Unmanned aerial vehicle path planning method

Abstract

The invention discloses an unmanned aerial vehicle path planning method, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: carrying out the first iteration, determining a next node coordinate set in an unmanned aerial vehicle region according to a current position coordinate and a flight end point coordinate, and enabling the current position coordinate to be a flight starting point coordinate; determining a gradient direction according to the current position coordinate and the flight end point coordinate, determining a gradient direction approximation evaluation function according to the gradient direction, and determining a comprehensive cost evaluation function according to the gradient direction approximation evaluation function; obtaining a next target node coordinate according to the comprehensive cost evaluation function; when the coordinate of the next target node is not the flight end point coordinate, taking the coordinate of the next target node as the current position coordinate to carry out second iteration, repeating the iteration until the coordinate of the next target node is the flight end point coordinate, and ending the iteration; and forming a target node coordinate set by all the next target node coordinates to determine an optimal planning path. According to the invention, the real-time performance and applicability of the unmanned aerial vehicle path planning method can be improved.

Description

Technical Field

[0001] The present application relates to the field of drone technology, and is related to, but not limited to, a drone path planning method. Background Art

[0002] With the rapid development of drone technology, thanks to its advantages such as high flexibility, efficiency, real-time performance, and high-resolution data acquisition, drones are being applied in a variety of fields, including logistics and transportation, geological surveying and mapping, emergency rescue, and military security. In the field of drone path planning, especially point-to-point path planning, existing technologies include the A* algorithm (A-Star Algorithm), the Probabilistic Roadmap Method (PRM), the artificial potential field algorithm, and biomimetic algorithms. Experimental verification shows that the A* algorithm offers the best real-time performance, with a convergence speed of three to four seconds. However, because the movement cost and heuristic cost used in point-to-point path planning are calculated using distance, the real-time performance of the A* algorithm remains to be improved. This is particularly true in scenarios such as dynamic battlefield threat identification and high-real-time path planning, where the application of the A* algorithm to point-to-point path planning has many drawbacks.

[0003] Based on this, there is an urgent need for a more comprehensive and efficient UAV path planning method to make up for the shortcomings of the existing path planning methods and ultimately achieve improvements in the comprehensiveness and real-time performance of the UAV path planning methods. Summary of the Invention

[0004] An embodiment of the present application provides a method for planning a path for a drone.

[0005] The technical solution of the embodiment of the present application is implemented as follows: In a first aspect, an embodiment of the present application provides a method for drone path planning, the method comprising: in a first iteration, determining a next node coordinate set in a drone area based on current position coordinates and flight endpoint coordinates, the current position coordinates being the flight start point coordinates; determining a gradient direction based on the current position coordinates and the flight endpoint coordinates, determining a gradient direction approximation evaluation function based on the gradient direction, and determining a comprehensive cost evaluation function based on the gradient direction approximation evaluation function and a distance cost evaluation function; obtaining the next node coordinate with the minimum comprehensive cost in the next node coordinate set according to the comprehensive cost evaluation function as the next target node coordinate; when the next target node coordinate is not the flight endpoint coordinate, performing a second iteration with the next target node coordinate as the current position coordinate, repeating the iteration until the next target node coordinate is the flight endpoint coordinate, and the iteration terminates; forming a target node coordinate set from the next target node coordinates obtained in each iteration, and determining the optimal planned path based on the target node coordinate set.

[0006] The technical solution provided by the present application determines the next node coordinate set in the UAV area according to the current position coordinates and the flight endpoint coordinates in the first iteration, where the current position coordinates are the flight start point coordinates; determines the gradient direction according to the current position coordinates and the flight endpoint coordinates, determines the vector direction according to the current position coordinates and the next node coordinates, and determines the gradient direction approximation evaluation function according to the gradient direction and the vector direction. Since the gradient direction approximation evaluation function value corresponding to a next node is calculated by the gradient direction approximation evaluation function, the dot product calculation takes very little time, thereby greatly improving the real-time performance of determining the planned path by the gradient direction approximation evaluation function compared to the A* algorithm. The comprehensive cost evaluation function is determined according to the gradient direction approximation evaluation function and the distance cost evaluation function, thereby improving the perfection of the path planning process. In addition, By adding environmental factors and weighting coefficients to the comprehensive cost evaluation function, it is ensured that a variety of dynamic changing factors are taken into account in the path planning process, so that the comprehensive cost evaluation function can adapt to path planning in a variety of different scenarios, and improves the adaptability of path planning in dynamic scenarios through the comprehensive cost evaluation function; according to the comprehensive cost evaluation function, the next node coordinate with the minimum comprehensive cost is obtained in the next node coordinate set as the next target node coordinate; when the next target node coordinate is not the flight endpoint coordinate, the next target node coordinate is used as the current position coordinate for the second iteration, and the iteration is repeated until the next target node coordinate is the flight endpoint coordinate, and the iteration is terminated; finally, the next target node coordinate obtained in each iteration constitutes the target node coordinate set, and the optimal planning path is determined according to the target node coordinate set. The technical solution provided in this application calculates the comprehensive cost of all possible next node coordinates through a comprehensive cost evaluation function, evaluates and determines the next target node coordinates, and continuously iterates to find the next target node coordinates to form a target node coordinate set. Finally, the path planning from the flight starting point to the flight end point is completed based on the target node coordinate set. It solves the problems of long calculation time and insufficient consideration of influencing factors in traditional path planning methods, and significantly improves the timeliness and universality of drone path planning methods.

[0007] Optionally, determining the gradient direction approximation evaluation function according to the gradient direction includes: determining a vector direction according to the current position coordinates and the next node coordinates; and determining the gradient direction approximation evaluation function according to the vector direction and the gradient direction.

[0008] Optionally, determining the gradient direction based on the current position coordinates and the flight endpoint coordinates includes: determining the vector direction between the current position and the flight endpoint based on the current position coordinates and the flight endpoint coordinates, and determining the gradient direction based on the vector direction between the current position and the flight endpoint.

[0009] Optionally, determining the gradient direction approximation evaluation function according to the vector direction and the gradient direction includes: calculating the dot product of the vector direction and the gradient direction to obtain the gradient direction approximation evaluation function.

[0010] Optionally, determining a comprehensive cost evaluation function according to the gradient direction approximation evaluation function and the distance cost evaluation function includes: determining the comprehensive cost evaluation function according to the gradient direction approximation evaluation function, the distance cost evaluation function, environmental factors, and a weighting coefficient, wherein a calculation formula of the comprehensive cost evaluation function is expressed by the following formula; ; Where, represents a comprehensive cost evaluation function from the current position to the next node; represents the distance cost evaluation function; represents the weighting coefficient of the distance cost evaluation function, or ; Represents the gradient direction approximation evaluation function; Represents the weighting coefficient of the gradient direction approximation evaluation function; represents the environmental factors; Represents the weighting coefficient of the environmental factor.

[0011] Optionally, the weighted coefficient of the gradient direction approximation evaluation function is dynamically adjusted according to the difficulty of reaching the next node and the experience value. The greater the difficulty of the next node, the greater the weighted coefficient of the gradient direction approximation evaluation function. When the next node is a node that has been successfully reached, the weighted coefficient of the gradient direction approximation evaluation function is , Indicates the successful experience value of reaching the next node.

[0012] Optionally, determining the next node coordinate set in the drone area based on the current position coordinates and the flight endpoint coordinates includes: removing obstacle points in the drone area; and determining the next node coordinate set in the drone area after removing the obstacle points based on the current position coordinates and the flight endpoint coordinates.

[0013] In a second aspect, an embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the steps in the above-mentioned drone path planning method are implemented.

[0014] In a third aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned drone path planning method are implemented.

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present application include at least: The present application provides a method for planning a path for a UAV. In the first iteration, a set of coordinates of the next node is determined in the UAV area according to the current position coordinates and the flight destination coordinates, where the current position coordinates are the flight start point coordinates; a gradient direction is determined according to the current position coordinates and the flight destination coordinates, a vector direction is determined according to the current position coordinates and the next node coordinates, and a gradient direction approximation evaluation function is determined according to the gradient direction and the vector direction. Since the gradient direction approximation evaluation function is used to calculate the gradient direction approximation evaluation function value corresponding to the next node, the dot product calculation takes very little time, thereby greatly improving the real-time performance of determining the planned path through the gradient direction approximation evaluation function compared to the A* algorithm. The comprehensive cost evaluation function is determined according to the gradient direction approximation evaluation function and the distance cost evaluation function, thereby improving the perfection of the path planning process and Moreover, by adding environmental factors and weighting coefficients to the comprehensive cost evaluation function, it is ensured that a variety of dynamic changing factors are taken into account in the path planning process, so that the comprehensive cost evaluation function can adapt to path planning in a variety of different scenarios, thereby improving the adaptability of path planning in dynamic scenarios through the comprehensive cost evaluation function; according to the comprehensive cost evaluation function, the next node coordinate with the minimum comprehensive cost is obtained in the next node coordinate set as the next target node coordinate; when the next target node coordinate is not the flight endpoint coordinate, the next target node coordinate is used as the current position coordinate for the second iteration, and the iteration is repeated until the next target node coordinate is the flight endpoint coordinate, and the iteration is terminated; finally, the next target node coordinate obtained in each iteration constitutes a target node coordinate set, and the optimal planning path is determined according to the target node coordinate set. The technical solution provided in this application calculates the comprehensive cost of all possible next node coordinates through a comprehensive cost evaluation function, evaluates and determines the next target node coordinates, and continuously iterates to find the next target node coordinates to form a target node coordinate set. Finally, the path planning from the flight starting point to the flight end point is completed based on the target node coordinate set. It solves the problems of long calculation time and insufficient consideration of influencing factors in traditional path planning methods, and significantly improves the timeliness and universality of drone path planning methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which: Figure 1 A flowchart of a drone path planning method provided in an embodiment of the present application; Figure 2 A schematic diagram of a drone area provided in an embodiment of the present application; Figure 3 A hardware entity diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. The following examples are used to illustrate the present application, but are not intended to limit the scope of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0018] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0019] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present application are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0020] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as generally understood by those skilled in the art in the art to which the embodiments of the present application belong. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0021] The embodiments of the present application will be further described below with reference to the accompanying drawings.

[0022] In view of the problems currently existing in the research of drone path planning methods in the field of drone technology, an embodiment of the present application provides a drone path planning method.

[0023] The technical solution of the present application is introduced below, and first the method embodiment of the present application is introduced.

[0024] Please refer to Figure 1 , which shows a flow chart of a drone path planning method provided by an embodiment of the present application, such as Figure 1 As shown, the method at least includes the following steps S110 to S150.

[0025] Step S110, in the first iteration, the next node coordinate set is determined in the UAV area according to the current position coordinates and the flight end coordinates, wherein the current position coordinates are the flight start coordinates.

[0026] In the embodiment of the present application, the flight starting point coordinates include the longitude, latitude and altitude of the UAV flight starting point, and the flight ending point coordinates include the longitude, latitude and altitude of the UAV flight ending point. Specifically, the longitude in the coordinate information is defined as , latitude is , the altitude is , define the flight starting point as , define the flight endpoint as , then, the starting point of the flight The coordinates are , flight end The coordinates are , define the current position as , then the current location The longitude in the coordinate information is , latitude is , the altitude is , current location The coordinates are ,In the first iteration, the coordinates of the flight starting point are the coordinates of the current position.

[0027] In the embodiment of the present application, in the first iteration, the obstacle point is first removed from the drone area. After the obstacle point is removed, the next node coordinate set is determined in the drone area after the obstacle point is removed based on the current position coordinates and the flight end point coordinates. All the next node coordinates in the next node coordinate set are the turning point coordinates of all possible movement paths starting from the current position. The next node set is defined as , any next node in the next node set is , then any next node The longitude in the coordinate information is , latitude is , the altitude is , then any next node The coordinates are For examples, please refer to Figure 2 , which shows a schematic diagram of a drone area provided by an embodiment of the present application, the drone area Including flight starting point , flight destination , obstacle points , current location , next node set All next nodes in the , next node , next node ), current location With the next node The connecting line between Construct possible path 1, current location With the next node The connecting line between Constitute possible path 2, similarly, the current position With the next node The connecting line between Constitute another possible path. All next nodes together constitute the next node set ,It is important to note that in the first iteration, the flight starting point This is the current location .

[0028] Step S120, determining the gradient direction according to the current position coordinates and the flight endpoint coordinates, determining a gradient direction approximation evaluation function according to the gradient direction, and determining a comprehensive cost evaluation function according to the gradient direction approximation evaluation function and the distance cost evaluation function.

[0029] In an embodiment of the present application, the distance cost evaluation function is determined as follows: the distance cost evaluation function is determined based on the current position coordinates, the flight endpoint coordinates, and the next node coordinates. The distance cost evaluation function is obtained based on the A* algorithm. Specifically, the A* algorithm is a classic heuristic search algorithm. The distance cost evaluation function includes a moving cost and an heuristic cost. The distance cost evaluation function is obtained by calculating the superposition of the moving cost and the heuristic cost. The moving cost is the actual moving cost from the current position to the next node. The moving cost can be calculated by the current position coordinates and the next node coordinates. The heuristic cost is the estimated remaining cost from the next node to the flight endpoint. The heuristic cost can be calculated by the next node coordinates and the flight endpoint coordinates. Optionally, in three-dimensional space, the moving cost and the heuristic cost can be obtained by the Euclidean distance. The calculation formula for the moving cost from the current position to the next node is expressed by the following formula (1): Formula (1); Where, Indicates current location To the next node The cost of movement; Indicates current location To the next node The Euclidean distance between the next node and the flight endpoint is calculated by the following formula (2): Formula (2); Where, Indicates the next node To the flight destination The inspiration cost; Indicates the next node To the flight destination The Euclidean distance between .

[0030] The movement cost is calculated based on the current position coordinates and the next node coordinates. After the heuristic cost is calculated based on the next node coordinates and the flight destination coordinates, the distance cost evaluation function is determined based on the movement cost and the heuristic cost. The distance cost evaluation function is obtained by calculating the superposition of the movement cost and the heuristic cost. The calculation formula of the distance cost evaluation function from the current position to the next node is expressed by the following formula (3): Formula (3); Where, Indicates from the current position To the next node Distance cost evaluation function; Indicates current location To the next node The cost of movement; Indicates the next node To the flight destination The inspiration cost.

[0031] In the embodiment of the present application, the gradient direction approximation evaluation function is determined as follows: first, the gradient direction is determined based on the current position coordinates and the flight end point coordinates. The gradient direction is determined based on the vector direction between the current position and the flight end point. Specifically, based on the current position coordinate and flight destination coordinate Determine current location and flight destination The vector direction between , the direction of the vector The calculation formula is expressed by the following formula (4): Formula (4); Where, Indicates current location and flight destination The vector direction between Indicates the end of the flight Longitude in coordinates, Indicates current location Longitude in coordinates; Indicates the end of the flight Latitude in coordinates, Indicates current location Latitude in coordinates; Indicates the end of the flight Altitude in coordinates, Indicates current location The altitude in coordinates.

[0032] Furthermore, the vector direction between the current position and the next node is determined based on the current position coordinates and the next node coordinates. coordinate and the next node coordinate Determine current location and the next node The vector direction between , the direction of the vector The calculation formula is expressed by the following formula (5): Formula (5); Where, Indicates current location and the next node The vector direction between Indicates the next node Longitude in coordinates, Indicates current location Longitude in coordinates; Indicates the next node Latitude in coordinates, Indicates current location Latitude in coordinates; Indicates the next node Altitude in coordinates, Indicates current location The altitude in coordinates.

[0033] Furthermore, the gradient direction approximation evaluation function is determined based on the vector direction between the current position and the next node and the gradient direction between the current position and the flight endpoint. Specifically, the gradient direction approximation evaluation function is obtained by calculating the dot product of the vector direction and the gradient direction. The calculation formula of the gradient direction approximation evaluation function from the current position to the next node is expressed by the following formula (6): Formula (6); Where, Indicates from the current position To the next node The gradient direction of approximates the evaluation function; Indicates the direction of the vector; Indicates the gradient direction.

[0034] In the embodiment of the present application, the gradient direction approximation evaluation function combines the gradient direction and the vector direction, and determines the gradient direction approximation evaluation function from the current position to the next node by calculating the dot product of the vector direction and the gradient direction. During the calculation process, the cost of calculating the gradient direction approximation evaluation function value corresponding to each next node coordinate in the next node coordinate set is to calculate a dot product as shown in formula (6), and the total amount of calculation includes 8 addition and subtraction operations and 3 multiplication operations; while for the distance cost evaluation function, the cost of calculating the distance cost evaluation function value corresponding to each next node coordinate in the next node coordinate set is to calculate the sum of the two distances as shown in formula (3), and the amount of calculation includes 11 addition and subtraction operations, 6 square operations, and 2 square root operations. Under normal computer processing conditions, it takes one clock cycle for the processor to perform one addition or subtraction operation, two to three clock cycles for one double-precision floating-point multiplication operation, and five to 20 clock cycles for one double-precision floating-point square root operation. Correspondingly, for the gradient direction approximation evaluation function, the calculation time for the computer processor to calculate the gradient direction approximation evaluation function value corresponding to each next node coordinate in the next node coordinate set is 17 clock cycles (about one second); for the distance cost evaluation function, the calculation time for calculating the distance cost evaluation function value corresponding to each next node coordinate in the next node coordinate set is 69 clock cycles (about three to four seconds). Obviously, the calculation time of calculating the gradient direction approximation evaluation function value corresponding to the coordinates of the next node is only 25% of the calculation time of calculating the distance cost evaluation function value corresponding to the coordinates of the next node. Compared with the distance cost evaluation function of the A* algorithm, the calculation time of calculating the gradient direction approximation evaluation function value using the gradient direction approximation evaluation function is greatly reduced. Then, the calculation time of determining the optimal planning path through the gradient direction approximation evaluation function is also reduced accordingly, thereby greatly improving the real-time performance of determining the optimal planning path through the gradient direction approximation evaluation function.

[0035] In the embodiment of the present application, a comprehensive cost evaluation function is determined by combining a gradient direction approximation evaluation function and a distance cost evaluation function. Specifically, the final comprehensive cost evaluation function is obtained based on the gradient direction approximation evaluation function, the weighted coefficient corresponding to the gradient direction approximation evaluation function, the distance cost evaluation function, the weighted coefficient corresponding to the distance cost evaluation function, and the environmental factors and the weighted coefficients corresponding to the environmental factors. The calculation formula of the comprehensive cost evaluation function is expressed by the following formula (7): Formula (7); Where, Indicates from the current position To the next node Comprehensive cost evaluation function; Indicates from the current position To the next node Distance cost evaluation function; Represents the weighting coefficient of the distance cost evaluation function, optional, It can be a consideration of whether the distance cost evaluation function should be applied, or When , it means that the distance cost evaluation function is applied, When , it means that the distance cost evaluation function is not applied; Indicates from the current position To the next node The gradient direction of approximates the evaluation function; Represents the weighted coefficient of the gradient direction approximation evaluation function. Optional. The weighted coefficient of the gradient direction approximation evaluation function is dynamically adjusted according to the difficulty of reaching the next node and the experience value. The greater the difficulty of the next node, the greater the weighted coefficient of the gradient direction approximation evaluation function corresponding to the next node. When the next node is a node that has been successfully reached, the weighted coefficient of the gradient direction approximation evaluation function corresponding to the next node is , Indicates the successful experience value of reaching the next node. When the gradient direction approximation evaluation function is applied in the comprehensive cost evaluation function, the weighted coefficient of the gradient direction approximation evaluation function is 1. When the gradient direction approximation evaluation function is not applied in the comprehensive cost evaluation function, the weighted coefficient of the gradient direction approximation evaluation function is 0. For example, if the next node The difficulty is higher than the next node , then set , if the next node is a next node that has been successfully reached, then , Indicates reaching the next node Success experience points, When , it means that the gradient direction approximation evaluation function is applied in the comprehensive cost evaluation function. When , it means that the gradient direction approximation evaluation function is not applied in the comprehensive cost evaluation function; Indicates environmental factors. Optionally, real-time sensor updates can be used to update environmental data including obstacle density, wind speed, and electromagnetic interference intensity to determine environmental factors. ; Represents the weighting coefficient of environmental factors. The embodiment of the present application determines the comprehensive cost evaluation function by comprehensively considering the gradient direction approximation evaluation function and the distance cost evaluation function, thereby improving the perfection of the process of determining the optimal planning path through the comprehensive cost evaluation function. By adding the weighting coefficients corresponding to the gradient direction approximation evaluation function and the distance cost evaluation function respectively to the comprehensive cost evaluation function, and adding environmental factors and the weighting coefficients corresponding to the environmental factors, it is ensured that the environmental factors, difficulty factors, experience factors, feasibility factors and other variable factors in the planning path process are taken into account, so that the comprehensive cost evaluation function can adapt to path planning in a variety of different scenarios, thereby improving the adaptability of path planning in dynamic scenarios through the comprehensive cost evaluation function.

[0036] Step S130 , obtaining the next node coordinates with the minimum comprehensive cost from the next node coordinate set according to the comprehensive cost evaluation function, as the next target node coordinates.

[0037] In an embodiment of the present application, for the next node coordinate set, the comprehensive cost corresponding to each next node coordinate in the next node coordinate set is calculated by a comprehensive cost evaluation function, and the minimum comprehensive cost is selected. The next node coordinate corresponding to the minimum comprehensive cost is the next target node coordinate.

[0038] Step S140, when the next target node coordinates are not the flight endpoint coordinates, the next target node coordinates are used as the current position coordinates for a second iteration, and the iteration is repeated until the next target node coordinates are the flight endpoint coordinates, and the iteration is terminated.

[0039] In an embodiment of the present application, the next node coordinate set is determined based on the current position coordinates, the flight endpoint coordinates and the drone area, and the next target node coordinates in the next node coordinate set are determined based on the comprehensive cost evaluation function. After the next target node coordinates are determined, it is judged whether the next target node coordinates are the flight endpoint coordinates. When the next target node coordinates are not the flight endpoint coordinates, the next target node coordinates are again iterated as the current position coordinates, and the next target node coordinates are continued to be determined. The iterative process is repeated until the next target node coordinates are the flight endpoint coordinates, and the iteration terminates.

[0040] Step S150 , forming a target node coordinate set from the next target node coordinates obtained in each iteration, and determining an optimal planning path according to the target node coordinate set.

[0041] In an embodiment of the present application, the target node coordinate set is determined by the next target node coordinate obtained in each iteration, and all the next target nodes in the target node set are connected in sequence to form a target node path; the flight starting point and the first next target node in the target node set are connected to obtain the path between the flight starting point and the target node set, and the path between the flight starting point and the target node set and the target node path are combined to obtain the optimal planned path.

[0042] In summary, the embodiment of the present application provides a method for UAV path planning. In the first iteration, the next node coordinate set is determined in the UAV area according to the current position coordinates and the flight endpoint coordinates, and the current position coordinates are the flight start point coordinates; the gradient direction is determined according to the current position coordinates and the flight endpoint coordinates, the vector direction is determined according to the current position coordinates and the next node coordinates, and the gradient direction approximation evaluation function is determined according to the gradient direction and the vector direction. Since the gradient direction approximation evaluation function value corresponding to a next node is calculated by the gradient direction approximation evaluation function, the dot product calculation takes very little time, so that compared with the A* algorithm, the real-time performance of determining the planned path by the gradient direction approximation evaluation function is greatly improved, and the comprehensive cost evaluation function is determined according to the gradient direction approximation evaluation function and the distance cost evaluation function, thereby improving the completeness of the path planning process. Moreover, by adding environmental factors and weighting coefficients into the comprehensive cost evaluation function, it is ensured that a variety of dynamic change factors are taken into account in the path planning process, so that the comprehensive cost evaluation function can adapt to path planning in a variety of different scenarios, and improves the adaptability of path planning in dynamic scenarios through the comprehensive cost evaluation function; according to the comprehensive cost evaluation function, the next node coordinate with the minimum comprehensive cost is obtained in the next node coordinate set as the next target node coordinate; when the next target node coordinate is not the flight endpoint coordinate, the next target node coordinate is used as the current position coordinate for the second iteration, and the iteration is repeated until the next target node coordinate is the flight endpoint coordinate, and the iteration is terminated; finally, the next target node coordinate obtained in each iteration constitutes a target node coordinate set, and the optimal planning path is determined according to the target node coordinate set. The technical solution provided in this application calculates the comprehensive cost of all possible next node coordinates through a comprehensive cost evaluation function, evaluates and determines the next target node coordinates, and continuously iterates to find the next target node coordinates to form a target node coordinate set. Finally, the path planning from the flight starting point to the flight end point is completed based on the target node coordinate set. It solves the problems of long calculation time and insufficient consideration of influencing factors in traditional path planning methods, and significantly improves the timeliness and universality of drone path planning methods.

[0043] It should be noted that in the embodiments of the present application, if the above-mentioned drone path planning method is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling an electronic device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0044] Correspondingly, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the steps of any of the drone path planning methods described in the above embodiments. Correspondingly, an embodiment of the present application also provides a computer program product. When the computer program product is executed by a processor of an electronic device, it is used to implement the steps of any of the drone path planning methods described in the above embodiments.

[0045] Based on the same technical concept, an embodiment of the present application provides an electronic device for implementing a drone path planning method described in the above method embodiment. Figure 3 A hardware entity diagram of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the electronic device 300 includes a memory 310 and a processor 320, and the memory 310 stores a computer program that can be run on the processor 320. When the processor 320 executes the program, the steps in any one of the drone path planning methods described in the embodiments of the present application are implemented.

[0046] The memory 310 is configured to store instructions and applications executable by the processor 320, and can also cache data to be processed or processed by the processor 320 and various modules in the electronic device (for example, image data, audio data, voice communication data, and video communication data), which can be implemented through flash memory (FLASH) or random access memory (RAM).

[0047] When the processor 320 executes the program, it implements the steps of any one of the above-mentioned methods for planning a drone path. The processor 320 generally controls the overall operation of the electronic device 300.

[0048] The processor may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that the electronic device that implements the functions of the processor may also be other electronic devices, and the embodiments of the present application are not specifically limited thereto.

[0049] The above-mentioned computer storage medium / memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface storage device, an optical disc, or a compact disc read-only memory (CD-ROM); it can also be various electronic devices that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0050] It should be noted that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0051] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0052] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0053] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0054] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0055] In addition, all functional units in the embodiments of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0056] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling the automatic test line of the device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0057] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0058] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0059] The above is merely an embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A UAV path planning method, characterized in that: The method comprises: In the first iteration, the next node coordinate set is determined in the drone area based on the current position coordinates and the flight end coordinates, where the current position coordinates are the flight start coordinates; determining a gradient direction according to the current position coordinates and the flight endpoint coordinates, determining a gradient direction approximation evaluation function according to the gradient direction, and determining a comprehensive cost evaluation function according to the gradient direction approximation evaluation function and a distance cost evaluation function; Obtaining the next node coordinates with the minimum comprehensive cost from the next node coordinate set according to the comprehensive cost evaluation function as the next target node coordinates; When the next target node coordinates are not the flight endpoint coordinates, performing a second iteration using the next target node coordinates as the current position coordinates, and repeating the iteration until the next target node coordinates are the flight endpoint coordinates, and the iteration is terminated; The next target node coordinates obtained in each iteration constitute a target node coordinate set, and the optimal planning path is determined based on the target node coordinate set.

2. The method according to claim 1, characterized in that Determining a gradient direction approximation evaluation function according to the gradient direction includes: Determine the vector direction according to the current position coordinates and the next node coordinates; The gradient direction approximation evaluation function is determined according to the vector direction and the gradient direction.

3. The method according to claim 1, characterized in that The step of determining the gradient direction according to the current position coordinates and the flight endpoint coordinates includes: The vector direction between the current position and the flight endpoint is determined according to the current position coordinates and the flight endpoint coordinates, and the gradient direction is determined according to the vector direction between the current position and the flight endpoint.

4. The method according to claim 2, characterized in that The determining the gradient direction approximation evaluation function according to the vector direction and the gradient direction includes: A dot product of the vector direction and the gradient direction is calculated to obtain the gradient direction approximation evaluation function.

5. The method according to claim 1, characterized in that The determining of the comprehensive cost evaluation function according to the gradient direction approximation evaluation function and the distance cost evaluation function comprises: The comprehensive cost evaluation function is determined according to the gradient direction approximation evaluation function, the distance cost evaluation function, environmental factors and weighting coefficients. The calculation formula of the comprehensive cost evaluation function is expressed by the following formula: ； Where, represents a comprehensive cost evaluation function from the current position to the next node; represents the distance cost evaluation function; represents the weighting coefficient of the distance cost evaluation function, or ; Represents the gradient direction approximation evaluation function; Represents the weighting coefficient of the gradient direction approximation evaluation function; represents the environmental factors; Represents the weighting coefficient of the environmental factor.

6. The method according to claim 5, characterized in that The weight coefficient of the gradient direction approximation evaluation function is dynamically adjusted according to the difficulty of reaching the next node and the experience value. The greater the difficulty of the next node, the greater the weight coefficient of the gradient direction approximation evaluation function. When the next node is a node that has been successfully reached, the weighted coefficient of the gradient direction approximation evaluation function is , Indicates the successful experience value of reaching the next node.

7. The method according to claim 1, characterized in that The step of determining the next node coordinate set in the drone area according to the current position coordinates and the flight endpoint coordinates includes: removing obstruction points in the drone area; In the drone area after the obstacle point is removed, the next node coordinate set is determined according to the current position coordinates and the flight endpoint coordinates.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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