Unmanned aerial vehicle flight path planning method and device and terminal equipment

Abstract

The invention provides an unmanned aerial vehicle flight path planning method and device and terminal equipment, and is suitable for the technical field of unmanned aerial vehicles, and the method comprises the steps: generating initial flight path information according to current flight environment information, current task demand information, current flight state information and a flight path planning model; generating intermediate flight path information according to the initial flight path information and a flight path extension model; in response to the generation of the intermediate flight path information, acquiring expert flight path planning information; and generating target flight path information of the unmanned aerial vehicle to be subjected to path planning according to the intermediate flight path information, the expert flight path planning information and the flight path decision weight information. According to the method, the initial flight path information meeting the current flight environment and task requirements is automatically generated through the flight path planning model, the flight path branches are expanded through the flight path expansion model, the middle flight path information with higher adaptability is generated, and the response capability of path planning to environment changes is improved.

Description

Technical Field

[0001] This application belongs to the technical field of unmanned aerial vehicles, and particularly relates to a method, device, and terminal device for unmanned aerial vehicle flight path planning. Background Art

[0002] With the rapid development of unmanned aerial vehicle technology, the cluster formation technology has received extensive attention and application in many fields such as military, security, environmental monitoring, and disaster relief. In the military field, cluster formation combat, as a new combat mode, is gradually becoming an important part of modern military systems.

[0003] In the prior art, a cluster formation combat task allocation and resource scheduling system based on a traditional rule engine and a static decision model plans flight paths for unmanned aerial vehicles, that is, relies on rules designed manually and a preset task allocation model to achieve task allocation and execution control for cluster formations in a specific tactical environment. Usually, a set of static task allocation rules and scheduling algorithms are used to perform task allocation according to task requirements, priorities, and other external conditions. These rules are usually based on empirical rules such as the "optimal resource allocation law" and the "time priority law".

[0004] However, in a complex battlefield environment, relying on static rules and preset strategies for task allocation cannot quickly adapt to environmental changes, resulting in low task allocation efficiency and difficulty in coping with sudden tactical changes. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method, device, and terminal device for unmanned aerial vehicle flight path planning, aiming to solve the problems in unmanned aerial vehicle technology that the flight path planning efficiency is low and the flight path cannot be dynamically adjusted in real time according to the complex and changeable flight environment.

[0006] The first aspect of the embodiments of this application provides a method for unmanned aerial vehicle flight path planning, including:

[0007] Obtain the current flight environment information, current task requirement information, and current flight state information of the unmanned aerial vehicle to be path-planned;

[0008] Generate initial flight path information according to the current flight environment information, current task requirement information, current flight state information, and a preset flight path planning model;

[0009] Generate intermediate flight path information according to the initial flight path information and a preset flight path extension model;

[0010] In response to the generation of the intermediate flight path information, obtain expert flight path planning information;

[0011] Generate the target flight path information of the drone to be path - planned based on the intermediate flight path information, the expert flight path planning information, and the preset flight path decision weight information.

[0012] The second aspect of the embodiments of the present application provides a drone flight path planning device, including:

[0013] An information acquisition module, configured to acquire the current flight environment information, the current task requirement information, and the current flight state information of the drone to be path - planned;

[0014] An initial flight path information generation module, configured to generate the initial flight path information according to the current flight environment information, the current task requirement information, the current flight state information, and the preset flight path planning model;

[0015] An intermediate flight path generation module, configured to generate the intermediate flight path information according to the initial flight path information and the preset flight path extension model;

[0016] An expert flight path planning information acquisition module, configured to acquire the expert flight path planning information in response to the generation of the intermediate flight path information; and

[0017] A target flight path information generation module, configured to generate the target flight path information of the drone to be path - planned based on the intermediate flight path information, the expert flight path planning information, and the preset flight path decision weight information.

[0018] The third aspect of the embodiments of the present application provides a terminal device. The terminal device includes a memory and a processor. A computer program is stored on the memory and can run on the processor. When the processor executes the computer program, the steps of the drone flight path planning method described in the first aspect above are implemented.

[0019] The fourth aspect of the embodiments of the present application provides a computer - readable storage medium, including: storing a computer program, and when the computer program is executed by a processor, the steps of the drone flight path planning method described in the first aspect above are implemented.

[0020] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: Based on the current flight environment information, current task requirement information, and current flight state information of the drone to be path-planned, the present application continuously learns and optimizes strategies through a flight path planning model to generate initial flight path information that conforms to the actual situation. The initial flight path information is extended and optimized through a flight path extension model, effectively enhancing the flexibility and diversity of the drone flight path planning. Furthermore, the flight path output by the model is combined with the expert's experience and knowledge to generate target flight path information, which not only ensures the scientificity and rationality of the path planning but also incorporates professional judgments and decisions, improving the reliability and practicality of the target flight path information. Therefore, in a complex and changeable flight environment, an efficient, safe, and task-demand-compliant flight path can be generated for the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a schematic flowchart of the implementation of the drone flight path planning method provided in the first embodiment of the present application;

[0023] Figure 2 It is a schematic flowchart of the implementation of the drone flight path planning method provided in the second embodiment of the present application;

[0024] Figure 3 It is a schematic flowchart of the implementation of the drone flight path planning method provided in the third embodiment of the present application;

[0025] Figure 4 It is a schematic flowchart of the implementation of the drone flight path planning method provided in the fourth embodiment of the present application;

[0026] Figure 5 It is a schematic flowchart of the implementation of the drone flight path planning method provided in the fifth embodiment of the present application;

[0027] Figure 6 It is a schematic flowchart of the implementation of the drone flight path planning method provided in the sixth embodiment of the present application;

[0028] Figure 7 It is a schematic flowchart of the implementation of the drone flight path planning method provided in the seventh embodiment of the present application;

[0029] Figure 8 It is a schematic structural diagram of the drone flight path planning device provided in the embodiments of the present application;

[0030] Figure 9 It is a schematic diagram of the terminal device provided by the embodiments of the present application. Detailed implementation manners

[0031] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0032] In order to illustrate the technical solutions described in the present application, the following will be described through specific embodiments.

[0033] Figure 1 The implementation flowchart of the unmanned aerial vehicle flight path planning method provided by Embodiment 1 of the present application is shown and described in detail as follows:

[0034] Step S101, obtain the current flight environment information, current task requirement information, and current flight state information of the unmanned aerial vehicle to be path-planned.

[0035] In this embodiment, the UAV to be path-planned can be a single UAV or multiple UAVs. The current flight environment information may include meteorological condition information, such as air temperature, air pressure, wind speed, wind direction, etc., which will affect the energy consumption, speed and stability of the UAV flight; it may also include geographical information, such as terrain features like mountains, rivers, urban layouts, etc., which determine the feasible areas and potential obstacles for the UAV flight; it may further include enemy deployment information, such as weaponry, defense ranges, etc., which is related to the safety of the UAV flight. Among them, the meteorological condition information can be obtained through meteorological monitoring stations or satellite meteorological data, the geographical information can be obtained through a Geographic Information System (GIS), and the enemy deployment information can be obtained through reconnaissance equipment, such as using instruments like radar, infrared detectors, etc. The current mission requirement information may involve different mission types, such as reconnaissance, strike, transportation, etc., and different missions have different requirements for the flight path; the mission priority determines the order of mission execution when resources are limited or there are conflicts; the mission target location, which specifies the location that the UAV needs to reach, can be obtained through the mission issuing system or through the command center system. The current flight state information may include the position of the UAV, such as longitude, latitude, altitude, which reflects the coordinate position of the UAV in space; speed, including the magnitude and direction of the flight speed; attitude, such as pitch angle, yaw angle, roll angle, etc., which is used to reflect its flight attitude; equipment status, such as battery power, sensor working status, communication status, etc., which is related to whether the UAV can fly normally. It can be measured by sensors carried by the UAV itself, such as the Global Positioning System, and the flight speed and flight attitude can also be measured by the inertial measurement unit carried by the UAV itself. The battery power and equipment working status can also be monitored by various sensors and the monitored information can be fed back to the computer system in real time.

[0036] Step S102: Generate initial flight path information according to the current flight environment information, current mission requirement information, current flight state information, and a preset flight path planning model.

[0037] In this embodiment, the preset flight path planning model can be a deep reinforcement learning model, which is by default a model that has been trained. The current flight environment information, current mission requirement information, and current flight state information can be used as the input information of the flight path planning model. After calculation by the flight path planning model, the output result is used as the initial flight path information.

[0038] Step S103: Generate intermediate flight path information according to the initial flight path information and a preset flight path extension model.

[0039] In this embodiment, the preset flight path expansion model can be a Monte Carlo tree model. The initial flight path information can be used as the input information of the flight path expansion model. After calculation by the flight path expansion model, intermediate flight path information is output.

[0040] Step S104: In response to the generation of the intermediate flight path information, obtain the expert flight path planning information.

[0041] In this embodiment, after the intermediate flight path information is generated, the system will automatically trigger an instruction to obtain the expert flight path planning information. The flight path planning information can be made by human military experts after judging the current battle situation, including information such as avoiding enemy defenses and selecting the best attack route, and is used as the expert flight path planning information.

[0042] Step S105: Generate the target flight path information of the drone to be path-planned according to the intermediate flight path information, the expert flight path planning information, and the preset flight path decision weight information.

[0043] In this embodiment, the preset flight path decision weight information can be set manually. It refers to setting a weight information for the intermediate flight path information and another weight information for the expert flight path planning information. Based on the flight path decision weight information, the intermediate flight path information and the expert flight path planning information are weighted and summed, and the sum calculation result is used as the target flight path information of the drone to be path-planned.

[0044] The drone flight path planning method provided in the embodiment of the present application is based on the current flight environment information, the current task requirement information, and the current flight state information of the drone to be path-planned. Through the flight path planning model, it continuously learns and optimizes strategies to generate initial flight path information that fits the actual situation. The initial flight path information is expanded and optimized by the flight path expansion model, effectively enhancing the flexibility and diversity of the drone flight path planning. Furthermore, the flight path output by the model is combined with the expert's experience and knowledge to generate the target flight path information, which not only ensures the scientificity and rationality of the path planning but also incorporates professional judgments and decisions, improving the reliability and practicality of the target flight path information. Therefore, in a complex and changeable flight environment, an efficient, safe, and task-demand-compliant flight path can be generated for the drone.

[0045] Figure 2 The flowchart showing the implementation of the drone flight path planning method provided in the second embodiment of the present application is different from the first embodiment above in that step S102 specifically includes:

[0046] Step S201, perform normalization processing on the current flight environment information, current task requirement information, and current flight state information to obtain current flight environment normalized information, current task requirement normalized information, and current flight state normalized information.

[0047] In this embodiment, the normalization processing method can be to use the Z-score normalization method for processing. The result after normalizing the current flight environment information is used as the current flight environment normalized information, the result after normalizing the current task requirement information is used as the current task requirement normalized information, and the result after normalizing the current flight state information is used as the current flight state normalized information.

[0048] Step S202, perform fusion processing on the current flight environment normalized information, current task requirement normalized information, and current flight state normalized information to obtain current flight process feature information.

[0049] In this embodiment, it can be to splice the current flight environment normalized information, current task requirement normalized information, and current flight state normalized information in a certain order to achieve fusion processing; it can also be to preset three normalized weight information, and perform weighted summation on the current flight environment normalized information, current task requirement normalized information, and current flight state normalized information based on this normalized weight information to achieve fusion processing. After fusion processing, current flight process feature information is obtained.

[0050] Step S203, based on a preset flight path planning model, perform parsing processing on the current flight process feature information to generate initial flight path information.

[0051] In this embodiment, the preset flight path planning model can be a deep reinforcement learning model. The current flight process feature information can be used as the input information of the flight path planning model, and after calculation by the flight path planning model, the initial flight path information is output.

[0052] The UAV flight path planning method provided by the embodiment of the present application performs normalization processing on the current flight environment information, the current task requirement information, and the current flight state information, which is used to eliminate the dimensional differences between different data, avoid calculation errors caused by data overflow due to dimensional differences during the calculation process of the flight path planning model. By performing fusion processing on the normalized information, key information in multiple aspects of the UAV is integrated, comprehensively reflecting the actual situation of the UAV flight. Furthermore, the potential laws and complex relationships behind the information after fusion processing are fully explored through the flight path planning model, so as to adaptively learn and optimize the path planning strategy according to different environments and task requirements, generate a more practical and efficient flight path, and improve the success rate and safety of the UAV in performing tasks.

[0053] Figure 3 The flowchart showing the implementation of the UAV flight path planning method provided by the third embodiment of the present application is different from that of the second embodiment above in that:

[0054] The preset flight path planning model includes a preset flight path planning input layer, a preset flight path planning hidden layer, and a preset flight path planning output layer;

[0055] The step S203 specifically includes:

[0056] Step S301: Based on the preset flight path planning input layer, perform parsing processing on the current flight process feature information to obtain multiple current flight process feature parsing vectors.

[0057] In this embodiment, the preset flight path planning model can be a deep reinforcement learning model. The preset flight path planning input layer can be used to receive the current flight process feature information, and can split or map the current flight process feature information according to certain rules, so that each numerical feature in the current flight process feature information can be mapped to different neurons or nodes in the input layer for parsing and calculation processing. The result output after being processed by the flight path planning model is used as the current flight process feature parsing vector.

[0058] Step S302: Based on the preset flight path planning hidden layer, perform conversion processing on the multiple current flight process feature parsing vectors to obtain multiple current flight process feature conversion vectors.

[0059] In this embodiment, the preset flight path planning hidden layer can be used to perform deep feature extraction and transformation on the current flight process feature parsing vector. The preset flight path planning hidden layer can include multiple neurons, and can perform complex linear and non-linear operations on the input current flight process feature parsing vector through the weight matrix and bias vector of the neurons, mine the deep features and relationships in the data, and convert it into the current flight process feature transformation vector.

[0060] Step S303: Based on the preset flight path planning output layer, perform non-linear transformation processing on the multiple current flight process feature transformation vectors to generate initial flight path information.

[0061] In this embodiment, the preset flight path planning output layer is used to receive the current flight process feature transformation vector output by the flight path planning hidden layer, and through non-linear transformation, convert it into an output that conforms to the format of the initial flight path information. The generated initial flight path information directly corresponds to the relevant parameters of the initial flight path, such as flight direction, speed adjustment, waypoint nodes, etc.

[0062] The UAV flight path planning method provided by the embodiments of the present application parses and processes the current flight process feature information through the flight path planning input layer, facilitating the flight path planning model to quickly capture the relationships between feature information. The flight path planning hidden layer mines the relationships between feature information and converts low-level features into high-level, more feature-representative current flight process feature transformation vectors. Furthermore, the flight path planning output layer processes the complex non-linear relationships in the flight path planning, making the generated path more in line with the actual flight requirements and improving the accuracy and adaptability of path planning.

[0063] Figure 4 FIG. shows the implementation flowchart of the UAV flight path planning method provided by the fourth embodiment of the present application, which is different from the first embodiment above: The step S103 specifically includes:

[0064] Step S401: Extract nodes from the initial flight path information to obtain flight path node information and the movement state information between nodes.

[0065] In this embodiment, the representative positions in the flight path can be first determined as flight path nodes. The representative positions can be nodes where the flight direction changes, or nodes where specific tasks are executed. Then, the situation between two adjacent nodes is analyzed, and the state information of the UAV when moving from one node to another is recorded, such as whether the moving speed is fast or slow, and which direction the moving direction is, etc., so as to obtain the flight path node information and the movement state information between nodes.

[0066] Step S402: Obtain flight path node extension information and movement state extension information between nodes according to the flight path node information, movement state information between nodes, and a preset flight path extension model.

[0067] In this embodiment, the preset flight path extension model can be a Monte Carlo tree model. Based on the flight path node information for flight path extension, the flight path node information can be used as the starting node for flight path extension, and the extension direction and range can be determined according to the movement state information between nodes. It can be through a large number of random extensions within the search range by the flight path extension model.

[0068] For the newly generated nodes, analyze the relationship between the new nodes and the original nodes. According to the movement state between the original nodes, combined with information such as the position of the new nodes, calculate the state when moving between the new nodes, such as changes in speed, changes in direction, etc., and use the new nodes as the flight path node extension information and the state when moving between the new nodes as the movement state extension information between nodes.

[0069] Step S403: Obtain multiple extended path energy consumption values according to the flight path node extension information, movement state extension information between nodes, and a preset flight path energy consumption calculation function.

[0070] In this embodiment, the preset flight path energy consumption calculation function can be set artificially. It can be designed based on flight distance, flight speed, or flight altitude. Each path composed of flight path node extension information and movement state extension information between nodes represents a possible flight extension path. The flight path energy consumption calculation function comprehensively considers various factors to calculate the energy consumption of each extended path. These factors can include the length of the path, flight speed, flight altitude, and other relevant flight conditions. Substitute the relevant parameters of each extended path into the energy consumption calculation function to calculate multiple extended path energy consumption values.

[0071] Step S404: Generate intermediate flight path information according to the extended path energy consumption values, flight path node extension information, and movement state extension information between nodes.

[0072] In this embodiment, when evaluating and screening the extended information of flight path nodes, it is possible to preferentially select a flight path with a lower energy consumption value as a candidate path. A lower energy consumption indicates that more energy can be saved during the flight, improving flight efficiency. After screening out the candidate paths, the candidate paths are optimized by combining the extended information of flight path nodes and the extended information of the movement state between nodes. For example, unnecessary nodes existing in the flight path are deleted, or the movement state between nodes is adjusted to make the flight process of the UAV smoother and more efficient. After screening and optimization, the finally determined flight path is used as the intermediate flight path information.

[0073] The UAV flight path planning method provided by the embodiment of the present application extracts nodes from the initial flight path information to accurately analyze the path structure, extends the flight path nodes through the flight path extension model, effectively expands the path selection range, improves the flexibility of flight path planning, makes the generated flight path more suitable for complex and changeable actual flight scenarios, calculates the energy consumption values of multiple extended paths, and generates intermediate flight path information by comprehensively considering the energy consumption values of the extended paths, the extended information of flight path nodes, and the extended information of the movement state between nodes, ensuring that the generated path takes into account both flight efficiency and energy consumption, achieving the scientificity and rationality of path planning, better meeting the requirements of UAV flight missions, and improving the success rate and safety of UAV flight mission execution.

[0074] Figure 5 The flowchart showing the implementation of the UAV flight path planning method provided in the fifth embodiment of the present application is different from the fourth embodiment above in that: step S404 specifically includes:

[0075] Step S501, determine whether the energy consumption value of the extended path is greater than or equal to a preset path energy consumption value threshold; if so, proceed to step S502; if not, proceed to step S503.

[0076] In this embodiment, the preset path energy consumption value threshold can be set manually. When the energy consumption value of the extended path is greater than or equal to the preset path energy consumption value threshold, it indicates that the extended information of the flight path nodes corresponding to the energy consumption value of the extended path results in a high flight energy consumption, so it is not necessary to continue using it to generate the flight path, and it is necessary to regenerate the extended information of the flight path nodes; when the energy consumption value of the extended path is less than the preset path energy consumption value threshold, it indicates that the extended information of the flight path nodes corresponding to the energy consumption value of the extended path results in a low flight energy consumption, so it can be used to generate the flight path when the UAV performs actual tasks.

[0077] Step S502, use the extended information of the flight path nodes as the flight path node information, use the extended information of the movement state between nodes as the movement state information between nodes, and return to step S402.

[0078] In this embodiment, when the extended path energy consumption value is greater than or equal to the preset path energy consumption value threshold, it indicates that the flight path node extension information corresponding to the extended path energy consumption value results in high flight energy consumption. Therefore, it is not necessary to continue using it to generate the flight path, but instead, the flight path node extension information needs to be regenerated.

[0079] Step S503: Generate intermediate flight path information according to the flight path node extension information and the node - to - node movement state extension information.

[0080] In this embodiment, when the extended path energy consumption value is less than the preset path energy consumption value threshold, it indicates that the flight path node extension information corresponding to the extended path energy consumption value results in low flight energy consumption. Therefore, it can be used to generate the flight path when the UAV executes the actual task, that is, the flight path node extension information and the node - to - node movement state extension information are fused or spliced to generate intermediate flight path information.

[0081] The UAV flight path planning method provided by the embodiments of the present application realizes the dynamic optimization of the path by judging the size relationship between the extended path energy consumption value and the preset path energy consumption value threshold. When the extended path energy consumption value is greater than or equal to the preset threshold, the flight path node extension information and the node - to - node movement state extension information are updated to the original information, and path extension is performed again. This can continuously explore a better flight path, avoid adopting a high - energy - consumption path scheme, effectively reduce the energy consumption of UAV flight, and improve energy utilization efficiency. If the extended path energy consumption value is less than the preset threshold, intermediate flight path information is generated based on the current flight path node extension information and the node - to - node movement state extension information, ensuring that the generated path not only meets the energy consumption requirements but also can adapt to the complex UAV flight environment and task requirements.

[0082] Figure 6 The flowchart of the implementation of the UAV flight path planning method provided by Embodiment VI of the present application is shown. The difference from Embodiment I above is that step S105 specifically includes:

[0083] Step S601: Extract feature values from the intermediate flight path information to obtain intermediate flight path feature information.

[0084] In this embodiment, when extracting feature values from the intermediate flight path information, it can be to extract path total length information, flight time information, flight altitude change range information, number of turns information, etc. in the intermediate flight path information as the intermediate flight path feature information.

[0085] Step S602: Extract feature values from the expert flight path planning information to obtain expert flight path feature information.

[0086] In this embodiment, for the expert flight path planning information, feature values can be extracted, such as the total path length information, flight time information, flight altitude change range information, number of turns information, etc. in the expert flight path planning information, as the expert flight path feature information.

[0087] Step S603: According to the preset flight path decision weight information, perform weighted summation on the intermediate flight path feature information and the expert flight path feature information to generate the target flight path information of the drone to be path-planned.

[0088] In this embodiment, the preset flight path decision weight information can be set manually, and different weight information can be set for the intermediate flight path feature information and the expert flight path feature information respectively. Based on the flight path decision weight information, perform weighted summation on the intermediate flight path feature information and the expert flight path feature information, and use the result of the weighted summation as the target flight path information of the drone to be path-planned.

[0089] The drone flight path planning method provided by the embodiments of the present application can accurately quantify complex path information into key features by extracting feature values from the intermediate flight path information and the expert flight path planning information. Through weighted summation calculation of the feature values, the organic integration of the intermediate flight path information and the expert flight path planning information is realized, taking into account both the rationality of the intermediate flight path generated based on the calculation model and the professional judgment and experience of experts, making the generated target flight path information more scientific and reasonable, better adapting to different flight environments and mission requirements, and improving the safety and efficiency of drone flight.

[0090] Figure 7 The implementation flowchart of the drone flight path planning method provided by the seventh embodiment of the present application is shown. The difference from the first embodiment above is that after the step S105, it further includes:

[0091] Step S701: Obtain the flight path completion quantity information, flight path execution time information, flight energy consumption information, and cooperative flight execution information of the drone to be path-planned.

[0092] In this embodiment, the flight path completion quantity information may refer to the number of flight paths actually completed by the UAV to be path-planned within a certain time or mission cycle, which can be obtained through the flight record system of the UAV. The flight record system is usually used to record the start and end states of each flight mission. By counting the number of completed flight missions, the flight path completion quantity information can be obtained. The flight path execution time information may refer to the time taken by the UAV to execute each flight path, which can be obtained through the clock system on the UAV. Record the start time at the beginning of the flight mission and the end time at the end of the mission. The difference between the two is the execution time of this flight path. For multiple flight missions, the execution time of each path can be recorded separately, and then summarized to form the flight path execution time information. The flight energy consumption information may refer to the amount of energy consumed by the UAV during flight. For a UAV using a battery, it is the reduction in battery power, which can be obtained through an energy monitoring device, such as a battery power monitor. The cooperative flight execution information may be used to represent the situation where each UAV executes tasks according to the predetermined cooperative rules in a cooperative flight mission of multiple UAVs, which can be obtained through the communication system between UAVs and the ground monitoring system. The communication system can transmit information such as the position and status of each UAV in real time. The ground monitoring system can judge whether the UAVs are flying according to the cooperative rules based on this information, such as whether they maintain a specified distance and whether they arrive at the specified position simultaneously.

[0093] Step S702: Obtain the flight path completion degree information according to the flight path completion quantity information and the quantity information of the target flight path information.

[0094] In this embodiment, the flight path completion degree information can be used to reflect the proportion of the number of flight paths actually completed by the UAV to the number of target flight paths. The calculation method can be to divide the flight path completion quantity by the number of target flight path information, and then multiply by 100% to obtain the completion degree in percentage form. For example, if the number of target flight paths is 10 and 8 are actually completed, then the flight path completion degree information is (8÷10)×100% = 80%.

[0095] Step S703: Obtain the flight efficiency information according to the flight path completion quantity information and the flight path execution time information.

[0096] In this embodiment, the flight efficiency information is used to reflect the ability of the UAV to complete the flight path within a unit time. It can be calculated by first summing up the execution times of all flight paths, and then dividing the number of completed flight paths by the sum of the execution times of all flight paths. For example, if the UAV has completed 5 flight paths with execution times of 10 minutes, 12 minutes, 11 minutes, 9 minutes, and 13 minutes respectively, and the total execution time is 55 minutes, then the flight efficiency information is 5÷55≈0.09 paths / minute.

[0097] Step S704: Obtain flight energy utilization rate information based on the flight energy consumption information and the preset available flight energy information.

[0098] In this embodiment, the preset available flight energy information can be set manually. The flight energy utilization rate information can be used to represent the proportion of the actual energy consumed by the UAV to the available energy. The calculation method can be to divide the flight energy consumption information by the preset available flight energy information and then multiply by 100% to obtain the utilization rate in percentage form. For example, if the preset available flight energy is 100 units and the actual consumption is 30 units, then the flight energy utilization rate information is (30÷100)×100% = 30%.

[0099] Step S705: Obtain flight cooperation degree information based on the cooperative flight execution information and the quantity information of the target flight path information.

[0100] In this embodiment, the flight cooperation degree information can be used to reflect the degree to which multiple UAVs execute according to the predetermined rules in the cooperative flight mission. It can be quantified according to the compliance with the cooperation rules in the cooperative flight execution information and then calculated by dividing by the quantity of the target flight path information. For example, in a cooperative flight mission with 10 target flight paths, the cooperative execution of 8 paths complies with the rules, then the flight cooperation degree information is (8÷10)×100% = 80%.

[0101] Step S706: Generate flight path planning quality characterization information based on the flight path completion degree information, flight efficiency information, flight energy utilization rate information, and flight cooperation degree information.

[0102] In this embodiment, different weights can be assigned to the flight path completion degree information, flight efficiency information, flight energy utilization rate information, and flight cooperation degree information respectively, and then weighted summation calculation is performed to obtain the flight path planning quality characterization information.

[0103] The drone flight path planning method provided by the embodiments of the present application can comprehensively and meticulously collect key data related to drone flight, quantify the effect of drone flight path planning from different dimensions, make the evaluation more scientific and accurate, and overall reflect the quality of the flight path planning by generating flight path planning quality characterization information, providing a comprehensive basis for optimizing the path planning, thereby continuously improving the performance and efficiency of drone flight.

[0104] Corresponding to the method in the above embodiment, Figure 8 The structural block diagram of the drone flight path planning device provided by the embodiments of the present application is shown. For ease of description, only the parts related to the embodiments of the present application are shown. Figure 8 The exemplary drone flight path planning device may be the execution subject of the drone flight path planning method provided in the foregoing Embodiment 1.

[0105] Referring to Figure 8 , the drone flight path planning device includes:

[0106] An information acquisition module 810, configured to acquire the current flight environment information, current task requirement information, and current flight status information of the drone to be path-planned;

[0107] An initial flight path information generation module 820, configured to generate initial flight path information according to the current flight environment information, current task requirement information, current flight status information, and a preset flight path planning model;

[0108] An intermediate flight path generation module 830, configured to generate intermediate flight path information according to the initial flight path information and a preset flight path extension model;

[0109] An expert flight path planning information acquisition module 840, configured to acquire expert flight path planning information in response to the generation of the intermediate flight path information; and

[0110] A target flight path information generation module 850, configured to generate target flight path information of the drone to be path-planned according to the intermediate flight path information, expert flight path planning information, and preset flight path decision weight information.

[0111] For the process of each module in the drone flight path planning device provided by the embodiments of the present application to implement its respective functions, reference may be specifically made to the description of Embodiment 1 shown above, which will not be elaborated here. Figure 1 It should be understood that the magnitudes of the sequence numbers of the above steps do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0112] ​

[0113] It should be understood that, as used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their combinations.

[0114] It should also be understood that the term "and / or" as used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0115] As used in the specification of the present application and the appended claims, the term "if" can be construed, depending on the context, as "when" or "once" or "in response to determining" or "in response to detecting". Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be construed, depending on the context, as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]".

[0116] In addition, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and should not be construed as indicating or implying relative importance. It should also be understood that although the terms "first", "second", etc. are used in the text in some embodiments of the present application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first table may be named the second table, and similarly, the second table may be named the first table, without departing from the scope of the various described embodiments. The first table and the second table are both tables, but they are not the same table.

[0117] Reference to "one embodiment" or "some embodiments" or the like described in the specification of the present application means that a particular feature, structure or characteristic described in connection with that embodiment is included in one or more embodiments of the present application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0118] The drone flight path planning method provided by the embodiments of this application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc. The embodiments of this application do not impose any restrictions on the specific types of terminal devices.

[0119] For example, the terminal device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication capabilities, a computing device, or other processing devices connected to a wireless modem, a vehicle-mounted device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a television set-top box (STB), a customer premise equipment (CPE), and / or other devices for communicating on a wireless system, as well as next-generation communication systems. For example, a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0120] By way of example and not limitation, when the terminal device is a wearable device, the wearable device can also be a general term for devices that apply wearable technology to the intelligent design of daily wear and develop wearable devices, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is either directly worn on the body or integrated into the user's clothes or accessories. A wearable device is not just a hardware device, but more importantly, it realizes powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable intelligent devices include those with complete functions and large sizes that can achieve complete or partial functions without relying on a smartphone, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to cooperate with other devices such as a smartphone, such as various smart bracelets and smart jewelry for monitoring physical signs.

[0121] Figure 9It is a schematic structural diagram of a terminal device provided by an embodiment of the present application. As Figure 9 shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Figure 9 only one is shown in the figure), a memory 91, and a computer program 92 that can run on the processor 90 is stored in the memory 91. When the processor 90 executes the computer program 92, the steps in the above-mentioned embodiments of various UAV flight path planning methods are implemented, such as Figure 1 the steps S101 to S105 shown in the figure. Alternatively, when the processor 90 executes the computer program 92, the functions of each module / unit in the above-mentioned device embodiments are implemented, such as Figure 8 the functions of the modules 810 to 850 shown in the figure.

[0122] The terminal device 9 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art can understand that Figure 9 this is only an example of the terminal device 9 and does not constitute a limitation on the terminal device 9. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the terminal device may further include an input and sending device, a network access device, a bus, etc.

[0123] The so-called processor 90 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0124] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as the hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., equipped on the terminal device 9. Further, the memory 91 may also include both the internal storage unit and the external storage device of the terminal device 9. The memory 91 is used to store an operating system, application programs, a BootLoader, data, and other programs, such as the program code of the computer program. The memory 91 may also be used to temporarily store data that has been sent or will be sent.

[0125] In addition, in each embodiment of the present application, each functional unit may be integrated in a processing unit, may exist separately physically for each unit, or two or more units may be integrated in one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0126] The embodiment of the present application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, the terminal device implements the steps in any of the above method embodiments.

[0127] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in any of the above method embodiments can be implemented.

[0128] The embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device is enabled to execute the steps in any of the above method embodiments.

[0129] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present application, it can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0130] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for unmanned aerial vehicle flight path planning, characterized in that, Including: Obtain the current flight environment information, current task requirement information, and current flight status information of the UAV to be path-planned; Generate initial flight path information according to the current flight environment information, current task requirement information, current flight status information, and a preset flight path planning model; Generate intermediate flight path information according to the initial flight path information and a preset flight path extension model; In response to the generation of the intermediate flight path information, obtain expert flight path planning information; Generate the target flight path information of the UAV to be path-planned according to the intermediate flight path information, expert flight path planning information, and preset flight path decision weight information.

2. The method for planning the flight path of a drone according to claim 1, wherein The step of generating initial flight path information according to the current flight environment information, current task requirement information, current flight status information, and a preset flight path planning model specifically includes: Perform normalization processing on the current flight environment information, current task requirement information, and current flight status information to obtain normalized current flight environment information, normalized current task requirement information, and normalized current flight status information; Perform fusion processing on the normalized current flight environment information, normalized current task requirement information, and normalized current flight status information to obtain current flight process feature information; Based on a preset flight path planning model, perform parsing processing on the current flight process feature information to generate initial flight path information.

3. The UAV flight path planning method according to claim 2, wherein The preset flight path planning model includes a preset flight path planning input layer, a preset flight path planning hidden layer, and a preset flight path planning output layer; The step of performing parsing processing on the current flight process feature information based on a preset flight path planning model to generate initial flight path information specifically includes: Based on the preset flight path planning input layer, perform parsing processing on the current flight process feature information to obtain multiple current flight process feature parsing vectors; Based on the preset flight path planning hidden layer, perform transformation processing on the multiple current flight process feature parsing vectors to obtain multiple current flight process feature transformation vectors; Based on the preset flight path planning output layer, perform non-linear transformation processing on the multiple current flight process feature transformation vectors to generate initial flight path information.

4. The method for planning the flight path of a drone according to claim 1, wherein, The step of generating intermediate flight path information according to the initial flight path information and a preset flight path extension model specifically includes: Extract nodes from the initial flight path information to obtain flight path node information and inter-node movement status information; According to the flight path node information, inter-node movement status information, and a preset flight path extension model, obtain flight path node extension information and inter-node movement status extension information; According to the flight path node extension information, inter-node movement status extension information, and a preset flight path energy consumption calculation function, obtain multiple extended path energy consumption values; Generate intermediate flight path information based on the extended path energy consumption value, flight path node extension information, and inter-node movement state extension information.

5. The method for planning the flight path of a drone according to claim 4, wherein, The step of generating intermediate flight path information based on the extended path energy consumption value, flight path node extension information, and inter-node movement state extension information specifically includes: Judge whether the extended path energy consumption value is greater than or equal to a preset path energy consumption value threshold; If so, use the flight path node extension information as the flight path node information, use the inter-node movement state extension information as the inter-node movement state information, and return to the step of obtaining the flight path node extension information and the inter-node movement state extension information according to the flight path node information, the inter-node movement state information, and a preset flight path extension model; If not, generate intermediate flight path information based on the flight path node extension information and the inter-node movement state extension information.

6. The drone flight path planning method according to claim 1, wherein, The step of generating the target flight path information of the drone to be path-planned based on the intermediate flight path information, the expert flight path planning information, and the preset flight path decision weight information specifically includes: Extract characteristic numerical values from the intermediate flight path information to obtain intermediate flight path characteristic information; Extract characteristic numerical values from the expert flight path planning information to obtain expert flight path characteristic information; According to the preset flight path decision weight information, perform weighted summation on the intermediate flight path characteristic information and the expert flight path characteristic information to generate the target flight path information of the drone to be path-planned.

7. The method for planning the flight path of a drone according to claim 1, wherein, After the step of generating the target flight path information of the drone to be path-planned based on the intermediate flight path information, the expert flight path planning information, and the preset flight path decision weight information, it further includes: Obtain the flight path completion quantity information, flight path execution time information, flight energy consumption information, and cooperative flight execution information of the drone to be path-planned; Obtain the flight path completion degree information according to the flight path completion quantity information and the quantity information of the target flight path information; Obtain the flight efficiency information according to the flight path completion quantity information and the flight path execution time information; Obtain the flight energy utilization rate information according to the flight energy consumption information and the preset available flight energy quantity information; Obtain the flight cooperation degree information according to the cooperative flight execution information and the quantity information of the target flight path information; Generate flight path planning quality characterization information according to the flight path completion degree information, flight efficiency information, flight energy utilization rate information, and flight cooperation degree information.

8. An unmanned aerial vehicle flight path planning device, characterized in that, It includes: An information acquisition module for acquiring the current flight environment information, current task requirement information, and current flight state information of the drone to be path-planned; An initial flight path information generation module for generating initial flight path information according to the current flight environment information, current task requirement information, current flight state information, and a preset flight path planning model. An intermediate flight path generation module, configured to generate intermediate flight path information according to the initial flight path information and a preset flight path extension model; An expert flight path planning information acquisition module, configured to acquire expert flight path planning information in response to the generation of the intermediate flight path information; And A target flight path information generation module, configured to generate target flight path information of the drone to be path-planned according to the intermediate flight path information, the expert flight path planning information, and preset flight path decision weight information.

9. A terminal device, characterized in that, The terminal device includes a memory and a processor. A computer program capable of running on the processor is stored on the memory. When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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