Real-time path planning method and system for fixed-wing unmanned aerial vehicle in simulated flight and electronic equipment

Abstract

The invention discloses a real-time path planning method and system for a fixed-wing unmanned aerial vehicle in simulated flight and electronic equipment. The real-time path planning method comprises the following steps: S1, importing discrete elevation data of a task area; s2, modifying the current route of the unmanned aerial vehicle based on the security constraint, and calculating discrete points between the current point of the unmanned aerial vehicle on the current route and the latest route point; s3, the discrete points are initialized; and S4, planning an optimal path of the unmanned aerial vehicle by using an algorithm and the initialized discrete points. According to the method, the bidirectional A * algorithm is adopted to synchronously initiate search from the starting point and the target point, and the invalid search range in a complex three-dimensional space is greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of unmanned system navigation, and in particular to a real-time path planning method, system and electronic equipment for a fixed-wing unmanned aerial vehicle (UAV) in terrain-following flight. Background Technology

[0002] The path planning problem for unmanned aerial vehicles (UAVs), especially terrain-following missions in complex mountainous terrain, can usually be abstracted as a three-dimensional traveling salesman problem (TSP) with multiple constraints or its variants (such as path planning problems with time windows and dynamic costs). These problems not only require consideration of the order and efficiency of traversing target points, but also necessitate incorporating the UAV's own physical characteristics, flight safety constraints, terrain-following accuracy, and real-time environmental uncertainties into the core considerations to achieve a balance between safety and performance.

[0003] However, as drone applications expand into complex low-altitude and ultra-low-altitude environments, the limitations of such pre-planning methods are becoming increasingly apparent in practical applications, mainly in the following aspects: Insufficient computational efficiency and dynamic adaptability: 3D path planning in complex mountainous environments is an NP-hard problem. To obtain a high-quality solution, pre-planning algorithms often require extensive computation, resulting in long planning times that fail to meet the demands of rapid task response. More importantly, this method is essentially a static or offline planning approach. Once the flight path is generated and execution begins, the system lacks effective online real-time replanning capabilities. When encountering sudden obstacles (such as temporary buildings, cables, or birds), abrupt changes in weather conditions (such as strong crosswinds, updrafts / downdrafts), or receiving new mission instructions, the pre-planned route may instantly become unsafe or no longer optimal.

[0004] Slow safety response in low-altitude environments: During low-altitude terrain-following flight, the distance between the drone and the ground / mountain is very close, resulting in an extremely short window for environmental perception and reaction. Relying on pre-planned flight paths is akin to "following a map," meaning that once the environment deviates from the preset model, the drone cannot autonomously and quickly adjust its path. Existing methods typically rely on preset emergency avoidance strategies (such as emergency climb or hovering), but this not only interrupts the mission but also introduces new collision risks in complex mountainous terrain. The slow-responding path adjustment mechanism itself is a safety hazard in low-altitude, high-speed scenarios.

[0005] The contradiction between model dependence and environmental uncertainty: The reliability of pre-planning methods is highly dependent on the accuracy and completeness of prior environmental modeling. However, complex mountainous areas may have insufficient accuracy in digital elevation data, missing small obstacles, and vegetation height that changes over time. This uncertainty in prior knowledge, coupled with the inherent dynamic uncertainty of the flight environment, makes planning schemes based entirely on pre-flight information inherently vulnerable.

[0006] Failure to fully integrate online perception information: Existing methods typically treat path planning and task execution as two relatively separate stages, resulting in weak closed-loop coupling between the planning module and airborne real-time perception systems (such as lidar, visual sensors, and millimeter-wave radar). Real-time terrain and obstacle information detected by the perception system is difficult to quickly and seamlessly feed back to the planning core and trigger global or local flight path reconfiguration, leading to breakpoints in the "perception-planning-execution" link.

[0007] Therefore, there is an urgent need to provide a solution for a real-time path planning method, system, and electronic equipment for fixed-wing UAVs in terrain-following flight. Summary of the Invention

[0008] To address the above issues, the present invention provides a real-time path planning method, system, and electronic equipment for fixed-wing unmanned aerial vehicles (UAVs) in terrain-following flight. This method considers the safety constraints and characteristics of the UAV and plans the shortest three-dimensional path for the UAV, effectively shortening mission time and enabling efficient mission completion.

[0009] According to a first aspect of the present invention, a real-time path planning method for a fixed-wing unmanned aerial vehicle (UAV) in terrain-following flight is provided, comprising: S1. Import discrete elevation data for the task area; S2. Based on safety constraints, modify the current flight path of the UAV and calculate the discrete points between the current point of the UAV on the current flight path and the latest flight path point. S3. Initialize the discrete points; S4. Utilize the initialized discrete points to plan the optimal path for the UAV. In the above scheme, step S2 includes: S21. Modify the current flight path of the UAV based on its current flight speed, climb rate, and descent rate, and reload the flight path to the latest flight path; S22. Based on the current location information of the UAV and the next waypoint information in the latest route, calculate the discrete point between the two points.

[0010] In the above scheme, step S3 includes: S31. Create two priority queues: one to perform a forward search from the drone's current point, and the other to perform a reverse search from the latest flight path point. S32. Record the actual cost from the drone's current point to each discrete point, and the estimated cost from that discrete point to the latest waypoint.

[0011] In the above scheme, step S4 includes: S41. Perform a forward search, select the discrete point with the minimum sum of actual cost and estimated cost for expansion, and perform a reverse search, select the discrete point with the minimum sum of actual cost and estimated cost for expansion. S42. After each expansion of the discrete point, check whether the currently expanded discrete point appears in the explored list of the other direction. If it does, then the forward and reverse search directions meet at this discrete point. If not, continue to expand. S43. Backtrack from the discrete point where the two search directions meet in the forward and reverse directions, construct paths from the current point of the UAV to the meeting point, and from the latest flight path point to the meeting point, and combine these two paths into the optimal path.

[0012] In the above scheme, step S41 includes: For each selected discrete point, check all its neighboring discrete points. If a neighboring discrete point has not been explored, calculate the actual cost from the UAV's current point to the neighboring discrete point, as well as the estimated cost from the neighboring discrete point to the latest waypoint, and expand the discrete points.

[0013] In the above scheme, step S22 further includes: setting the safe flight altitude range of the UAV based on the terrain height, the lowest elevation point of the flight area and the highest elevation point of the flight area.

[0014] In the above scheme, in step S1, the discrete elevation data includes high-precision elevation information of the terrain.

[0015] In the above scheme, in step S3, the estimated cost is calculated using three-dimensional Manhattan distance.

[0016] According to a second aspect of the present invention, a real-time path planning system for a fixed-wing unmanned aerial vehicle (UAV) in terrain-following flight is provided, characterized in that the system is used to implement the method described in any one of the above solutions, and the system comprises: The import module is used to import discrete elevation data for the task area; The modification module is used to modify the current flight path of the UAV based on safety constraints, and to calculate the discrete points between the current point of the UAV on the current flight path and the latest flight path point. The initialization module is used to initialize the discrete points; The calculation module is used to plan the optimal path for the UAV using the initialized discrete points.

[0017] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the method described in any of the above schemes.

[0018] The beneficial effects of this invention are: By decoupling the core path planning algorithm from specific UAV dynamics models and performance parameters, a high degree of modularity is achieved. This allows the same planning engine to be quickly adapted to various UAV configurations by loading different aircraft characteristic configuration files, greatly enhancing the technology's versatility and commercial potential. Employing a bidirectional A* algorithm, the search is initiated simultaneously from both the starting and target points, significantly reducing the invalid search range in complex 3D space. Combined with an industrial computer, rapid replanning is possible. It can cope with sudden low-altitude obstacles, temporary mission changes, or weather interference. It reduces reliance on continuous remote control or ground station commands, enhancing the UAV's independent operation capability and survivability in environments with poor communication. Planning the shortest path results in shorter flight time and less energy consumption, enabling drones to complete missions efficiently and cover a larger area or perform more missions in a single takeoff and landing. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the steps of the real-time path planning method for a fixed-wing UAV during terrain-following flight in this invention. Figure 2 This is a flowchart of the real-time path planning method for a fixed-wing UAV in terrain-following flight according to the present invention; Figure 3 This is a schematic diagram showing the altitude of the new flight mission area after the UAV changes its flight path and the discrete mission flight area of ​​the UAV in this embodiment of the invention; Figure 4 This is a schematic diagram of the optimal path of the UAV planned by the bidirectional A* algorithm in an embodiment of the present invention; Figure 5 This is a time comparison chart of real-time path planning and global path planning in an embodiment of the present invention.

[0021] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0023] The terms "first," "second," etc., used in this disclosure are for distinguishing similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0024] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0025] Multiple, including two or more.

[0026] And / or, it should be understood that, for the purposes of this disclosure, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0027] The bidirectional A* algorithm used in this invention is an improved version of the traditional A* algorithm. It accelerates the path finding process by simultaneously searching from the start and end points, and is a highly efficient algorithm commonly used in path planning. Specifically, one search proceeds from the start point to the end point (forward search), and the other search proceeds from the end point to the start point (reverse search). The two searches stop when they "meet" at some point in between. The meeting condition is when the open sets of the two searches intersect or when a node is visited by both searches simultaneously. When the two searches meet, the path consists of three parts: the forward path from the start point to the meeting point; the reverse path from the meeting point to the end point (which needs to be reversed); and connecting these two parts yields the complete path. Therefore, the bidirectional A* algorithm significantly reduces the number of search nodes, making it particularly suitable for large-scale search spaces, and it performs best in uniform cost graphs.

[0028] like Figure 1 and Figure 2As shown, a first aspect of the present invention provides a real-time path planning method for a fixed-wing unmanned aerial vehicle (UAV) in terrain-following flight, comprising: S1. Import discrete elevation data for the task area; S2. Based on safety constraints, modify the current flight path of the UAV and calculate the discrete points between the current point of the UAV on the current flight path and the latest flight path point. S3. Initialize the discrete points using the double A* algorithm; S4. Utilize the double A* algorithm and initialized discrete points to plan the optimal path for the UAV.

[0029] In step S1, the discrete elevation data includes high-precision elevation information of the terrain. This data is imported from existing map libraries, such as Google Maps and Amap (Gaode Maps), providing an accurate 3D terrain environment model for the entire route planning process. Furthermore, the high-precision elevation information is fundamental for calculating safe flight altitudes and assessing route feasibility and cost. Utilizing existing map libraries, the data can be quickly applied to most parts of the world without the need for prior deployment of dedicated surveying equipment, reducing system deployment costs and complexity. Step S2 includes: S21. Modify the current flight path of the UAV based on its current flight speed, climb rate, and descent rate, and reload the flight path to the latest flight path; S22. Based on the current location information of the UAV and the next waypoint information in the latest route, calculate the discrete point between the two points.

[0030] Specifically, during flight, the ground station modifies the UAV's original flight path through flight path setting and waypoint entry commands, and re-sets the flight path. The ground station then uploads and saves the latest flight path to the industrial computer. Based on the UAV's current location information and the next waypoint information in the latest flight path, the industrial computer recalculates the discrete points between the two points and sets the UAV's safe flight altitude. The calculation formula is as follows: (1) Among them, h i This represents the terrain elevation at point i. This indicates the safe altitude at which the drone can remain when it reaches point t. This represents the safe flight altitude of the lowest waypoint on the entire flight path of the drone. It can be obtained by adding the safe flight altitude to the lowest elevation point of the entire flight area. h represents the safe flight altitude of the highest point on the entire flight path of the UAV. It is obtained by adding the safe flight altitude to the highest elevation point of the entire flight area. safe This indicates the safe flight altitude range for the drone at the current point. This indicates the discrete precision of the safe flight altitude range. Indicates the number of discrete precision values.

[0031] It ensures that the drone stays above the terrain at any waypoint and provides room for optimization of the path planning algorithm by setting a safe altitude range, allowing for the search of more energy-efficient or shorter paths while ensuring safety.

[0032] In step S3, discrete points are initialized based on the UAV's flight speed, climb rate, and descent rate, as follows: S31. Create two priority queues: one to perform a forward search from the drone's current point, and the other to perform a reverse search from the latest flight path point. S32. Record the actual cost from the starting point to each discrete point, and the estimated cost from that discrete point to the end point.

[0033] Specifically, the current point of the UAV is the starting point, the latest flight path point is the ending point, and the discrete points between the starting point and the ending point are path nodes. Therefore, it is necessary to calculate the heuristic function from the starting point to the ending point. In this invention, the three-dimensional Manhattan distance is used to calculate the heuristic function. Let the starting point be... Node is The formula for calculating the actual cost is as follows: (2) Therefore, when calculating three-dimensional distance, let the starting point be... Node is The actual calculation formula is as follows: (3) The heuristic function is shown below: (4) in, It is the actual cost from the starting point to node n. It is the estimated cost from node n to the destination, and f(n) is the sum of the actual cost and the estimated cost from the starting point to the destination.

[0034] This invention employs a bidirectional search, simultaneously unfolding from the starting point and the ending point, significantly reducing the number of nodes that need to be explored. This is particularly effective in large-scale or complex terrain areas, drastically shortening the time required to find the optimal path. The three-dimensional Manhattan distance calculation is simple and fast, providing effective directional guidance for the algorithm, accelerating convergence, and ensuring that the path found is the most cost-efficient.

[0035] Step S4 includes: S41. Perform a forward search, select the node with the smallest sum of actual cost and estimated cost f(n) for expansion, remove it from the open list, perform a reverse search, and select the node with the smallest sum of actual cost and estimated cost for expansion f(n). S42. After each node expansion, check if the currently expanded node appears in the explored list of the other direction. If it does, then the forward and reverse search directions meet at this node. If not, continue expanding. S43. Backtrack from the node where the two search directions meet in the forward and reverse directions to construct paths from the starting point to the meeting point and from the ending point to the meeting point. Combine these two paths to form the optimal path.

[0036] In step S41, a node in the open list is a selectable node; if a node is removed from the open list, it becomes unselectable.

[0037] Step S41 specifically includes: for each selected node, checking all its neighboring nodes; if a neighboring node has not been explored, calculating the actual cost from the starting point to the neighboring node. And the estimated cost from adjacent nodes to the destination. And add it to the corresponding open list. If the neighboring node already exists in the open list, and the current path provides a smaller... The value will be updated by setting the selected node to its neighboring nodes and updating... The value indicates that the previously selected node is the parent node of the updated node.

[0038] According to a second aspect of the present invention, a real-time path planning system for a fixed-wing unmanned aerial vehicle (UAV) in terrain-following flight is provided. The system is used to implement the method described in any one of the above solutions, and the system includes: The import module is used to import discrete elevation data for the task area; The modification module is used to modify the current flight path of the UAV based on safety constraints, and to calculate the discrete points between the current point of the UAV on the current flight path and the latest flight path point. The initialization module is used to initialize the discrete points; The calculation module is used to plan the optimal path for the UAV using the initialized discrete points.

[0039] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the method described in any of the above schemes.

[0040] Example In this embodiment, the simulation experiment is set with a task area size of 5km × 10km, and the distance between the aircraft's current point and the latest waypoint is 7km. The path planning method of this invention is used, and the experimental results are as follows: Figures 3-5 As shown.

[0041] Figure 3 This diagram illustrates the altitude of the new flight mission area after the UAV's flight path is changed, along with the calculated discrete mission flight area. Blue dots represent discrete points in the real-time flight area, and red dots represent terrain altitudes. This verifies the transformation of continuous terrain into a discrete feasible space that the algorithm can process. Figure 4 The optimal path for the UAV planned using the bidirectional A* algorithm is shown in the figure. As can be seen, the final generated path smoothly conforms to the terrain undulations and selects the route with the lowest overall cost while ensuring a safe altitude. This demonstrates the ability of the bidirectional A* algorithm to generate feasible and optimized paths under complex constraints. Figure 5 The graph shows a time comparison between real-time path planning and global path planning. By comparing the time with traditional global path planning, the performance advantages brought by bidirectional search and real-time replanning initialization are significantly demonstrated. The substantial reduction in planning time is the key to achieving real-time path planning in this invention, enabling UAVs to quickly respond to route change commands during flight.

[0042] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0043] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0044] Through the above description of the embodiments, those skilled in the art can clearly understand that the above implementation methods can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0045] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A real-time path planning method for a fixed-wing unmanned aerial vehicle (UAV) in terrain-following flight, characterized in that, include: S1. Import discrete elevation data for the task area; S2. Based on safety constraints, modify the current flight path of the UAV and calculate the discrete points between the current point of the UAV on the current flight path and the latest flight path point. S3. Initialize the discrete points; S4. Utilize the algorithm and the initialized discrete points to plan the optimal path for the UAV.

2. The real-time path planning method for a fixed-wing UAV in terrain-following flight according to claim 1, characterized in that, Step S2 includes: S21. Based on the current flight speed, climb rate, and descent rate of the UAV, modify the current flight path of the UAV to form the latest flight path; S22. Based on the current location information of the UAV and the next waypoint information in the latest route, calculate the discrete point between the two points.

3. The real-time path planning method for a fixed-wing UAV in terrain-following flight according to claim 1, characterized in that, Step S3 includes: S31. Create two priority queues: one to perform a forward search from the drone's current point, and the other to perform a reverse search from the latest flight path point. S32. Record the actual cost from the current point of the drone to each discrete point, and the corresponding estimated cost from that discrete point to the latest flight path.

4. The real-time path planning method for a fixed-wing UAV in terrain-following flight according to claim 1, characterized in that, Step S4 includes: S41. Perform a forward search, select the discrete point with the minimum sum of actual cost and estimated cost for expansion, and perform a reverse search, select the discrete point with the minimum sum of actual cost and estimated cost for expansion. S42. After each expansion of the discrete point, check whether the currently expanded discrete point appears in the explored list of the other direction. If it does, then the forward and reverse search directions meet at this discrete point. If not, continue to expand. S43. Backtrack from the discrete point where the two search directions meet in the forward and reverse directions, construct paths from the current point of the UAV to the meeting point, and from the latest flight path point to the meeting point, and combine these two paths into the optimal path.

5. The real-time path planning method for a fixed-wing UAV in terrain-following flight according to claim 4, characterized in that, Step S41 includes: For each selected discrete point, check all its neighboring discrete points. If a neighboring discrete point has not been explored, calculate the actual cost from the UAV's current point to the neighboring discrete point, as well as the estimated cost from the neighboring discrete point to the latest waypoint, and expand the discrete points.

6. The real-time path planning method for a fixed-wing UAV in terrain-following flight according to claim 2, characterized in that, Step S22 also includes: setting the safe flight altitude range of the UAV based on the terrain height, the lowest elevation point of the flight area, and the highest elevation point of the flight area.

7. The real-time path planning method for a fixed-wing UAV in terrain-following flight according to claim 1, characterized in that, In step S1, the discrete elevation data includes high-precision elevation information of the terrain.

8. The real-time path planning method for a fixed-wing UAV in terrain-following flight according to claim 3, characterized in that, In step S3, the estimated cost is calculated using three-dimensional Manhattan distance.

9. A real-time path planning system for a fixed-wing unmanned aerial vehicle (UAV) in terrain-following flight, characterized in that, The system is used to implement the method as described in any one of claims 1-8, the system comprising: The import module is used to import discrete elevation data for the task area; The modification module is used to modify the current flight path of the UAV based on safety constraints, and to calculate the discrete points between the current point of the UAV on the current flight path and the latest flight path point. The initialization module is used to initialize the discrete points; The calculation module is used to plan the optimal path for the UAV using the initialized discrete points.

10. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the method of any one of claims 1-8.

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