Aircraft path planning method, aircraft and storage medium

Abstract

The embodiment of the invention provides an aircraft path planning method, an aircraft and a storage medium. The aircraft path planning method comprises the steps that path planning reference information of an aircraft is acquired, and the path planning reference information comprises a path planning starting point position and a path planning ending point position; a path planning data set associated with the path planning reference information is determined, and the path planning data set comprises topographic data, obstacle data, environment data and aircraft attribute data associated with the path planning reference information; a target navigation path of the aircraft is generated according to the path planning data set, the target navigation path is used for describing a flight plan adopted when the aircraft passes through multiple target waypoints, and the multiple target waypoints are determined based on the path planning starting point position and the path planning ending point position. According to the invention, the technical problem of poor flight safety of the hovercar caused by low precision of path planning in the prior art is solved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of intelligent navigation and path planning, in particular, to a method for path planning of a flying vehicle, a flying vehicle and a storage medium. BACKGROUND

[0002] The development of the navigation system of the flying vehicle as a new type of transportation tool faces many challenges. At present, the flying vehicle mainly relies on two-dimensional maps and simple airspace data for path planning. However, in a complex three-dimensional environment, the height change of the terrain and the position of the obstacles cannot be considered when relying on two-dimensional maps for path planning. The existing path planning method cannot fully consider the restrictions of various factors on flight, resulting in limited accuracy and safety of path planning. In particular, when flying in a densely populated urban area, it may cause flight accidents and reduce the safety of the flying vehicle. Therefore, how to improve the accuracy of path planning to improve the flight safety of the flying vehicle is one of the important technical problems in the related technical field.

[0003] At present, there is no good solution to the above problems. SUMMARY

[0004] Embodiments of the present application provide a method for path planning of a flying vehicle, a flying vehicle and a storage medium to at least solve the technical problem of low accuracy of path planning in the related art, which leads to poor flight safety of the flying vehicle.

[0005] According to an aspect of an embodiment of the present application, a method for path planning of a flying vehicle is provided, comprising: obtaining path planning reference information of the flying vehicle, wherein the path planning reference information comprises a path planning starting point position and a path planning ending point position; determining a path planning data set associated with the path planning reference information, wherein the path planning data set comprises terrain data, obstacle data, environmental data and flying vehicle attribute data associated with the path planning reference information; and generating a target navigation path of the flying vehicle according to the path planning data set, wherein the target navigation path is used to describe a flight plan of the flying vehicle when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning starting point position and the path planning ending point position.

[0006] Optionally, generating the target navigation path of the flying vehicle according to the path planning data set comprises: generating a plurality of candidate navigation paths for the flying vehicle according to the path planning data set; performing path feasibility evaluation on the plurality of candidate navigation paths by using a target optimization function, and selecting the target navigation path from the plurality of candidate navigation paths.

[0007] Optionally, generating the plurality of candidate navigation paths for the aerial vehicle based on the path planning dataset comprises: obtaining, based on the path planning dataset, a first weight corresponding to the terrain data, a second weight corresponding to the obstacle data, a third weight corresponding to the environment data, and a fourth weight corresponding to the aerial vehicle attribute data, wherein the first weight, the second weight, the third weight, and the fourth weight are used to distinguish the importance of the terrain data, the obstacle data, the environment data, and the aerial vehicle attribute data in the path planning process; performing passability analysis on the terrain data, the obstacle data, the environment data, and the aerial vehicle attribute data based on the first weight, the second weight, the third weight, and the fourth weight to generate the plurality of candidate navigation paths.

[0008] Optionally, performing the passability analysis on the terrain data, the obstacle data, the environment data, and the aerial vehicle attribute data based on the first weight, the second weight, the third weight, and the fourth weight to generate the plurality of candidate navigation paths comprises: performing the passability analysis on the terrain data, the obstacle data, the environment data, and the aerial vehicle attribute data based on the first weight, the second weight, the third weight, and the fourth weight to filter a plurality of candidate waypoints from a plurality of initial waypoints contained in a to-be-planned geographical region range, wherein the to-be-planned geographical region range is determined based on a path planning start point position and a path planning end point position; and generating the plurality of candidate navigation paths using the plurality of candidate waypoints.

[0009] Optionally, the aerial vehicle path planning method further comprises: in response to a data mutation occurring in a part of the terrain data, the obstacle data, the environment data, and the aerial vehicle attribute data, readjusting the weight distribution among the first weight, the second weight, the third weight, and the fourth weight to obtain an adjustment result; and based on the adjustment result, performing local path re-planning on at least part of the plurality of candidate navigation paths.

[0010] Optionally, the path feasibility evaluation of the plurality of candidate navigation paths using the target optimization function to select the target navigation path from the plurality of candidate navigation paths comprises: performing the path feasibility evaluation of the plurality of candidate navigation paths using the target optimization function to obtain an evaluation result, wherein the evaluation result is used to comprehensively evaluate the plurality of candidate navigation paths based on a plurality of evaluation indexes, and the plurality of evaluation indexes comprise: a path length index, an aerial vehicle energy consumption index, and a safety coefficient index; and based on the evaluation result, the target navigation path is selected from the plurality of candidate navigation paths.

[0011] Optionally, determining the path planning dataset associated with the path planning reference information comprises: determining the path planning dataset associated with the path planning reference information from a plurality of dimensional data sources, wherein the plurality of dimensional data sources comprise at least part of the following data sources: a public channel data source; an aerial vehicle self-collected data source; a third-party channel data source; a multi-party fusion data source; and a special scenario data source.

[0012] According to another aspect of the embodiments of the present application, a method for planning a path of an aerial vehicle is also provided. The method includes: sending path planning reference information of the aerial vehicle to a cloud server, wherein the path planning reference information includes a path planning start point position and a path planning end point position; receiving a target navigation path from the cloud server, wherein the target navigation path is generated according to a path planning data set, the path planning data set is determined based on the path planning reference information, the path planning data set includes terrain data, obstacle data, environment data and aerial vehicle attribute data associated with the path planning reference information, the target navigation path is used to describe a flight plan of the aerial vehicle when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning start point position and the path planning end point position; and displaying the target navigation path.

[0013] According to another aspect of the embodiments of the present application, an apparatus for planning a path of an aerial vehicle is also provided. The apparatus includes: an obtaining module configured to obtain path planning reference information of the aerial vehicle, wherein the path planning reference information includes a path planning start point position and a path planning end point position; a determining module configured to determine a path planning data set associated with the path planning reference information, wherein the path planning data set includes terrain data, obstacle data, environment data and aerial vehicle attribute data associated with the path planning reference information; and a generating module configured to generate a target navigation path of the aerial vehicle according to the path planning data set, wherein the target navigation path is used to describe a flight plan of the aerial vehicle when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning start point position and the path planning end point position.

[0014] According to another aspect of the embodiments of the present application, an apparatus for planning a path of an aerial vehicle is also provided. The apparatus includes: a sending module configured to send path planning reference information of the aerial vehicle to a cloud server, wherein the path planning reference information includes a path planning start point position and a path planning end point position; a receiving module configured to receive a target navigation path from the cloud server, wherein the target navigation path is generated according to a path planning data set, the path planning data set is determined based on the path planning reference information, the path planning data set includes terrain data, obstacle data, environment data and aerial vehicle attribute data associated with the path planning reference information, the target navigation path is used to describe a flight plan of the aerial vehicle when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning start point position and the path planning end point position; and a displaying module configured to display the target navigation path.

[0015] According to another aspect of the embodiments of the present application, an aerial vehicle is also provided. The aerial vehicle includes: a memory configured to store an executable program; and a processor configured to run the executable program, wherein the executable program performs the method for planning a path of an aerial vehicle in the embodiments of the present application when running.

[0016] According to another aspect of the embodiments of the present application, a computer readable storage medium is also provided, which includes a stored executable program, wherein the executable program controls the device where the readable storage medium is located to perform the aircraft path planning method in various embodiments of the present application when the executable program is running.

[0017] According to another aspect of the embodiments of the present application, a computer program product is also provided, which includes a computer program, and the computer program implements the aircraft path planning method in various embodiments of the present application when executed by a processor.

[0018] According to another aspect of the embodiments of the present application, a computer program product is also provided, which includes a non-volatile computer readable storage medium, and the non-volatile computer readable storage medium stores a computer program, and the computer program implements the aircraft path planning method in various embodiments of the present application when executed by a processor.

[0019] According to another aspect of the embodiments of the present application, a computer program is also provided, and the computer program implements the aircraft path planning method in various embodiments of the present application when executed by a processor.

[0020] In the embodiments of the present application, first, the path planning reference information of the aircraft is acquired, wherein the path planning reference information includes a path planning starting point position and a path planning ending point position; then, the path planning data set associated with the path planning reference information is determined, wherein the path planning data set includes terrain data, obstacle data, environmental data and aircraft attribute data associated with the path planning reference information; finally, the target navigation path of the aircraft is generated according to the path planning data set, wherein the target navigation path is used to describe the flight plan of the aircraft when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning starting point position and the path planning ending point position. The present application first acquires the path planning starting point position and the path planning ending point position, which provides necessary position information for subsequent path calculation; then, the path planning data set containing terrain, obstacles, environmental factors and aircraft attributes is constructed, and the constructed path planning data set comprehensively considers the key external factors and internal factors affecting flight, thereby providing comprehensive data support for path planning, so as to more accurately evaluate the flight conditions of the aircraft in the three-dimensional space and improve the accuracy of path planning; finally, based on the analysis of the path planning data set, the target navigation path of the aircraft is generated, and the path of the generated target navigation path can avoid obstacles, adapt to terrain changes, consider aircraft performance and environmental factors, thereby effectively improving the accuracy and safety of aircraft path planning and reducing the risk in the flight process. Therefore, when the aircraft is a flying car, the present application can improve the accuracy of path planning to improve the flight safety of the flying car, thereby solving the technical problem of low accuracy of path planning in the related art, which leads to poor flight safety of the flying car. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 This is a flowchart of an aircraft path planning method according to an embodiment of this application;

[0023] Figure 2 This is a flowchart of another aircraft path planning method according to an embodiment of this application;

[0024] Figure 3 This is a flowchart of a flying car path planning method according to an embodiment of this application;

[0025] Figure 4 This is a structural block diagram of an aircraft path planning device according to an embodiment of this application;

[0026] Figure 5 This is a structural block diagram of another aircraft path planning device according to an embodiment of this application. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] According to the embodiment of the present application, a method embodiment of an aircraft path planning method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0030] An aircraft path planning method is provided in the embodiment, Figure 1 is a flowchart of an aircraft path planning method according to the embodiment of the present application, as shown in Figure 1 , the flow includes the following steps:

[0031] Step S11, obtaining path planning reference information of the aircraft, wherein the path planning reference information includes: path planning starting point position and path planning end point position;

[0032] The aircraft in the present application refers to a mechanical device capable of controlled flight within the atmosphere, including manned aircraft and unmanned aircraft.

[0033] In the following, the aircraft will be specifically taken as a flying car to introduce the scheme of the present application.

[0034] Before the flight task of the flying car starts, the path planning reference information of the flying car is first obtained, including the path planning starting point position and the path planning end point position.

[0035] The path planning starting point position represents the exact geographical position coordinates (longitude, latitude, altitude) of the flying car ready for takeoff, which is the starting point of path planning; the path planning end point position represents the exact geographical position coordinates of the destination of the flying car, which is the terminal target of path planning.

[0036] In an optional embodiment, the path planning reference information can be input by the user through the control interface of the flying car.

[0037] By determining the path planning starting point position and the path planning end point position, the path planning method focuses on the flight space between the two points, providing a basic spatial framework for path selection. Determining the path planning starting point position and the path planning end point position is a prerequisite for determining the flight path, ensuring the effectiveness and pertinence of path planning.

[0038] Step S12, determining the path planning data set associated with the path planning reference information, wherein the path planning data set includes: terrain data, obstacle data, environment data, aircraft attribute data associated with the path planning reference information;

[0039] A path planning dataset related to the path planning start position and the path planning end position is determined. The path planning dataset not only contains static terrain data and aircraft attribute data, but also contains dynamic obstacle data and environmental data.

[0040] The terrain data refers to the natural and man-made landscape data of the area involved in flight path planning, including but not limited to elevation, slope, vegetation, urban buildings, and terrain texture.

[0041] Obstacle data not only covers fixed obstacles such as towers and bridges, but also includes dynamic obstacles such as other aircraft, birds, and temporary large facilities.

[0042] Environmental data refers to external natural environmental data that affects the flight of the flying car, including but not limited to wind speed, air pressure, temperature, humidity, light intensity, visibility, precipitation, and snow depth.

[0043] Aircraft attribute data relates to the physical characteristics and operating performance of the flying car, including but not limited to the maximum flight height, maximum flight speed, minimum turning radius, maximum climb angle, fuel type, range, flight state monitoring data, etc.

[0044] A comprehensive path planning dataset is constructed to enable the implementation of path planning to consider multiple key factors, improve the accuracy of path planning, and enhance the safety of the path.

[0045] Step S13, generating a target navigation path for the aircraft based on the path planning dataset, wherein the target navigation path is used to describe the flight plan adopted by the aircraft when passing through multiple target waypoints, and the multiple target waypoints are determined based on the path planning start position and the path planning end position.

[0046] Based on the determined path planning dataset, an optimal flying car flight path, i.e. the target navigation path, is calculated through a path planning algorithm.

[0047] Optionally, the path planning algorithm includes but is not limited to A* algorithm, Rapidly-exploring Random Trees (RRT), Dijkstra algorithm, genetic algorithm, etc.

[0048] The target navigation path describes the flight trajectory of the flying car in three-dimensional space from the path planning start position to the path planning end position, including multiple target waypoints and the flight plan adopted when passing through the multiple target waypoints, such as specific flight height, flight direction, and flight speed.

[0049] The target waypoint refers to a key point on the flight path, and is intended to guide the flying car to perform flight conversion at a proper height, direction and speed, so as to avoid obstacles and adverse environment, while optimizing flight performance.

[0050] In an optional embodiment, the position point meeting the safe flight condition of the flying car is selected from the flight space determined based on the path planning start position and the path planning end position as the target waypoint.

[0051] Based on the steps S11 to S13, first, the path planning reference information of the flying vehicle is acquired, wherein the path planning reference information includes the path planning start position and the path planning end position; then, the path planning data set associated with the path planning reference information is determined, wherein the path planning data set includes the terrain data, obstacle data, environmental data and flying vehicle attribute data associated with the path planning reference information; finally, the target navigation path of the flying vehicle is generated based on the path planning data set, wherein the target navigation path is used to describe the flight plan of the flying vehicle when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning start position and the path planning end position. The path planning start position and the path planning end position are first acquired in the present application, which provides necessary position information for subsequent path calculation; then, the path planning data set containing the terrain, obstacles, environmental factors and flying vehicle attributes is constructed, and the constructed path planning data set comprehensively considers the key external factors and internal factors affecting flight, thereby providing comprehensive data support for path planning, so as to more accurately evaluate the flight conditions of the flying vehicle in the three-dimensional space and improve the accuracy of path planning; finally, based on the analysis of the path planning data set, the target navigation path of the flying vehicle is generated, and the path of the generated target navigation path can avoid obstacles, adapt to terrain changes, consider the performance of the flying vehicle and environmental factors, thereby effectively improving the accuracy and safety of the path planning of the flying vehicle and reducing the risk in the flight process. Therefore, when the flying vehicle is a flying car, the present application can achieve the technical effect of improving the accuracy of path planning to improve the flight safety of the flying car, thereby solving the technical problem of low accuracy of path planning in the related art, which leads to poor flight safety of the flying car.

[0052] The flying vehicle path planning method in the embodiments of the present application will be further introduced below.

[0053] In an optional embodiment, in step S13, generating the target navigation path of the flying vehicle based on the path planning data set includes the following steps:

[0054] Step S131, generating a plurality of alternative navigation paths for the flying vehicle based on the path planning data set;

[0055] Optionally, the path planning dataset includes, but is not limited to, real-time three-dimensional map data, air vehicle performance parameters, airspace regulation rules, etc. According to the collected path planning dataset, a plurality of possible alternative navigation paths are generated to cover a plurality of flight schemes from the path planning starting point position to the path planning end point position.

[0056] The real-time three-dimensional map data is detailed map data containing information such as terrain features, obstacle distribution, building height, and real-time weather conditions within the flight area of the air vehicle. Through high-precision three-dimensional modeling and data fusion, the real-time three-dimensional map data provides a basic reference for path planning.

[0057] The air vehicle performance parameters include key performance indicators such as the maximum flight speed, maximum climb rate, minimum turn radius, and flight height range of the air vehicle. The air vehicle performance parameters limit the movement ability of the air vehicle and are constraint conditions that must be considered in the path planning process.

[0058] For example, based on the real-time three-dimensional map data and the air vehicle performance parameters, a plurality of flight corridors are created, where the flight corridor refers to an area in which the air vehicle can safely move, and then the RRT algorithm is used to generate a plurality of alternative navigation paths of the air vehicle from the starting point to the end point based on the constraints of the flight corridor.

[0059] In step S132, the target optimization function is used to evaluate the path feasibility of the plurality of alternative navigation paths, and a target navigation path is selected from the plurality of alternative navigation paths.

[0060] After generating a plurality of alternative navigation paths, the target optimization function is used to evaluate these paths, and finally a target navigation path that meets the safety, efficiency requirements of the air vehicle is selected.

[0061] The target optimization function is a core mathematical model in the path planning algorithm, and is used to evaluate and compare the comprehensive performance of different paths, thereby helping the air vehicle to determine the best flight route while following the principles of safety, efficiency, and comfort. In the path planning of the air vehicle, the target optimization function considers a plurality of factors, including but not limited to flight distance, energy consumption, safety risk, and flight time.

[0062] The plurality of alternative navigation paths generated can fully reflect the complexity and diversity of the flight environment of the air vehicle, including not only static terrain and building information, but also dynamic obstacles and weather conditions, thereby increasing the flexibility and safety of flight route selection. In addition, by applying the target optimization function, each alternative navigation path can be comprehensively evaluated and quantified to determine the pros and cons of each path, thereby achieving scientific and refined path selection.

[0063] In an optional embodiment, in step S131, generating a plurality of candidate navigation paths for the aerial vehicle according to the path planning dataset comprises the following steps:

[0064] In step S1311, according to the path planning dataset, a first weight corresponding to the terrain data, a second weight corresponding to the obstacle data, a third weight corresponding to the environment data, and a fourth weight corresponding to the aerial vehicle attribute data are obtained, wherein the first weight, the second weight, the third weight, and the fourth weight are used to distinguish the importance of the terrain data, the obstacle data, the environment data, and the aerial vehicle attribute data in the path planning process.

[0065] In an optional embodiment, the obtained path planning dataset is input into a machine learning model to obtain the weights corresponding to the different types of data. The machine learning model is trained based on historical flight data of the flying car. The historical flight data includes historical terrain data, historical obstacle data, historical environment data, historical aerial vehicle attribute parameters, and records of problems and accidents encountered during the historical flight process.

[0066] In addition, a probability model is constructed to analyze the different types of data contained in the path planning dataset, and then weights are assigned according to the potential probability of each influencing factor leading to an increase in flight risk or cost. The probability model is used to evaluate the potential influence of different influencing factors on the success, safety, and efficiency of the flight mission. Each influencing factor corresponds to a type of data, and the influencing factors are terrain factors, obstacle factors, environmental factors, and aerial vehicle attribute factors. The basic principle of weight assignment is that the higher the potential probability of a factor leading to an increase in flight risk or cost, the higher its weight in path planning, i.e., the higher its importance in path planning, to ensure that these factors are given priority and avoided.

[0067] In the construction of the probability model, in addition to the Bayesian network, Markov model, decision tree, or random forest method can also be used.

[0068] In step S1312, based on the first weight, the second weight, the third weight, and the fourth weight, the terrain data, the obstacle data, the environment data, and the aerial vehicle attribute data are analyzed for passability to generate a plurality of candidate navigation paths.

[0069] Based on the obtained first weight, second weight, third weight, and fourth weight, the terrain data, obstacle data, environment data, and aerial vehicle attribute data are comprehensively analyzed, and a plurality of candidate navigation paths are generated through a path generation algorithm.

[0070] In an optional embodiment, in step S1312, based on the first weight, the second weight, the third weight and the fourth weight, the terrain data, the obstacle data, the environment data and the aircraft attribute data are analyzed for passability to generate the plurality of candidate navigation paths, including the following steps:

[0071] In step S13121, based on the first weight, the second weight, the third weight and the fourth weight, the terrain data, the obstacle data, the environment data and the aircraft attribute data are analyzed for passability to filter a plurality of candidate waypoints from a plurality of initial waypoints contained in the to-be-planned geographical area range, wherein the to-be-planned geographical area range is determined based on the path planning start point position and the path planning end point position.

[0072] The to-be-planned geographical area range is determined based on the path planning start point position and the path planning end point position of the flying car. In an optional embodiment, determining the to-be-planned geographical area range includes: first, determining the path planning start point position coordinates and the path planning end point position coordinates of the flying car, then, according to the determined start point and end point position coordinates and the endurance of the flying car, coarsely predicting the flight area of the flying car, further, combining the specific requirements of the flight task and the obtained path planning data, refining the coarsely predicted flight area, and determining the area boundary to obtain the to-be-planned geographical area range.

[0073] In the to-be-planned geographical area range, a series of initial waypoints are pre-set through three-dimensional map data and other auxiliary information. The initial waypoints can be natural landmarks based on terrain features, such as mountain peaks and river turning points, or artificial landmarks based on city planning, such as high-rise buildings and open areas.

[0074] Based on the first weight, the second weight, the third weight and the fourth weight, the terrain data, the obstacle data, the environment data and the aircraft attribute data corresponding to each initial waypoint are analyzed for passability to determine the passability evaluation value of each initial waypoint. The passability evaluation value of the initial waypoint reflects the suitability of the initial waypoint as a flight waypoint under given conditions.

[0075] For example, for any initial waypoint, based on the terrain data and the first weight corresponding to the terrain data, a passability evaluation value of the terrain data is determined, based on the obstacle data and the second weight corresponding to the obstacle data, a passability evaluation value of the obstacle data is determined, based on the environment data and the third weight corresponding to the environment data, a passability evaluation value of the environment data is determined, and based on the aircraft attribute data and the fourth weight corresponding to the aircraft attribute data, a passability evaluation value of the aircraft attribute data is determined. According to the passability evaluation values corresponding to the above four types of data respectively, the passability evaluation value of the initial waypoint is determined. Among them, the passability evaluation value of the initial waypoint can be determined by summing the passability evaluation values corresponding to the above four types of data respectively, or other operation methods can be used, which are not limited here.

[0076] Further, based on the passability evaluation value of each initial waypoint, the pre-set multiple initial waypoints are screened, the initial waypoints with passability evaluation values less than the pre-set evaluation value threshold are removed, and the initial waypoints with passability evaluation values greater than or equal to the pre-set evaluation value threshold are retained, and these retained initial waypoints are used as candidate waypoints.

[0077] Through the above implementation steps, multiple candidate waypoints can be effectively screened based on the weight analysis of terrain, obstacle, environment and aircraft attribute data, and then a safe and efficient flight path is generated.

[0078] Step S13122, a plurality of candidate navigation paths are generated by using the plurality of candidate waypoints.

[0079] A path generation algorithm is used to generate a plurality of candidate navigation paths based on the plurality of candidate waypoints.

[0080] In an optional embodiment, a Rapidly-exploring Random Trees (RRT) algorithm is used to generate a plurality of candidate paths, specifically as follows:

[0081] First, an RRT tree is established, and the starting point of the flying car is used as the root node of the RRT tree, so that there is only one node in the tree at this time.

[0082] Secondly, the parameters of the random exploration tree algorithm are determined, including the maximum number of iterations, the tree expansion step (i.e. the distance limit between the random node and the nearest node in the tree each time the expansion is performed), the obstacle detection method, etc.

[0083] Then, a point is randomly sampled in the configuration space, which is any point in the candidate waypoints and represents a potential position of the flying car in space. The sampling method usually follows a uniform distribution or a Gaussian distribution to ensure the uniformity and efficiency of the entire space exploration.

[0084] Further, the nearest node to the randomly sampled point in the RRT tree is found. This step is achieved by calculating the distance between the sampling point and all nodes in the tree, and then selecting the node with the smallest distance as the nearest node. The distance calculation takes into account the dimension of the space, i.e. the position and state of the flying car. It should be noted that the nodes in the tree refer to a plurality of determined alternative waypoints.

[0085] Next, starting from the nearest node determined in the above step, new random sampling is continued to find the next node, and the path is continuously expanded in the manner described above until a path to the destination is generated. It should be noted that during the continuous expansion of the path, it is necessary to continuously determine whether the newly generated path segment intersects with the obstacle, and in the case of no intersection, it is determined that the newly generated path segment is feasible, and the path expansion can continue.

[0086] By running the RRT algorithm multiple times using different random sequences and parameters (such as expansion step), multiple alternative paths can be generated, each with its specific strategy for avoiding obstacles.

[0087] When the multiple alternative paths generated by the RRT algorithm are relatively tortuous, a local path simplification method is used to remove unnecessary turning points in the path, making the path smoother. Alternatively, a curve fitting method is used to fit the path, making it smoother and ensuring the maneuverability and flight safety of the flying car.

[0088] Based on the screened multiple alternative waypoints, a path planning algorithm is used to generate multiple alternative navigation paths, ensuring the diversity of the paths.

[0089] The weight distribution mechanism makes the path planning more flexible and targeted, and can adjust the weight distribution proportion of each data type according to the characteristics of different flight tasks and flight environments, improving the applicability and effectiveness of the path planning. The multiple alternative navigation paths generated are based on a deep understanding of the actual flight conditions and limitations of the flying car, and each path is formed by considering the terrain, obstacles, environment and aircraft properties, effectively improving the accuracy of the path planning and enhancing the flight safety of the flying car.

[0090] In an alternative embodiment, the aircraft path planning method further comprises the following steps:

[0091] Step S141, in response to data mutation of part of the data in the terrain data, obstacle data, environment data and aircraft attribute data, readjusting the weight distribution between the first weight, the second weight, the third weight and the fourth weight to obtain an adjustment result;

[0092] In the process of aircraft path planning, sudden changes in flight environment or real-time updates of aircraft status can have a significant impact on flight safety and path feasibility. When any of the terrain data, obstacle data, environmental data, and aircraft attribute data undergoes data mutation (i.e., the data changes significantly, exceeding the preset threshold or normal range), the data mutation needs to be responded to, and the weights corresponding to each type of data need to be re-evaluated to ensure that the path planning can adapt to the new flight conditions.

[0093] Data mutation refers to a significant change in flight-related data within a short period of time, for example, the weather suddenly changes from sunny to thunderstorm, temporary obstacles are added to the obstacle database, and the maximum flight height of the aircraft is reduced due to failure, etc.

[0094] Re-adjusting the weight distribution refers to changing the initial determined weight distribution results of each type of data. For example, when severe weather conditions occur, the weight of environmental data (the third weight) needs to be increased to ensure that the aircraft avoids dangerous weather areas. If the performance of the aircraft power system is detected to be reduced, the weight of the aircraft attribute data (the fourth weight) should be increased to ensure that the path planning can consider the actual ability limit of the aircraft.

[0095] In addition, the aircraft sensor data, weather forecast updates, obstacle database changes, etc. need to be continuously monitored, and once data mutation is detected, the weight adjustment process is triggered immediately.

[0096] By using the machine learning model or probability model mentioned in the above embodiments, the mutated data is analyzed to obtain the adjusted weight distribution results.

[0097] Optionally, according to the severity of the data mutation, multiple levels of weight adjustment are set, and slight changes only fine-tune the weights, while severe changes require significant adjustment, or even re-plan the path.

[0098] Step S142, based on the adjustment result, locally re-planning at least part of the multiple candidate navigation paths.

[0099] After weight adjustment, based on the adjustment result, at least part of the generated candidate navigation paths are locally re-planned to ensure the safety and efficiency of the flight path.

[0100] In an optional embodiment, based on the adjustment result, the candidate navigation paths that need to be re-planned are identified. This includes the part of the path that directly passes through the mutation area, and the path segment that is indirectly affected.

[0101] For the affected path segment, a local path planning algorithm such as A* is used to locally re-plan the candidate navigation path affected by the data mutation. The local path planning algorithm focuses on the local area and quickly finds the best path that avoids obstacles or adapts to environmental changes.

[0102] In local path generation, a local exploration window is usually set, and the size and position of the window depend on the range of influence of the data mutation. The local path planning algorithm finds the best path within this window.

[0103] The generated local new path is integrated with the remaining unaffected part of the original path to form an updated complete candidate path. At the same time, the integrated path is smoothed and optimized to ensure that the aircraft can fly stably and safely according to the new path.

[0104] In an optional embodiment, the smoothing process is implemented using curve fitting methods, including Bezier curves, spline interpolation, etc.

[0105] By dynamically adjusting the weight distribution in path planning, the flight conditions can be quickly adapted to ensure flight safety. The local path re-planning strategy effectively reduces the computational burden while ensuring the quality of the flight path, improving the ability of the aircraft to respond to unexpected situations.

[0106] In an optional embodiment, in step S132, the target optimization function is used to evaluate the path feasibility of the plurality of candidate navigation paths, and the target navigation path is selected from the plurality of candidate navigation paths, including the following steps:

[0107] Step S1321, using the target optimization function to evaluate the path feasibility of the plurality of candidate navigation paths, obtaining an evaluation result, wherein the evaluation result is used to comprehensively evaluate the plurality of candidate navigation paths according to a plurality of evaluation indexes, and the plurality of evaluation indexes include: path length index, aircraft energy consumption index, safety coefficient index;

[0108] In the final stage of the flying car path planning, the path feasibility of the plurality of candidate navigation paths needs to be evaluated based on the target optimization function, so as to determine the final flight route.

[0109] The target optimization function is used to quantify the path quality, combining path length, aircraft energy consumption, safety coefficient and other evaluation indexes, aiming to find the optimal path that meets multiple constraint conditions.

[0110] The plurality of evaluation indexes include: a path length index, an aircraft energy consumption index, and a safety factor index. The path length index is used to measure the flight distance, which directly affects the flight time of the flying car. The aircraft energy consumption index is used to measure the efficiency of the aircraft power system, which is related to factors such as flight height, speed, wind resistance, and heading, and affects the endurance of the aircraft. The safety factor index is used to evaluate the safety of the flight path, mainly considering factors such as obstacle avoidance, weather adaptability, and airspace regulation compliance.

[0111] A composite function including the above evaluation indexes is constructed as a target optimization function, and the expression of the target optimization function is as follows:

[0112] Cost = a·L + b·E + g·S

[0113] Wherein, Cost represents the value of the target optimization function, L represents the path length, E represents the aircraft energy consumption, S represents the safety factor, a represents the weight corresponding to the path length, b represents the weight corresponding to the aircraft energy consumption, g represents the weight corresponding to the safety factor, and a, b, g are preset values.

[0114] Using the above target optimization function, the target optimization function value corresponding to any candidate navigation path is calculated as the path feasibility evaluation result of the candidate navigation path.

[0115] In an optional embodiment, the path length corresponding to any candidate navigation path can be the shortest distance in the sky on a straight line, or the actual flight distance considering the terrain undulation and obstacle bypassing. The aircraft energy consumption corresponding to any candidate navigation path is calculated by the constructed energy consumption model. The safety factor corresponding to any candidate navigation path is determined based on the comprehensive evaluation of the relative distance between the aircraft and the obstacle, the matching degree of the flight height and the terrain, the maneuvering performance margin of the aircraft, and the adaptability of the flight environment.

[0116] By applying the target optimization function, comprehensive feasibility evaluation of multiple candidate navigation paths can be performed to ensure that the selected flight route achieves the best balance in key indicators such as length, energy consumption, and safety factor.

[0117] Step S1322, selecting a target navigation path from the plurality of candidate navigation paths based on the evaluation results.

[0118] The plurality of candidate navigation paths are sorted according to the evaluation results (i.e. the target optimization function values), and the path with the optimal evaluation result (i.e. the path with the smallest target optimization function value) is selected as the target navigation path.

[0119] Optionally, when there are multiple candidate navigation paths with the same evaluation result, a decision mechanism is started. For example, expert knowledge judgment or path selection based on flight priority is introduced.

[0120] After selecting the target navigation path, a final confirmation is made, including checking the path details (such as speed, height, turn radius, etc. whether it meets the performance of the flying car).

[0121] The selected target navigation path is the optimal path that best meets the current flight conditions and performance limitations of the flying car. It takes into account flight efficiency and energy consumption while ensuring flight safety, providing precise and safe flight guidance for the flying car, thereby significantly improving the flight safety of the flying car.

[0122] In an alternative embodiment, in step S12, determining the path planning data set associated with the path planning reference information includes the following steps: determining the path planning data set associated with the path planning reference information from the multi-dimensional data source, wherein the multi-dimensional data source includes at least part of the following data sources: public channel data source; aircraft self-collected data source; third-party channel data source; multi-party fusion data source; special scene data source.

[0123] From multi-level data sources, data closely related to flying car path planning is screened, fused, and formed into a comprehensive, accurate, and real-time path planning data set. The construction of the data set is the basis for safe and efficient flight of the flying car, ensuring that flight path planning can take into account multiple key environmental variables and flight conditions.

[0124] The public channel data source includes official published geographic information system data, urban planning data, weather forecasts, etc.

[0125] The aircraft self-collected data source refers to the data obtained by the flying car's own sensors and collection devices, such as real-time GPS positioning, radar detection data, and aircraft state information during flight. The sensor network equipped on the flying car continuously collects surrounding environment data, including but not limited to real-time distance from obstacles, flight speed and height, status of on-board equipment, etc., providing immediate feedback for path planning.

[0126] The third-party channel data source refers to data information from flight service providers, navigation companies or other third parties, including road traffic conditions, airspace monitoring data, and emergency alerts, etc.

[0127] The multi-party fusion data source involves data integration and processing, fusing, cross-verifying and real-time updating of data from multiple sources to improve the accuracy and reliability of the data.

[0128] The special scene data source refers to data for specific flight situations, such as flying in mountainous areas, forest fire sites, or densely populated urban areas, which require the collection and processing of specific terrain, obstacles, and even real-time smoke concentration or building height information.

[0129] By determining the path planning data set associated with the path planning reference information from the multi-dimensional data source, the flying car path planning can comprehensively consider the complexity and dynamic changes of the flight environment, and ensure the accuracy of the flight path planning.

[0130] In the embodiment, a flying vehicle path planning method is also provided, Figure 2 is a flowchart of another flying vehicle path planning method according to an embodiment of the present application, as Figure 2 shown, the flow includes the following steps:

[0131] Step S21, sending the path planning reference information of the flying vehicle to the cloud server, wherein the path planning reference information includes the path planning starting point position and the path planning end point position;

[0132] Step S22, receiving the target navigation path from the cloud server, wherein the target navigation path is generated according to the path planning data set, the path planning data set is determined based on the path planning reference information, the path planning data set includes the terrain data, obstacle data, environmental data and flying vehicle attribute data associated with the path planning reference information, the target navigation path is used to describe the flight plan of the flying vehicle when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning starting point position and the path planning end point position;

[0133] Step S23, displaying the target navigation path.

[0134] In the embodiment, the flying vehicle performs the above-mentioned flying vehicle path planning method, and the above-mentioned flying vehicle path planning method is consistent in essence with the flying vehicle path planning method shown in Figure 1 .

[0135] Taking a flying car as an example, the flying car user or the automatic driving system of the flying car sends the path planning reference information to the cloud server through the vehicle terminal or the mobile device, and the path planning reference information sent contains the path planning starting point position and the path planning end point position. Exemplarily, the path planning reference information is transmitted in an electronic data format, ensuring the safety and accuracy of information transmission.

[0136] The cloud server obtains the path planning data set based on the received path planning reference information, determines the target navigation path according to the path planning data set, and returns it to the flying car. For the specific process of determining the target navigation path, reference can be made to the explanation and description in the above-mentioned embodiment.

[0137] After receiving the target navigation path generated by the cloud server, the flying car displays it to the user or automatically displays it on the flight control interface, so as to facilitate the flying car to perform the flight task according to the target path.

[0138] In an alternative embodiment, detailed information of the target navigation path will be displayed on the control interface of the flying car, including the location of each target waypoint, the estimated time of arrival, the flight altitude and the recommended flight speed, etc. In addition, the control interface also indicates auxiliary information such as key terrain features, obstacle locations and weather conditions, etc. to enhance the driver's awareness of the flight environment.

[0139] The specific implementation process of this embodiment can refer to the above-mentioned embodiments, which will not be repeated here.

[0140] In the embodiments of the present application, a flying car path planning method is also provided, Figure 3 is a flowchart of a flying car path planning method according to an embodiment of the present application, as Figure 3 shown, the method comprises the following steps:

[0141] Step S31, sending the path planning starting point position and the path planning ending point position of the flying car to the cloud;

[0142] Step S32, receiving the target navigation path returned by the cloud, wherein the cloud determines the target navigation path of the flying car based on terrain data, obstacle data, environment data and aircraft attribute data;

[0143] Step S33, displaying the received target navigation path on the display device of the flying car.

[0144] In an alternative embodiment, the specific implementation process is as follows: set the path planning starting point position and the path planning ending point position of the flying car and upload to the cloud background. The cloud background obtains terrain, obstacle and environment (such as strong wind, precipitation and snowfall, etc.) information according to the real-time three-dimensional map. Synchronize the size and maneuverability of the flying car, and generate the target navigation path (including the specific planning of direction, altitude and speed at different nodes or in different stages of the path) based on the analysis of flyability. The path is the optimal solution that takes safety as the first target.

[0145] The data of the three-dimensional map is realized through multi-dimensional technical means and collection methods. The data sources of the three-dimensional map include:

[0146] Public data sources include city basic geographic information and traffic and planning data. The street block database, terrain elevation data and building three-dimensional models provided by city-related institutions constitute the basic geographic information framework. Traffic and planning data refers to the highway network, airspace planning and obstacle distribution information obtained in cooperation with the traffic department to ensure that the path planning meets the requirements of airspace control.

[0147] Professional surveying and remote sensing technology, including three-dimensional laser scanning and photogrammetry, and synthetic aperture radar (SAR) technology. For example, high-precision topographic data is obtained through airborne / vehicle-mounted three-dimensional laser scanning technology, and three-dimensional raster maps are generated in combination with aerial photogrammetry. SAR technology is used to achieve large-scale terrain monitoring and enhance three-dimensional modeling capabilities in complex environments.

[0148] Dynamic acquisition of navigation enterprises, navigation enterprises collect three-dimensional real scene data of roads, buildings and airspace environment through street view cars, drones and other equipment, and construct dynamically updated three-dimensional models. Alternatively, vehicle GPS trajectory, aircraft sensor data and real-time traffic flow information are integrated to supplement the updating needs of dynamic obstacles (such as temporary buildings, air traffic).

[0149] Crowdsourcing and multi-source fusion, real-time scene data uploaded by users (such as sudden obstacles, weather changes) is collected using active or passive crowdsourcing mode to improve the timeliness of the map. At the same time, satellite images, laser radar point clouds and camera images are combined to generate high-resolution three-dimensional stereo maps through algorithm fusion, supporting multi-dimensional safety constraints for path planning.

[0150] Specific scene data expansion, for the needs of flying cars, three-dimensional data in the low-altitude field (such as high-voltage lines, no-fly zones for drones) is expanded, and the map accuracy is verified in real time through sensors. In the emergency rescue scene, temporary surveying and mapping equipment is introduced to quickly construct three-dimensional maps of disaster areas to support emergency path planning.

[0151] Through the above multi-source data acquisition and fusion technology, three-dimensional stereo maps can cover static terrain, dynamic obstacles and airspace control information, providing high-precision, real-time path planning basis for flying cars.

[0152] Optionally, when determining the target navigation path, various factors need to be considered, including:

[0153] Terrain elevation and slope, the terrain grid is divided through the three-dimensional raster map, and the linear influence coefficient of flight height and slope on energy consumption is calculated (such as an increase of 3% in energy consumption for every 10 meters increase in height).

[0154] Obstacle density and distribution, based on point cloud data, the three-dimensional bounding box of obstacles is labeled, and the priority of obstacle avoidance is quantified in combination with the dynamic threat field model (such as the closer to the obstacle, the higher the path weight penalty value).

[0155] Flying car size constraints, according to the geometric parameters of the flying car (such as wingspan, height), the minimum clearance threshold of the path node is set to avoid collision with buildings or terrain.

[0156] The maneuverability parameters of the flying car are introduced, including the minimum turning radius and the maximum climbing rate, and the dynamic feasibility constraint equation is generated by polynomial fitting.

[0157] The weather and airspace control are converted into the path feasibility coefficient (e.g., low-altitude flight is prohibited under strong wind conditions) by using real-time weather data (wind speed, visibility) and airspace control rules.

[0158] The path weight is dynamically adjusted based on real-time sensor data, and the local path is re-planned in response to sudden obstacles.

[0159] Optionally, when determining the target navigation path, each node needs to be determined by a three-dimensional grid map with multiple conditions. It is determined whether the preselected flight node meets the preset judgment condition. The preset judgment condition includes static condition and dynamic condition. The static condition is whether the node altitude meets the flight safety distance (e.g., higher than the ground obstacle by 5 meters); the dynamic condition is whether the node is occupied by real-time obstacles (e.g., other aircraft).

[0160] Optionally, the smoothness of the target navigation path is evaluated by using a gravity-repulsion field model. The strength of the repulsion field is inversely proportional to the distance of the obstacle, and the strength of the gravity field is related to the target direction.

[0161] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to select authorization or refusal.

[0162] According to another aspect of the embodiments of the present application, a flying vehicle path planning device is also provided, Figure 4 is a structural block diagram of a flying vehicle path planning device 400 according to an embodiment of the present application, as Figure 4 shown, the device comprises: an acquisition module 401, configured to acquire path planning reference information of a flying vehicle, wherein the path planning reference information comprises: a path planning starting point position and a path planning ending point position; a determination module 402, configured to determine a path planning data set associated with the path planning reference information, wherein the path planning data set comprises: terrain data, obstacle data, environmental data and flying vehicle attribute data associated with the path planning reference information; a generation module 403, configured to generate a target navigation path of the flying vehicle according to the path planning data set, wherein the target navigation path is used to describe a flight plan of the flying vehicle when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning starting point position and the path planning ending point position.

[0163] According to another aspect of embodiments of the present application, there is also provided an aircraft path planning device, Figure 5 is a structural block diagram of another aircraft path planning device 500 according to embodiments of the present application, as shown, the device comprises: a sending module 501, configured to send path planning reference information of an aircraft to a cloud server, wherein the path planning reference information comprises: a path planning starting point position and a path planning ending point position; a receiving module 502, configured to receive a target navigation path from the cloud server, wherein the target navigation path is generated according to a path planning data set, the path planning data set is determined based on the path planning reference information, the path planning data set comprises: terrain data, obstacle data, environment data and aircraft attribute data associated with the path planning reference information, the target navigation path is used to describe a flight plan adopted by the aircraft when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning starting point position and the path planning ending point position; a display module 503, configured to display the target navigation path. Figure 5

[0164] According to another aspect of embodiments of the present application, there is also provided an aircraft, comprising: a memory, which stores an executable program; and a processor, configured to run the executable program, wherein the executable program performs the aircraft path planning method in various embodiments of the present application when running.

[0165] Optionally, in the present embodiment, the processor in the aircraft can be configured to run the executable program to perform the following steps:

[0166] Step S11, obtaining path planning reference information of an aircraft, wherein the path planning reference information comprises: a path planning starting point position and a path planning ending point position;

[0167] Step S12, determining a path planning data set associated with the path planning reference information, wherein the path planning data set comprises: terrain data, obstacle data, environment data and aircraft attribute data associated with the path planning reference information;

[0168] Step S13, generating a target navigation path of the aircraft according to the path planning data set, wherein the target navigation path is used to describe a flight plan adopted by the aircraft when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning starting point position and the path planning ending point position.

[0169] According to another aspect of embodiments of the present application, there is also provided a computer readable storage medium, which comprises a stored executable program, wherein the executable program controls a device where the readable storage medium is located to perform the aircraft path planning method in various embodiments of the present application when running.

[0170] ​Optionally, in the embodiment, the executable program can be configured to store an executable program for performing the following steps:

[0171] In step S11, path planning reference information of the aircraft is acquired, wherein the path planning reference information comprises a path planning start point position and a path planning end point position.

[0172] In step S12, a path planning data set associated with the path planning reference information is determined, wherein the path planning data set comprises terrain data, obstacle data, environment data and aircraft attribute data associated with the path planning reference information.

[0173] In step S13, a target navigation path of the aircraft is generated according to the path planning data set, wherein the target navigation path is used to describe a flight plan of the aircraft when passing through a plurality of target waypoints, and the plurality of target waypoints are determined based on the path planning start point position and the path planning end point position.

[0174] The embodiment of the present application further provides a computer program product comprising a computer program, which, when executed by a processor, implements the aircraft path planning method in the various embodiments of the present application.

[0175] The embodiment of the present application further provides a computer program product comprising a non-volatile computer readable storage medium, which is used to store a computer program, and the computer program, when executed by a processor, implements the aircraft path planning method in the various embodiments of the present application.

[0176] The embodiment of the present application further provides a computer program, which, when executed by a processor, implements the aircraft path planning method in the various embodiments of the present application.

[0177] In the above-described embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0178] In the several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. Of course, the device embodiment described above is only schematic. For example, the division of the units can be a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, unit or module, and can be electrical or other forms.

[0179] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0180] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0181] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various program code storage media.

[0182] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method for aircraft path planning, characterized in that, include: Obtain the path planning reference information of the aircraft, wherein the path planning reference information includes: the starting position of the path planning and the ending position of the path planning; Determine the path planning dataset associated with the path planning reference information, wherein the path planning dataset includes: terrain data, obstacle data, environmental data, and aircraft attribute data associated with the path planning reference information; The target navigation path of the aircraft is generated based on the path planning dataset. The target navigation path describes the flight plan adopted by the aircraft when passing through multiple target waypoints, which are determined based on the starting position and ending position of the path planning.

2. The aircraft path planning method according to claim 1, characterized in that, Generating the target navigation path for the aircraft based on the path planning dataset includes: Based on the path planning dataset, multiple alternative navigation paths are generated for the aircraft; The feasibility of the multiple alternative navigation paths is evaluated using a target optimization function, and the target navigation path is selected from the multiple alternative navigation paths.

3. The aircraft path planning method according to claim 2, characterized in that, Based on the path planning dataset, the multiple alternative navigation paths generated for the aircraft include: Based on the path planning dataset, a first weight corresponding to the terrain data, a second weight corresponding to the obstacle data, a third weight corresponding to the environmental data, and a fourth weight corresponding to the aircraft attribute data are obtained. The first weight, the second weight, the third weight, and the fourth weight are used to distinguish the importance of the terrain data, the obstacle data, the environmental data, and the aircraft attribute data in the path planning process. Based on the first weight, the second weight, the third weight, and the fourth weight, a passability analysis is performed on the terrain data, the obstacle data, the environmental data, and the aircraft attribute data to generate the multiple alternative navigation paths.

4. The aircraft path planning method according to claim 3, characterized in that, Based on the first weight, the second weight, the third weight, and the fourth weight, a drivability analysis is performed on the terrain data, the obstacle data, the environmental data, and the aircraft attribute data to generate the multiple alternative navigation paths, including: Based on the first weight, the second weight, the third weight, and the fourth weight, a passability analysis is performed on the terrain data, the obstacle data, the environmental data, and the aircraft attribute data. Multiple candidate waypoints are selected from multiple initial waypoints included in the geographical area to be planned. The geographical area to be planned is determined based on the starting position and the ending position of the path planning. The multiple alternative waypoints are used to generate the multiple alternative navigation paths.

5. The aircraft path planning method according to claim 4, characterized in that, The aircraft path planning method also includes: In response to a data mutation in some categories of the terrain data, obstacle data, environmental data, and aircraft attribute data, the weight allocation among the first weight, the second weight, the third weight, and the fourth weight is readjusted to obtain the adjustment result; Based on the adjustment results, at least some of the alternative navigation paths among the multiple alternative navigation paths are partially replanned.

6. The aircraft path planning method according to claim 2, characterized in that, The feasibility of the multiple candidate navigation paths is evaluated using the objective optimization function, and the selection of the target navigation path from the multiple candidate navigation paths includes: The feasibility of the multiple alternative navigation paths is evaluated using the objective optimization function to obtain evaluation results. The evaluation results are used to comprehensively evaluate the multiple alternative navigation paths based on multiple evaluation indicators, including: path length indicator, aircraft energy consumption indicator, and safety factor indicator. The target navigation path is selected from the multiple alternative navigation paths based on the evaluation results.

7. The aircraft path planning method according to claim 1, characterized in that, The path planning dataset associated with the path planning reference information includes: The path planning dataset associated with the path planning reference information is determined from a multi-dimensional data source, wherein the multi-dimensional data source includes at least some of the following data sources: Public channel data source; The aircraft itself collects data from its own data sources; Third-party channel data source; Multiple data sources are integrated; Data source for special scenarios.

8. A method for aircraft path planning, characterized in that, include: The aircraft's path planning reference information is sent to the cloud server, wherein the path planning reference information includes: the starting position of the path planning and the ending position of the path planning; The system receives a target navigation path from the cloud server. The target navigation path is generated based on a path planning dataset, which is determined based on path planning reference information. The path planning dataset includes terrain data, obstacle data, environmental data, and aircraft attribute data associated with the path planning reference information. The target navigation path describes the flight plan adopted by the aircraft when passing through multiple target waypoints. The multiple target waypoints are determined based on the starting position and ending position of the path planning. Display the target navigation path.

9. An aircraft, characterized in that, include: Memory, which stores executable programs; A processor for running the executable program, wherein the executable program, when running, performs the aircraft path planning method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the computer-readable storage medium is located to perform the aircraft path planning method according to any one of claims 1 to 8.

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