; S613: Defines heading deflection as the angle between continuous path vectors, and improves path smoothness by selecting a smaller deflection angle; the heading angle cost of the original obstacle avoidance path segment. The cost of yaw angle for optimizing the path segment The calculation is as follows: ； in, This represents the included angle of the vectors of the original obstacle avoidance path segment. This represents the angle between vectors on the optimized path segments. This indicates the number of optimizations for the current path. Indicates the number of groups currently being optimized; S614: Obtain the height increment of the corresponding path segment by subtracting the height coordinates of two adjacent path points. The pitch angle between the path segment and the horizontal plane Calculated using the line-plane angle formula The positive and negative signs represent the drone's ascent and descent, respectively; an energy consumption coefficient is also introduced. To quantify the energy consumption changes caused by changes in elevation; Energy consumption coefficient Piecewise linear function mapping is used: ； in, The baseline energy consumption coefficient is represented when the pitch angle is 0°. This represents the energy consumption coefficient when the descent angle is 90°. This represents the energy consumption coefficient when the climbing angle is 90°. Cost of elevation change in the original obstacle avoidance path segment Cost of elevation change in optimized path segment They are respectively: ； in, Indicates changes in altitude. , , This represents the path points within the current sliding window. express Click Energy consumption coefficient of the path segment between points. express Click Energy consumption coefficient of the path segment between points. express Click Changes in the height of the point express Click Changes in the height of the point; S62: Constructing the energy consumption calculation function during the optimization process: S621: Constructing the path cost function Quantify the overall energy consumption level of the original path point combination: ; S622: Constructing the Optimization Cost Function Quantify the overall energy consumption level of the path after removing intermediate path points: ; S623: Define the global fitness function This intuitively reflects the iterative process of global optimization: ； Where k represents the kth path point as the starting point; This represents the cost function value of the path from the starting point to the k-th path node; n represents the total number of path nodes that need to be optimized; j represents the j-th group on the current optimization path; m represents the total number of groups in the current optimization calculation. This indicates the calculation of the cost function value for the current combination; This indicates the calculation of the optimization cost value for the current combination; S63: Starting from the beginning of the obstacle avoidance path, perform local optimization: S631: Set a sliding window to select three adjacent path points starting from the origin. , , Calculation of local path segments is performed within a window; S632: Calculate the original path cost function value within the current three-point sliding window. and optimization cost function value And compare, when When, remove intermediate path points; when If so, then the intermediate points of the original path are retained; S633: After the current window is optimized, it moves sequentially to the next window position and repeats the same optimization calculation process. To ensure that all path points participate in the optimization process, the previous window... The point is used as the next window point; S634: After the sliding window traverses all path points on the current path, calculate the total cost function value for each path. and optimize value And compare, when Then, repeat the local optimization on the path that has already undergone one local optimization, until... This ends the local optimization of the path from the current starting point; S64: To ensure that inner-layer optimization can effectively extend to every path, outer-layer optimization optimizes the starting point. The inner-layer optimization is driven by local optimization, and the specific steps are as follows: Starting from the path origin, the optimization begins... After performing local optimization on the path, the optimization starting point will be... Move sequentially to the next position until the optimization starting point is met. Traversal After considering the conditions for each path point, the outer optimization process is complete, and the optimal path is obtained.

2. The low-energy UAV path planning method for urban low-altitude flight scenarios according to claim 1, characterized in that, In step S4, the directions of the four-directional obstacle avoidance strategy include: the shortest path from point A to point B when going up around the top surface of the 3D model, the shortest path from point A to point B when going down around the bottom surface of the 3D model, the shortest path from point A to point B when going left around the left side of the 3D model, and the shortest path from point A to point B when going right around the right side of the 3D model.

3. The low-energy UAV path planning method for urban low-altitude flight scenarios according to claim 1, characterized in that, In step S614, , , .

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