Unmanned aerial vehicle route planning method, device, equipment, product and storage medium

Abstract

The invention discloses an unmanned aerial vehicle route planning method and device, equipment, a product and a storage medium. Comprising the following steps: determining a first grid bitmap of at least one unmanned aerial vehicle route planning area; the first grid bitmap comprises identification information of grids; the identification information represents whether the grid is released after a preset time when the grid is occupied; acquiring planning request information of the first unmanned aerial vehicle under the condition that the first unmanned aerial vehicle needs to adjust the route; determining a second grid bitmap corresponding to the at least one unmanned aerial vehicle route planning area according to the identification information and the planning request information; based on the first grid bitmap and the second grid bitmap, whether the first unmanned aerial vehicle passes the course adjustment is judged, and a judgment result is obtained; whether the first unmanned aerial vehicle travels according to the adjusted route is determined according to the judgment result, the efficiency and flexibility of intelligent flight planning and grid distribution are achieved, and meanwhile the probability that the same grid is seized by multiple aircrafts is reduced, and the situation that the unmanned aerial vehicles lose communication exists and the route conflicts among the unmanned aerial vehicles are reduced.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, electronic device, product, and storage medium for unmanned aerial vehicle (UAV) route planning. Background Technology

[0002] In current technologies, the image and data transmission links of industrial drones currently use point-to-point command and control (C2) communication, which has short communication distances and is easily interfered with. Drone operation modes currently mostly employ a single pilot, a single ground station, and a single aircraft, with few multi-drone collaborative operation solutions. There is currently no large-scale intelligent aerial traffic planning system; only drone monitoring systems exist, used for simple data collection and monitoring of current drones. Current solutions have the following problems: 1) Existing drone management platforms primarily focus on collecting drone data for monitoring, failing to provide advance global planning and unified management; 2) While existing drones can be equipped with 5G customer premises equipment (CPE) for cellular communication, drones may lose connection in areas without cellular network coverage; 3) Current drone flight path planning is primarily based on spatial location (latitude, longitude, and altitude), and is all within visual line of sight, unsuitable for the needs of large-scale drone aerial traffic planning, unable to avoid flight path conflicts between drones, and resulting in extremely high aerial traffic planning costs. Summary of the Invention

[0003] To address the existing technical problems, embodiments of this application provide a method, apparatus, equipment, product, and storage medium for unmanned aerial vehicle (UAV) route planning.

[0004] The technical solution of this application embodiment is implemented as follows:

[0005] This application provides a method for unmanned aerial vehicle (UAV) route planning, including:

[0006] A first grid bitmap corresponding to at least one UAV flight path planning area is determined; the first grid bitmap includes identification information of at least one grid; the identification information indicates whether the grid will be released after a preset time when it is occupied;

[0007] If the first drone needs to adjust its flight path, obtain the planning request information of the first drone; the first drone is any one of the at least one drones.

[0008] Determine the second grid bitmap corresponding to the at least one UAV route planning area based on the identification information and the planning request information;

[0009] Based on the first grid bitmap and the second grid bitmap, it is determined whether the first UAV needs to adjust its flight path and whether it has passed through, and the determination result is obtained;

[0010] Based on the judgment result, determine whether the first UAV travels according to the adjusted route.

[0011] In the above scheme, determining the first grid bitmap corresponding to at least one UAV flight path planning area includes:

[0012] Obtain spatial information corresponding to the at least one UAV flight path planning area;

[0013] The spatial information is subjected to grid segmentation to obtain the first grid information corresponding to the spatial information;

[0014] The identification information of the grid is determined based on the first grid information;

[0015] If the identification information indicates that the grid is occupied, after the preset time, the identification information of the grid is re-determined based on the first grid information;

[0016] If the redefined identification information of the grid indicates that the grid has been released, then the second grid information corresponding to the acquisition of the spatial information is obtained;

[0017] The first grid bitmap is generated based on the second grid information.

[0018] In the above scheme, the step of performing grid segmentation processing on the spatial information to obtain the first grid information corresponding to the spatial information includes:

[0019] Based on the spatial information, obtain the first volume parameter corresponding to the overall airspace occupied by the at least one UAV, the second volume parameter of the at least one UAV itself, the speed parameter of the at least one UAV flight, and the first quantity parameter corresponding to the at least one UAV;

[0020] The grid segmentation granularity parameter corresponding to the spatial information is determined based on at least one of the first volume parameter, the second volume parameter, the velocity parameter, and the first quantity parameter.

[0021] The spatial information is divided into grids according to the grid division granularity parameters to obtain the first grid information.

[0022] In the above scheme, determining the identification information of the grid based on the first grid information includes:

[0023] The location information of each grid is obtained based on the first grid information;

[0024] Determine the base duration and incremental duration of the preset timer corresponding to each grid;

[0025] The duration information corresponding to the preset timer is determined based on the base duration and the incremental duration;

[0026] The location information and the duration information are used as the identification information.

[0027] In the above scheme, determining the base duration of the preset timer corresponding to each grid includes:

[0028] Obtain a first quantity parameter corresponding to the at least one UAV and a second quantity parameter corresponding to the grid;

[0029] The probability parameters of the at least one UAV flying over the grid are determined based on the first quantity parameter and the second quantity parameter;

[0030] The baseline duration is determined based on the probability parameters.

[0031] In the above scheme, determining the incremental duration of the preset timer corresponding to each grid includes:

[0032] The system acquires the speed parameters of the first drone passing through the first grid, the distance parameters between the first grid and the second grid occupied by the closest second drone to the first grid, and the grid parameters occupied by drones other than the first drone within a preset area; the first grid is any grid in each grid; the preset area is determined based on the distance parameters.

[0033] The incremental duration is determined based on the velocity parameter, the distance parameter, and the grid parameter.

[0034] In the above scheme, the step of determining whether the first UAV needs to adjust its flight path based on the first grid bitmap and the second grid bitmap to obtain the determination result includes:

[0035] The first grid bitmap and the second grid bitmap are compared to obtain the comparison result;

[0036] If the comparison results show that the target grids corresponding to the flight path adjustment of the first UAV are all empty, it is determined that the judgment result is that the flight path adjustment of the first UAV has been passed.

[0037] If the comparison results show that the target grid corresponding to the flight path adjustment of the first UAV is not all empty, it is determined that the judgment result is that the flight path adjustment of the first UAV has not been passed.

[0038] Determining whether the first UAV follows the adjusted flight path based on the judgment result includes:

[0039] If the determination result is that the first UAV needs to adjust its route and the route is passed, it is determined that the first UAV will travel according to the adjusted route.

[0040] If the determination result is that the first UAV needs to adjust its route but the route is not passed, it is determined that the first UAV cannot travel according to the adjusted route.

[0041] In the above scheme, if the judgment result indicates that the first UAV needs to adjust its flight path and the route has not been cleared, the method further includes:

[0042] A response message is sent to the first UAV to request the planning information; the response message is used for the first UAV to travel along the corresponding route in the first grid bitmap; or...

[0043] If the determination result indicates that the first UAV needs to adjust its flight path to pass, the method further includes:

[0044] Update the first grid bitmap to the second grid bitmap;

[0045] The second grid bitmap is broadcast to the at least one UAV; the second grid bitmap is used by the first UAV to travel along the adjusted route.

[0046] The method in the above scheme further includes:

[0047] If the redefined identifier information of the grid indicates that the grid has been released, obtain the difference information between the third grid bitmap after the grid has been released and the first grid bitmap.

[0048] Based on the difference information, it is determined whether the flight path corresponding to the at least one UAV has been passed;

[0049] If the flight path corresponding to at least one UAV is passed, the third grid bitmap updates the first grid bitmap, and the third grid bitmap is broadcast to the at least one UAV; the third grid bitmap is used to determine the flight path corresponding to the at least one UAV.

[0050] This application also provides an unmanned aerial vehicle (UAV) route planning device, including:

[0051] The first determining unit is used to determine a first grid bitmap corresponding to at least one UAV flight path planning area; the first grid bitmap includes identification information of at least one grid; the identification information indicates whether the grid will be released after a preset time when it is occupied;

[0052] The acquisition unit is used to acquire the planning request information of the first drone when the first drone needs to adjust its flight path; the first drone is any one of the at least one drones.

[0053] The second determining unit is used to determine the second grid bitmap corresponding to the at least one UAV route planning area based on the identification information and the planning request information;

[0054] The judgment unit is used to determine, based on the first grid bitmap and the second grid bitmap, whether the first UAV needs to adjust its flight path and whether it has been passed, and to obtain a judgment result;

[0055] The third determining unit is used to determine whether the first UAV travels along the adjusted route based on the judgment result.

[0056] This application embodiment also provides an unmanned aerial vehicle (UAV) route planning system, the system including at least one UAV, a network device, and a cloud platform; each UAV covers the communication radiated by the network device;

[0057] The cloud platform is used to determine a first grid bitmap corresponding to at least one UAV flight path planning area; the first grid bitmap includes identification information of at least one grid; the identification information indicates whether the grid will be released after a preset time when it is occupied; when a first UAV needs to adjust its flight path, the platform obtains the planning request information of the first UAV; the first UAV is any UAV among the at least one UAV; the platform determines a second grid bitmap corresponding to the flight path planning area of ​​the at least one UAV based on the identification information and the planning request information; the platform determines whether the flight path adjustment required by the first UAV has been passed based on the first grid bitmap and the second grid bitmap, and obtains a judgment result; the platform determines whether the first UAV travels according to the adjusted flight path based on the judgment result.

[0058] This application also provides an unmanned aerial vehicle (UAV) route planning device, including:

[0059] Memory, used to store executable instructions;

[0060] A processor, when executing executable instructions stored in the memory, implements any step of the method described above.

[0061] This application also provides a computer program product, which, when executed by a processor, implements any step of the method described above.

[0062] This application also provides a computer-readable storage medium storing executable instructions for implementing any step of the method described above when executed by a processor.

[0063] The UAV route planning method, apparatus, electronic device, product, and storage medium provided in this application include: determining a first grid bitmap corresponding to at least one UAV route planning area; the first grid bitmap includes identification information of at least one grid; the identification information indicates whether the grid will be released after a preset time when it is occupied; obtaining planning request information of the first UAV when the first UAV needs to adjust its route; the first UAV is any UAV among the at least one UAV; determining a second grid bitmap corresponding to the at least one UAV route planning area based on the identification information and the planning request information; and determining whether the first UAV needs to adjust its route based on the first grid bitmap and the second grid bitmap. The solution in this application embodiment involves determining whether the flight path has been passed and obtaining a judgment result; based on the judgment result, determining whether the first UAV should travel according to the adjusted flight path. This is achieved by determining a first grid bitmap corresponding to at least one UAV flight path planning area, which includes identification information for at least one grid; the identification information indicates whether a grid will be released after a preset time when it is occupied; when the first UAV needs to adjust its flight path, obtaining the planning request information of the first UAV; determining a second grid bitmap corresponding to at least one UAV flight path planning area based on the identification information and the planning request information; judging whether the first UAV's adjusted flight path has been passed based on the first and second grid bitmaps, obtaining a judgment result; and determining whether the first UAV should travel according to the adjusted flight path based on the judgment result. This involves dividing at least one UAV flight path planning area into grids, introducing grid identification information to indicate whether a UAV will fly away after a preset time once a grid is occupied, and whether the occupied grid is released. Then, in cases where a UAV needs to adjust its flight path, the system intelligently plans the area based on the grid identification information and the UAV's flight path adjustment request information. This achieves a leap from pilot-controlled aircraft to intelligent flight planning, as well as improving the efficiency and flexibility of grid allocation. At the same time, it reduces the probability of multiple aircraft occupying the same grid, the possibility of UAVs losing contact, and flight path conflicts between UAVs. Attached Figure Description

[0064] Figure 1 This is a schematic diagram illustrating the process of a UAV route planning method according to an embodiment of this application;

[0065] Figure 2 This is the "cloud-network-device" architecture in the UAV route planning method in the embodiments of this application;

[0066] Figure 3 This is a schematic diagram showing that the grid in the embodiment of this application has been occupied;

[0067] Figure 4 This is a schematic diagram illustrating unified route planning on the cloud side in an embodiment of this application.

[0068] Figure 5 This is a flowchart illustrating aircraft A in an embodiment of this application;

[0069] Figure 6 This is a schematic diagram of the cloud-side process in an embodiment of this application;

[0070] Figure 7 This is a schematic diagram of a UAV route planning device according to an embodiment of this application;

[0071] Figure 8 This is a schematic diagram of the hardware structure of an unmanned aerial vehicle (UAV) route planning device in an embodiment of this application. Detailed Implementation

[0072] Unmanned Aerial Vehicles (UAVs) are unmanned aircraft controlled by radio remote control equipment and their own program control devices, or operated autonomously by an onboard computer, either completely or intermittently.

[0073] Currently, the image and data transmission links for drones in the industry use point-to-point C2 communication, which has a short communication distance and is susceptible to interference. Drone operations currently mostly employ a model of one pilot, one ground station, and one aircraft, with few multi-drone collaborative operations. There is currently no large-scale intelligent aerial transportation planning system; only drone monitoring systems exist, used for simple data collection and monitoring of current drone usage.

[0074] In current large-scale drone monitoring systems, the approach is mostly that each drone manufacturer builds its own platform to collect and upload information about its own drones. For route planning, each ground station plans the route for one aircraft.

[0075] Existing industrial drones face the following problems in large-scale applications:

[0076] 1. Existing drone management platforms mainly focus on monitoring drone data. Each drone transmits its data to its own platform via a ground station, and then transmits the location data to the drone management platform. The data entry point is controlled by the drone manufacturers themselves, and there is no way to plan and manage the data globally in advance.

[0077] 2. Some drones currently use 5G CPEs to achieve cellular communication, but in areas without cellular network coverage or at excessively high altitudes, the drones may lose contact.

[0078] 3. Current UAV route planning is mainly based on spatial location (latitude, longitude, and altitude), and all routes are within visual range. This is not suitable for the needs of air traffic planning for a large number of UAVs, and it cannot avoid route conflicts between UAVs. The planning cost of air traffic is extremely high.

[0079] Based on this, embodiments of this application provide a UAV route planning method applied to UAV route planning equipment. The functions implemented by this method can be achieved by a processor in an electronic device calling program code. Of course, the program code can be stored in a computer storage medium. Therefore, the electronic device includes at least a processor and a storage medium. As an example, the electronic device can be a mobile phone, computer, terminal, information transceiver, tablet device, personal digital assistant, etc.

[0080] Figure 1 This application provides a schematic diagram of a UAV route planning method according to an embodiment; as shown below. Figure 1 As shown, the method includes:

[0081] Step 101: Determine a first grid bitmap corresponding to at least one UAV flight path planning area; the first grid bitmap includes identification information of at least one grid; the identification information indicates whether the grid will be released after a preset time when it is occupied;

[0082] Step 102: If the first UAV needs to adjust its flight path, obtain the planning request information of the first UAV; the first UAV is any one of the at least one UAVs;

[0083] Step 103: Determine the second grid bitmap corresponding to the at least one UAV route planning area based on the identification information and the planning request information;

[0084] Step 104: Based on the first grid bitmap and the second grid bitmap, determine whether the first UAV needs to adjust its flight path and whether it has passed through, and obtain the determination result;

[0085] Step 105: Determine whether the first UAV is traveling according to the adjusted route based on the judgment result.

[0086] It should be noted that the UAV route planning method can be applied to a UAV route planning system, which can be determined according to actual conditions and is not limited here. As an example, the UAV route planning system can be a 5G-based BeiDou UAV intelligent air traffic system. In practical applications, the 5G-based BeiDou UAV intelligent air traffic system can also be referred to as a 5G+BeiDou UAV intelligent air traffic system. This 5G+BeiDou UAV intelligent air traffic system can adopt a "cloud-network-terminal" system architecture. The cloud is the UAV intelligent management platform, the network is the mobile 5G network + BeiDou network, and the terminal is the 5G+BeiDou airborne communication terminal. The 5G+BeiDou UAV airborne communication terminal adopts a dual-mode design of 5G and BeiDou short message. When 5G and 4G are available, priority is given to accessing the cellular network; where there is no cellular network, BeiDou short message is used. Using the cellular network and BeiDou short message, UAV information can be transmitted back to the UAV intelligent management platform without distance limitations, and UAVs can be controlled beyond line of sight. Meanwhile, the UAV's onboard communication terminal can access the "ONE POINT" platform to achieve China Mobile's 5G+BeiDou high-precision positioning. The 5G+BeiDou UAV intelligent aerial transportation system uses latitude, longitude, altitude, and time to determine aircraft waypoints and comprehensively plan flight routes. The flight routes are synchronized between the onboard communication terminal and the intelligent management platform. After the management platform issues the flight route, the UAV can dynamically adjust its route based on the flight situation and surrounding environment during flight, synchronizing with the management platform. Different UAVs can fly through the same spatial grid during flight, but the time values ​​must be staggered by more than one minute.

[0087] In step 101, the specific process for determining the first grid bitmap corresponding to at least one UAV flight path planning area can be determined according to the actual situation and is not limited here. As an example, determining the first grid bitmap corresponding to at least one UAV flight path planning area can be done by dividing at least one UAV flight path planning area into at least one grid with a fixed granularity, and then determining the first grid bitmap corresponding to at least one UAV flight path planning area based on at least one grid. Specifically, determining the first grid bitmap corresponding to at least one UAV flight path planning area can include obtaining spatial information corresponding to the at least one UAV flight path planning area; performing grid segmentation processing on the spatial information to obtain first grid information corresponding to the spatial information; determining the identification information of the grid based on the first grid information; if the identification information indicates that the grid is occupied, after the preset time, re-determining the identification information of the grid based on the first grid information; if the re-determined identification information of the grid indicates that the grid is released, obtaining the second grid information after release corresponding to the spatial information; and generating the first grid bitmap based on the second grid information. The UAV can be determined according to the actual situation and is not limited here. As an example, the UAV can be an aircraft. The first grid bitmap can be determined according to the actual situation, and is not limited here. As an example, the first grid bitmap can be referred to as a bitmap.

[0088] The first grid bitmap includes identification information for at least one grid; wherein, the identification information can be determined according to actual conditions and is not limited here. As an example, the identification information can be understood as assigning a flag bit and a timer to each grid to characterize whether the grid will be released after a preset time when it is occupied.

[0089] The identification information indicates whether the grid will be released after a preset time when it is occupied; the preset time can be determined according to the actual situation and is not limited here. As an example, the preset time can be determined by a timer. In practical applications, the timer can specifically be a timer, and the preset time can be set by the duration of the timer.

[0090] In step 102, the specific process of obtaining the planning request information of the first UAV when the flight path needs to be adjusted can be determined according to the actual situation and is not limited here. The planning request information can be determined according to the actual situation and is not limited here. As an example, the planning request information can be a request to replan the flight path.

[0091] As an example, the first drone can be an airplane, which can be denoted as Airplane A. The main reason for the first drone to adjust its flight path is that when the airplane detects unknown obstacles due to its own sensors or needs to return or change its target for special reasons, the airplane itself can replan its flight path. However, to ensure the uniqueness of the airplane passing through a certain local space at the same time and to ensure flight safety, it is necessary to ensure that this process is authorized by the cloud side. Therefore, it is necessary to obtain the planning request information of the first drone.

[0092] In step 103, the specific process of determining the second grid bitmap corresponding to the at least one UAV route planning area based on the identification information and the planning request information can be determined according to the actual situation and is not limited here. As an example, determining the second grid bitmap corresponding to the at least one UAV route planning area based on the identification information and the planning request information can be done by determining the new path of the at least one UAV based on the identification information and the planning request information, and generating the second grid bitmap corresponding to the route planning area based on the new path. The second grid bitmap can be determined according to the actual situation and is not limited here. As an example, the second grid bitmap can be understood as an updated Bitmap.

[0093] In step 104, the specific judgment process for determining whether the first UAV needs to adjust its flight path based on the first grid bitmap and the second grid bitmap has passed can be determined according to the actual situation and is not limited here. As an example, the judgment process for determining whether the first UAV needs to adjust its flight path based on the first grid bitmap and the second grid bitmap has passed may include comparing the first grid bitmap and the second grid bitmap to obtain a comparison result; if the comparison result shows that the target grids corresponding to the first UAV need to adjust its flight path are all free, the judgment result is determined to be that the first UAV needs to adjust its flight path has passed; if the comparison result shows that the target grids corresponding to the first UAV need to adjust its flight path are not all free, the judgment result is determined to be that the first UAV needs to adjust its flight path has not passed.

[0094] In step 105, the specific determination process for determining whether the first UAV is traveling along the adjusted route based on the judgment result can be determined according to the actual situation and is not limited here. As an example, determining whether the first UAV is traveling along the adjusted route based on the judgment result may include determining that the first UAV is traveling along the adjusted route when the judgment result indicates that the first UAV needs to adjust its route and has passed through it; and determining that the first UAV cannot travel along the adjusted route when the judgment result indicates that the first UAV needs to adjust its route and has not passed through it.

[0095] This application defines a first grid bitmap corresponding to at least one UAV flight path planning area, including identification information of at least one grid. The identification information indicates whether a grid will be released after a preset time when it is occupied. When a first UAV needs to adjust its flight path, the application obtains the planning request information of the first UAV. Based on the identification information and the planning request information, a second grid bitmap corresponding to at least one UAV flight path planning area is determined. Based on the first and second grid bitmaps, the application determines whether the UAV's adjusted flight path has been traversed, obtaining a judgment result. Based on the judgment result, the application determines whether the first UAV will travel according to the adjusted flight path. In other words, by dividing at least one UAV flight path planning area into grids and introducing grid identification information to indicate whether a UAV will fly away after a preset time when a grid is occupied, and whether the occupied grid is released, the application intelligently plans the area based on the grid identification information and the UAV's flight path adjustment request information when a UAV needs to adjust its flight path. This achieves a leap from pilot-controlled aircraft to intelligent flight planning, improving the efficiency and flexibility of grid allocation, while reducing the probability of multiple aircraft occupying the same grid, UAV disconnection, and flight path conflicts between UAVs.

[0096] In one embodiment, determining the first grid bitmap corresponding to at least one UAV route planning area includes:

[0097] Obtain spatial information corresponding to the at least one UAV flight path planning area;

[0098] The spatial information is subjected to grid segmentation to obtain the first grid information corresponding to the spatial information;

[0099] The identification information of the grid is determined based on the first grid information;

[0100] If the identification information indicates that the grid is occupied, after the preset time, the identification information of the grid is re-determined based on the first grid information;

[0101] If the redefined identification information of the grid indicates that the grid has been released, then the second grid information corresponding to the acquisition of the spatial information is obtained;

[0102] The first grid bitmap is generated based on the second grid information.

[0103] In this embodiment, spatial information corresponding to the at least one UAV flight path planning area is obtained; wherein, the spatial information can be determined according to the actual situation and is not limited here. As an example, the spatial information may include 3D spatial information corresponding to the UAV flight path planning area. In practical applications, the 3D spatial information can be simply referred to as 3D space.

[0104] The process of performing grid segmentation on the spatial information to obtain the first grid information corresponding to the spatial information can be determined according to the actual situation and is not limited here. As an example, the process of performing grid segmentation on the spatial information to obtain the first grid information corresponding to the spatial information may include obtaining a first volume parameter corresponding to the overall airspace occupied by the at least one UAV, a second volume parameter of the at least one UAV itself, a speed parameter of the at least one UAV, and a first quantity parameter corresponding to the at least one UAV based on the spatial information; determining the grid segmentation granularity parameter corresponding to the spatial information based on at least one of the first volume parameter, the second volume parameter, the speed parameter, and the first quantity parameter; and performing grid segmentation on the spatial information according to the grid segmentation granularity parameter to obtain the first grid information.

[0105] The specific determination process for determining the identification information of the grid based on the first grid information can be determined according to the actual situation and is not limited here. As an example, determining the identification information of the grid based on the first grid information may include obtaining the location information of each grid based on the first grid information; determining the base duration and incremental duration of a preset timer corresponding to each grid; determining the duration information corresponding to the preset timer according to the base duration and the incremental duration; and using the location information and the duration information as the identification information.

[0106] When the identification information indicates that the grid is occupied, after the preset time, re-determining the identification information of the grid based on the first grid information can be understood as releasing the grid through a preset release strategy after the preset time, so that the occupied grid is no longer occupied, and then re-determining the identification information of the grid based on the first grid information. The release strategy can be determined according to the actual situation and is not limited here. As an example, the release strategy may include: Type: 0, the grid represents that it is not occupied; Type: 1, the grid represents that the aircraft occupying the space has not yet arrived, and the timer is not triggered; Type: 2, the grid represents the current location of the aircraft, and the timer is not triggered; Type: 3, the grid represents that the aircraft has flown away, the timer is triggered, and the grid is automatically released after the timer expires; Type: 4, the grid represents a static obstacle occupying the space, the timer is not triggered, and the grid is not released; Type: 5, the grid represents a dynamic obstacle occupying the space, the timer is triggered, and the grid is automatically released after the timer expires.

[0107] When the redefined grid identification information indicates that the grid has been released, obtaining the second grid information after release corresponding to the spatial information can be understood as the grid occupied by the UAV being released due to the UAV flying away, thereby obtaining the grid information after release.

[0108] Generating the first grid bitmap based on the second grid information can be understood as generating a grid bitmap based on grid information. The grid information can be determined according to actual conditions and is not limited here. As an example, the grid information can be global grid information, which can be denoted as global bitmap.

[0109] In this embodiment, spatial division and grid processing are performed through at least one UAV route planning area, and identification information is introduced to indicate whether a grid is occupied or released, thereby realizing pre-takeoff and in-flight planning strategies for multiple UAVs and ensuring intelligent autonomous flight of a large number of UAVs.

[0110] In one embodiment, the step of performing grid segmentation processing on the spatial information to obtain the first grid information corresponding to the spatial information includes:

[0111] Based on the spatial information, obtain the first volume parameter corresponding to the overall airspace occupied by the at least one UAV, the second volume parameter of the at least one UAV itself, the speed parameter of the at least one UAV flight, and the first quantity parameter corresponding to the at least one UAV;

[0112] The grid segmentation granularity parameter corresponding to the spatial information is determined based on at least one of the first volume parameter, the second volume parameter, the velocity parameter, and the first quantity parameter.

[0113] The spatial information is divided into grids according to the grid division granularity parameters to obtain the first grid information.

[0114] The first volume parameter, the second volume parameter, the velocity parameter, and the first quantity parameter can all be determined according to actual conditions, and are not limited here. As an example, the first volume parameter can be the overall volume of the airspace, which can be denoted as vol. total The second volume parameter can be the average volume of the drone's fuselage, which can be denoted as vol. mean The speed parameter can be the average flight speed of the UAV, which can be denoted as vel; the first quantity parameter can be the number of aircraft, which can be denoted as N.

[0115] The specific determination process for determining the mesh partitioning granularity parameter corresponding to the spatial information based on at least one of the first volume parameter, the second volume parameter, the velocity parameter, and the first quantity parameter can be determined according to actual conditions and is not limited here. As an example, determining the mesh partitioning granularity parameter corresponding to the spatial information based on at least one of the first volume parameter, the second volume parameter, the velocity parameter, and the first quantity parameter can be done by determining the mesh partitioning granularity parameter corresponding to the spatial information based on the first volume parameter, the second volume parameter, the velocity parameter, and the first quantity parameter through a preset algorithm. The granularity parameter and the preset algorithm can both be determined according to actual conditions and are not limited here. As an example, the granularity parameter can be the mesh division granularity, which can be denoted as vol. grid .

[0116] The preset algorithms can all refer to the following formula (1):

[0117]

[0118] In equation (1), the mesh size is vol grid The overall volume of the airspace is vol total Average volume of UAV fuselage (vol) mean 1. Average flight speed of the drone, vel; 2. Number of drones, N.

[0119] vol grid With vol mean Positively correlated, i.e., vol mean The larger the value, the larger the granularity of the mesh should be;

[0120] α∈R, its function is to perform a global scaling on the first half of the above equation;

[0121] vel and vol grid They are positively correlated, and using the negative exponential form of e, exp(-X), can ensure that its value does not fluctuate drastically.

[0122] β∈R, its function is to control the overall scaling of the influence of speed on mesh generation, and can be assigned a default value of 1 if there is no empirical value.

[0123] This is the ratio of the total airspace volume to the total number of aircraft. It can be understood as the average airspace occupied independently by each aircraft, and is related to vol. grid They are positively correlated. Using the negative exponential form of e, exp(-X), ensures that its value does not fluctuate drastically.

[0124] δ∈R, its function is to control its influence on the mesh division by scaling it as a whole. If there is no empirical value, it can be assigned a default value of 1.

[0125] The specific processing steps for performing grid segmentation on the spatial information according to the grid segmentation granularity parameters to obtain the first grid information can be determined according to the actual situation and are not limited here. As an example, the process of performing grid segmentation on the spatial information according to the grid segmentation granularity parameters to obtain the first grid information can be as follows: performing grid segmentation on the spatial information according to the grid segmentation granularity parameters to obtain an initialized global occupancy grid, and then assigning grid information values ​​to the global occupancy grid based on known obstacles to obtain the first grid information; wherein, the first grid information can be determined according to the actual situation and is not limited here. As an example, the first grid information can be global grid information.

[0126] In practical applications, this step can be understood as a partitioning strategy in spatial meshing, dividing the entire spatial domain into several meshes in 3D space with a fixed granularity. Let the mesh partitioning granularity be vol. grid After extensive testing, it was found that mesh generation is more effective when the following factors are considered comprehensively: overall spatial volume (vol). total Average volume of UAV fuselage (vol) mean Let vol be the average flight speed of the drones (vel) and the number of drones (N). grid The division criteria can be referred to in the previous formula (1).

[0127] Through the above division strategy, the division of the spatial grid is guaranteed to be (1) based on the aircraft fuselage volume as the basic granularity, and appropriately scaled in combination with the speed; (2) the grid volume is finely adjusted according to the total spatial volume and the number of aircraft.

[0128] In this embodiment, the efficiency and flexibility of the grid are improved by dividing the spatial information into grids, for example, using the aircraft fuselage volume as the basic granularity and making an appropriate scaling based on the speed; the grid volume is fine-tuned according to the total spatial volume and the number of aircraft.

[0129] In one embodiment, determining the identification information of the grid based on the first grid information includes:

[0130] The location information of each grid is obtained based on the first grid information;

[0131] Determine the base duration and incremental duration of the preset timer corresponding to each grid;

[0132] The duration information corresponding to the preset timer is determined based on the base duration and the incremental duration;

[0133] The location information and the duration information are used as the identification information.

[0134] In this embodiment, the base duration, the incremental duration, and the duration information can all be determined according to actual conditions, and are not limited here. As an example, the base duration can be denoted as T. base The incremental duration can be understood as a dynamic increment, which can be denoted as T. inc The duration information can be understood as the set timer duration, which can be denoted as T.

[0135] In practical applications, a flag bit and a timer are assigned to each grid cell to indicate whether the cell has been occupied within a certain time period. The timer duration is set according to the following principles:

[0136] Let the total number of grids be N. grid The total number of aircraft is N aircraft The probability that an airplane flies over any grid follows a uniform distribution. Then the average probability N of the total number of airplanes passing over a grid is... aircraft / N grid For any grid cell, assume that the number of aircraft flying over it in a future time interval t follows a Poisson distribution. For any grid cell, the probability that at least two aircraft will fly over it in a future time interval t can be determined by formulas (2), (3), (4), and (5):

[0137]

[0138] in:

[0139] λ=N aircraft / N grid (3)

[0140] We require that this probability be less than a fixed value (e.g., 0.2).

[0141] P(N(t)≥2)=1-P(N(t)=1)-P(N(t)=0) (4)

[0142]

[0143] In equations (2), (3), (4), and (5), the only unknown is t. A linearized approximation can be obtained by performing a first-order Taylor expansion of the power function e on the right-hand side of the above equations, followed by solving the positive solution of the quadratic equation to obtain t. This t is used as the baseline T for the duration of each grid timer. base At the same time, considering that the duration should fully take into account the aircraft's speed at takeoff and the complexity of the current surrounding environment, a dynamic increment T is provided on top of the baseline. inc .

[0144] The aircraft's speed is vel, and the minimum distance to the nearest existing non-aircraft occupied grid cell is dis. min (Manhattan distance can be used), let a constant scalar dis be defined. buff Let the radius be dis. min +dis buff The number of non-local placeholder grids is N occup The total number of grids is N total Therefore, the length of the grid timer can be determined by formula (6):

[0145]

[0146] In equation (6), vel is the velocity, and T inc Positive correlation: when the aircraft's flight speed is high, the set time increment should increase; dis min The minimum distance to the nearest existing non-aircraft occupied grid within the surrounding grid, and T inc Negative correlation; N occup The number of grid cells occupied by other nearby aircraft, and T inc Positive correlation; α, β, and γ are all constants and >0, representing the weights of the three; without empirical values, they can be defaulted to 1; using the negative exponential form of e, exp(-X), ensures that its value does not fluctuate drastically; δ∈R is used to correlate T. inc Perform an overall scaling; without empirical values, it can be assigned a default value of 0; η∈R, used to scale T. inc Make an overall offset, which can be set to 0 by default if there is no empirical value. Then, for any grid, the timer duration after the aircraft flies away can be set according to formula (7):

[0147] T = T base +T inc (7)

[0148] The advantages of this mechanism are: (1) the grid occupancy time can be dynamically adjusted according to the volume of the airspace and the number of aircraft; (2) the time a grid is locked by a certain aircraft is as short as possible, which is conducive to improving the efficiency and flexibility of the system in allocating grids; (3) the probability of the same grid being occupied by multiple aircraft is reduced as much as possible.

[0149] In this embodiment, the location information of each grid is obtained based on the first grid information; the base duration and incremental duration of a preset timer corresponding to each grid are determined; the duration information corresponding to the preset timer is determined based on the base duration and incremental duration; and the location information and duration information are used as the identification information. That is, the goal is to minimize the time a grid is locked by a particular aircraft, thereby reducing the probability of multiple aircraft occupying the same grid.

[0150] In one embodiment, determining the base duration of the preset timer corresponding to each grid includes:

[0151] Obtain a first quantity parameter corresponding to the at least one UAV and a second quantity parameter corresponding to the grid;

[0152] The probability parameters of the at least one UAV flying over the grid are determined based on the first quantity parameter and the second quantity parameter;

[0153] The baseline duration is determined based on the probability parameters.

[0154] In this embodiment, the drone, the first quantity parameter, and the second quantity parameter can all be determined according to actual conditions, and are not limited here. As an example, the drone can be an airplane; the first quantity parameter can be the total number of airplanes, which can be denoted as N. aircraft The second quantity parameter can be understood as the total number of grid cells, which can be denoted as N. grid .

[0155] The specific process for determining the probability parameters of at least one UAV flying over the grid based on the first and second quantity parameters can be determined according to the actual situation and is not limited here. As an example, the probability parameters of at least one UAV flying over the grid can be determined by a preset algorithm based on the first and second quantity parameters. The preset algorithm can be determined according to the actual situation and is not limited here. As an example, the preset algorithm can refer to (2), (3), (4), and (5) above.

[0156] In equations (2), (3), (4), and (5), the only unknown is t. A linearized approximation can be obtained by performing a first-order Taylor expansion of the power function e on the right-hand side of the above equations, followed by solving the positive solution of the quadratic equation to obtain t. This t is used as the baseline T for the duration of each grid timer. base .

[0157] In this embodiment, a first quantity parameter corresponding to at least one drone and a second quantity parameter corresponding to the grid are obtained; a probability parameter for at least one drone flying over the grid is determined based on the first and second quantity parameters; and a base duration is determined based on the probability parameter. That is, the time a grid is locked by a certain aircraft is minimized, reducing the probability of multiple aircraft occupying the same grid.

[0158] In one embodiment, determining the incremental duration of a preset timer corresponding to each grid includes:

[0159] The system acquires the speed parameters of the first drone passing through the first grid, the distance parameters between the first grid and the second grid occupied by the closest second drone to the first grid, and the grid parameters occupied by drones other than the first drone within a preset area; the first grid is any grid in each grid; the preset area is determined based on the distance parameters.

[0160] The incremental duration is determined based on the velocity parameter, the distance parameter, and the grid parameter.

[0161] In this embodiment, the speed parameter, distance parameter, and grid parameter can all be determined according to actual conditions, and are not limited here. As an example, the speed parameter can be understood as the aircraft's passing speed, which can be denoted as vel; the distance parameter can be understood as the minimum distance to the nearest existing non-aircraft-occupied grid in the surrounding grid, which can be denoted as dis. min The mesh parameter can be understood as the radius dis. min +dis buff The number of non-native placeholder grid cells; where, this dis buff It can be understood as a constant scalar, and this radius dis min +dis buff The number of non-local placeholder grid cells can be denoted as N. occup .

[0162] As an example, the aircraft's passing speed is vel, and the minimum distance to the nearest existing non-aircraft occupied grid cell is dis. min (Manhattan distance can be used), let a constant scalar dis be defined. buff Let the radius be dis. min +dis buff The number of non-local placeholder grids is N occup The total number of grids is N total .

[0163] The specific determination process for determining the incremental duration based on the velocity parameter, distance parameter, and grid parameter can be determined according to actual conditions and is not limited here. As an example, determining the incremental duration based on the velocity parameter, distance parameter, and grid parameter can be done by determining the incremental duration based on the velocity parameter, distance parameter, and grid parameter using a preset algorithm. Both the preset algorithm and the incremental duration can be determined according to actual conditions and are not limited here. As an example, the incremental duration can be understood as a dynamic increment, which can be denoted as T. inc The preset algorithm can be referred to in the previous formula (6).

[0164] In equation (6), vel is the velocity, and T inc Positive correlation: when the aircraft's flight speed is high, the set time increment should increase; dis min The minimum distance to the nearest existing non-aircraft occupied grid within the surrounding grid, and T inc Negative correlation; N occup The number of grid cells occupied by other nearby aircraft, and T inc Positive correlation; α, β, and γ are all constants and >0, representing the weights of the three; without empirical values, they can be defaulted to 1; using the negative exponential form of e, exp(-X), ensures that its value does not fluctuate drastically; δ∈R is used to correlate T. inc Perform an overall scaling; without empirical values, it can be assigned a default value of 0; η∈R, used to scale T. inc Make an overall offset; if there is no empirical value, you can default to 0.

[0165] In this embodiment, the incremental time is determined by the speed parameters of the first UAV passing through the first grid, the distance parameters between the first grid and the second grid occupied by the second UAV closest to the first grid, and the grid parameters of the UAVs occupying other UAVs in the preset area; that is, the grid occupancy time can be dynamically adjusted with the volume of the airspace and the number of aircraft.

[0166] In one embodiment, the step of determining whether the flight path adjustment required by the first UAV has been passed based on the first grid bitmap and the second grid bitmap, and obtaining the determination result, includes:

[0167] The first grid bitmap and the second grid bitmap are compared to obtain the comparison result;

[0168] If the comparison results show that the target grids corresponding to the flight path adjustment of the first UAV are all empty, it is determined that the judgment result is that the flight path adjustment of the first UAV has been passed.

[0169] If the comparison results show that the target grid corresponding to the flight path adjustment of the first UAV is not all empty, it is determined that the judgment result is that the flight path adjustment of the first UAV has not been passed.

[0170] Determining whether the first UAV follows the adjusted flight path based on the judgment result includes:

[0171] If the determination result is that the first UAV needs to adjust its route and the route is passed, it is determined that the first UAV will travel according to the adjusted route.

[0172] If the determination result is that the first UAV needs to adjust its route but the route is not passed, it is determined that the first UAV cannot travel according to the adjusted route.

[0173] The first grid bitmap can be understood as the global grid information maintained by the global bitmap; the second grid bitmap can be understood as updating the local grid bitmap; the bitmap records the grid information of the new line (type set to 1) and the grid information of the old line that needs to be released (type set to 0).

[0174] If the comparison results show that all target grids corresponding to the flight path adjustment of the first UAV are idle, and the judgment result is that the flight path adjustment of the first UAV is approved, it can be understood as a newly requested grid that is currently all idle, then the decision is made to approve it.

[0175] If the comparison results indicate that the target grid corresponding to the flight path adjustment of the first UAV is not all empty, then the judgment result that the flight path adjustment of the first UAV was not approved can be understood as a new request for a grid that is not currently all empty, and thus it is determined that the request is not approved.

[0176] In practical applications, the cloud side uniformly calculates grid information and then broadcasts it to all aircraft. The terminal initiates pre-planning. Assuming aircraft A needs to dynamically adjust its flight path, A needs to pre-update its local grid on the terminal side. The pre-update method is as follows: back up the local grid (local bitmap --> bitmap~), record the placeholder grid information for the new route (type set to 1) and the grid information for the old route that needs to be released (type set to 0) in bitmap~, and send bitmap~ to the cloud side. After receiving A's pre-planning request, the cloud side compares it with its own maintained global grid information (global bitmap). If all the grids requested by A are currently free, it is considered approved; otherwise, it is considered rejected. If approved, the cloud side first updates its own global grid information and then broadcasts the new information to all aircraft, including A. If rejected, the cloud side's grid information remains unchanged, and a response is sent to A, notifying A of its disagreement with the new route. When the judgment result is that the first UAV's flight path adjustment is approved, determining that the first UAV travels according to the adjusted flight path can be understood as the first UAV executing the adjusted flight path when the judgment result is that the first UAV's flight path adjustment is approved.

[0177] If the determination result is that the first UAV needs to adjust its route but the route is not passed, then determining that the first UAV cannot travel according to the adjusted route can be understood as the first UAV not executing the adjusted route and maintaining the original route if the determination result is that the first UAV needs to adjust its route but the route is not passed.

[0178] In practical applications, if the cloud determines that the route is approved, the local grid information is updated, that is, the bitmap is replaced with the original bitmap, and the new route is executed; for other aircraft: the local grid information is updated, that is, the bitmap is replaced with the original bitmap; if the cloud determines that the route is not approved, the original route is maintained until the new grid information is received from the cloud.

[0179] In this embodiment, if the comparison results show that all target grids corresponding to the route that the first UAV needs to adjust are empty, the judgment result is determined to be that the route that the first UAV needs to adjust has been passed; if the comparison results show that not all target grids corresponding to the route that the first UAV needs to adjust are empty, the judgment result is determined to be that the route that the first UAV needs to adjust has not been passed; then, if the judgment result is that the route that the first UAV needs to adjust has been passed, the first UAV is determined to travel according to the adjusted route; if the judgment result is that the route that the first UAV needs to adjust has not been passed, the first UAV is determined not to travel according to the adjusted route, so as to realize intelligent management of aerial UAVs and intelligent route planning and design.

[0180] In one embodiment, if the determination result indicates that the first UAV needs to adjust its flight path and has not passed, the method further includes:

[0181] A response message is sent to the first UAV to request the planning information; the response message is used for the first UAV to travel along the corresponding route in the first grid bitmap; or...

[0182] If the determination result indicates that the first UAV needs to adjust its flight path to pass, the method further includes:

[0183] Update the first grid bitmap to the second grid bitmap;

[0184] The second grid bitmap is broadcast to the at least one UAV; the second grid bitmap is used by the first UAV to travel along the adjusted route.

[0185] In this embodiment, the response information can be determined according to the actual situation, and is not limited here. As an example, the response information can be denoted as response.

[0186] In practical applications, after receiving a pre-planning request from A, the cloud side compares it with its own maintained global bitmap information. If all the grids requested by A are currently idle, the request is approved; otherwise, it is rejected. If rejected, the cloud side's grid information remains unchanged, and a response is sent to A, notifying A of its disapproval of the new route. Updating the first bitmap to the second bitmap can be understood as updating its own global bitmap to obtain new information.

[0187] In practical applications, assuming the cloud receives a pre-planning request from aircraft A, it compares it with its own maintained global bitmap. If all the grids requested by A are currently idle, the request is approved. If approved, the cloud first updates its own global bitmap and then broadcasts the new information to all aircraft, including A.

[0188] In this embodiment, if the determination result is that the first UAV needs to adjust its route but the route is not passed, a response message for a planning request is sent to the first UAV. The response message is used for the first UAV to travel according to the route corresponding to the first grid bitmap. Alternatively, if the determination result is that the first UAV needs to adjust its route and the route is passed, the first grid bitmap is updated to a second grid bitmap. The second grid bitmap is broadcast to the at least one UAV. The second grid bitmap is used for the first UAV to travel according to the adjusted route, so as to maintain the original route or execute a new route.

[0189] In one embodiment, the method further includes:

[0190] If the redefined identifier information of the grid indicates that the grid has been released, obtain the difference information between the third grid bitmap after the grid has been released and the first grid bitmap.

[0191] Based on the difference information, it is determined whether the flight path corresponding to the at least one UAV has been passed;

[0192] If the flight path corresponding to at least one UAV is passed, the third grid bitmap updates the first grid bitmap, and the third grid bitmap is broadcast to the at least one UAV; the third grid bitmap is used to determine the flight path corresponding to the at least one UAV.

[0193] In this embodiment, when the redefined identifier information of the grid indicates that the grid is being released, it can be understood as grid information that the old line needs to be released. In practical applications, type can be set to 0 to indicate grid information that the old line needs to be released.

[0194] In practical applications, when the end-side or third-party sensors detect an unknown obstacle in the airspace, the new obstacle information is reported to the cloud side. The cloud side compares it with the existing grid information. If the information already exists in the current grid, it is ignored; otherwise, (1) the cloud side global grid information is updated, (2) it is broadcast to all aircraft, and (3) the aircraft updates its local grid data after receiving it.

[0195] In this embodiment, when the redefined grid identification information indicates that the grid has been released, the difference information between the third grid bitmap after the grid is released and the first grid bitmap is obtained; based on the difference information, it is determined whether the flight path corresponding to at least one UAV has been passed; if the flight path corresponding to at least one UAV has been passed, the third grid bitmap is updated to the first grid bitmap, and the third grid bitmap is broadcast to at least one UAV; the third grid bitmap is used to determine the flight path corresponding to the at least one UAV, so that the grid information is up-to-date, ensuring the uniqueness of the passing aircraft and guaranteeing flight safety.

[0196] For ease of understanding, the example UAV route planning method here is specifically a 5G-based BeiDou UAV intelligent air transportation method, applied to a 5G-based BeiDou UAV intelligent air transportation system.

[0197] This proposal puts forward a 5G+BeiDou unmanned aerial vehicle intelligent transportation system:

[0198] 1. The 5G+BeiDou UAV intelligent aerial transportation system adopts a "cloud-network-terminal" system architecture. The cloud is the UAV intelligent management platform, the network is the mobile 5G network + BeiDou network, and the terminal is the 5G+BeiDou airborne communication terminal.

[0199] 2. The 5G+BeiDou UAV intelligent management platform comprises multiple levels. The primary platform is the overall UAV control platform, which is further divided into several secondary application platforms, such as UAV transportation applications, logistics applications, emergency support, and park inspection. It also includes general capability modules such as target recognition and 3D modeling for easy access.

[0200] 3. The 5G+BeiDou UAV onboard communication terminal adopts a dual-mode design of 5G and BeiDou short message service. When 5G or 4G is available, it prioritizes access to the cellular network; where there is no cellular network, it uses BeiDou short message service. Using both cellular network and BeiDou short message service, UAV information can be transmitted back to the UAV intelligent management platform without distance limitations, and UAVs can be controlled beyond line-of-sight. Simultaneously, the UAV onboard communication terminal can access the "ONE POINT" platform to achieve China Mobile's 5G+BeiDou high-precision positioning.

[0201] 4. The 5G+BeiDou UAV intelligent aerial transportation system uses latitude, longitude, altitude, and time to determine aircraft waypoints and comprehensively plan flight routes. The flight routes are synchronized between the onboard communication terminal and the intelligent management platform. After the management platform issues the flight route, the UAV can dynamically adjust its route based on the flight situation and surrounding environment during flight, and synchronize with the management platform. Different UAVs can fly through the same spatial grid during flight, but the time values ​​must be staggered by more than one minute.

[0202] like Figure 2 As shown, Figure 2This application presents a "cloud-network-terminal" architecture for the UAV route planning method. The cloud represents the UAV intelligent management platform, the network represents the mobile 5G network + BeiDou network, and the terminal represents the 5G + BeiDou airborne communication terminal. The route planning grid can be deployed on the cloud and the terminal respectively. The cloud is responsible for global planning, while the terminal is responsible for real-time local planning.

[0203] I. Spatial grid segmentation.

[0204] 1.1 Partitioning Strategy.

[0205] The entire spatial domain is divided into several grids in 3D space with a fixed granularity, let the grid division granularity be vol. grid After extensive testing, it was found that mesh generation is more effective when the following factors are considered comprehensively: overall spatial volume (vol). total Average volume of UAV fuselage (vol) mean Let vol be the average flight speed of the drones (vel) and the number of drones (N). grid The division criteria can be referenced in formula (1).

[0206] In equation (1), vol grid With vol mean Positively correlated, i.e., vol mean The larger the value, the larger the granularity of the mesh should be;

[0207] α∈R, its function is to perform a global scaling on the first half of the above equation;

[0208] vel and vol grid They are positively correlated, and using the negative exponential form of e, exp(-X), can ensure that its value does not fluctuate drastically.

[0209] β∈R, its function is to control the overall scaling of the influence of speed on mesh generation, and can be assigned a default value of 1 if there is no empirical value.

[0210] This is the ratio of the total airspace volume to the total number of aircraft. It can be understood as the average airspace occupied independently by each aircraft, and is related to vol. grid They are positively correlated. Using the negative exponential form of e, exp(-X), ensures that its value does not fluctuate drastically.

[0211] δ∈R, its function is to control its influence on mesh generation by scaling it as a whole. If there is no empirical value, it can be assigned a default value of 1.

[0212] Through the above mechanism, the division of the spatial grid is guaranteed to be (1) based on the aircraft fuselage volume as the basic granularity, and appropriately scaled in combination with the speed; (2) the grid volume is finely adjusted according to the total spatial volume and the number of aircraft.

[0213] In this proposal, a flag and a timer are allocated to each grid cell to indicate whether the cell has been occupied within a certain time period. The timer duration is set according to the following principles:

[0214] Let the total number of grids be N. grid The total number of aircraft is N aircraft The probability that an airplane flies over any grid follows a uniform distribution. Then the average probability N of the total number of airplanes passing over a grid is... aircraft / N grid For any grid, assume that the number of aircraft flying over it in a future time period t follows a Poisson distribution. For any grid, the probability that at least two aircraft will fly over it in a future time period t can be referred to the previous formulas (2), (3), (4), and (5).

[0215] In equations (2), (3), (4), and (5), the only unknown is t. A linearized approximation can be obtained by performing a first-order Taylor expansion of the power function e on the right-hand side of the above equations, followed by solving the positive solution of the quadratic equation to obtain t. This t is used as the baseline T for the duration of each grid timer. base At the same time, considering that the duration should fully take into account the aircraft's speed at takeoff and the complexity of the current surrounding environment, a dynamic increment T is provided on top of the baseline. inc .

[0216] The aircraft's speed is vel, and the minimum distance to the nearest existing non-aircraft occupied grid cell is dis. min (Manhattan distance can be used), let a constant scalar dis be defined. buff Let the radius be dis. min +dis buff The number of non-local placeholder grids is N occup The total number of grids is N total Therefore, the length of the grid timer can be determined by referring to the previous formula (6).

[0217] In equation (6), vel is the velocity, and T inc Positive correlation: when the aircraft's flight speed is high, the set time increment should increase; dis min The minimum distance to the nearest existing non-aircraft occupied grid within the surrounding grid, and T inc Negative correlation; N occup The number of grid cells occupied by other nearby aircraft, and T inc Positive correlation; α, β, and γ are all constants and >0, representing the weights of the three; without empirical values, they can be defaulted to 1; using the negative exponential form of e, exp(-X), ensures that its value does not fluctuate drastically; δ∈R is used to correlate T. inc Perform an overall scaling; without empirical values, it can be assigned a default value of 0; η∈R, used to scale T. incMake an overall offset, which can be set to 0 by default if there is no empirical value. Then, for any grid, the timer duration after the aircraft flies away can be set according to the previous formula (7).

[0218] The advantages of this mechanism are: (1) the grid occupancy time can be dynamically adjusted according to the volume of the airspace and the number of aircraft; (2) the time a grid is locked by a certain aircraft is as short as possible, which is conducive to improving the efficiency and flexibility of the system in allocating grids; (3) the probability of the same grid being occupied by multiple aircraft is reduced as much as possible.

[0219] like Figure 3 As shown, Figure 3 This is a schematic diagram showing a grid representing a space that has been occupied, as described in an embodiment of this application.

[0220] 1.2 Release Strategy.

[0221] Type: 0, the grid is not occupied.

[0222] Type: 1, the grid indicates that its placeholder aircraft has not yet arrived and the timer is not triggered.

[0223] Type: 2, the grid represents the current location of the aircraft, and the timer is not triggered.

[0224] Type: 3, the grid indicates that the aircraft has flown away, triggering the timer, which will automatically release when the timer expires.

[0225] Type: 4, the grid represents a static obstacle that occupies space, does not trigger a timer, and is not released.

[0226] Type: 5, the grid represents dynamic obstacle occupancy, triggers a timer, and automatically releases after the timer expires.

[0227] 1.3 Deployment strategy.

[0228] The grid is deployed simultaneously on both the cloud side and the aircraft side.

[0229] 1.4 Synchronization Strategy.

[0230] 1.4.1 Before takeoff.

[0231] The cloud side uses a unified computing grid, and the specific computing scheme is described in 1.1, which is then broadcast to all aircraft.

[0232] 1.4.2 After takeoff.

[0233] During flight, an aircraft may need to dynamically adjust its flight path.

[0234] 1) Local grid pre-update.

[0235] Pre-planning is initiated on the device side. Assuming that aircraft A needs to dynamically adjust its flight path, A needs to pre-update its local mesh on the device side. The pre-update method is as follows: back up the local mesh local bitmap --> bitmap~, record the placeholder mesh information for the new route (type set to 1) and the mesh information for the old route that needs to be released (type set to 0) in bitmap~, and send bitmap~ to the cloud side.

[0236] 2) Cloud-side determination.

[0237] After receiving A's pre-planning request, the cloud side compares it with its own global bitmap. If all the grids requested by A are currently idle, the request is approved; otherwise, it is rejected.

[0238] If successful, the cloud side first updates its own global grid information, and then broadcasts the new information to all aircraft, including A.

[0239] If the request fails, the cloud-side grid information remains unchanged, and a response is sent to A to notify A that it does not agree to the new line.

[0240] 3) Execution on the end side.

[0241] If the cloud determines that the route is approved, A: Update the local grid information, that is, replace the original bitmap with the bitmap and execute the new route.

[0242] Other aircraft: Update local grid information, that is, replace the original bitmap with bitmap~.

[0243] If the cloud determines that the connection is not approved, A: Maintain the existing connection until the new grid information is received from the cloud.

[0244] 1.4.3 Obstacle information update.

[0245] When an edge-side or third-party sensor detects an unknown obstacle in the airspace, the new obstacle information is reported to the cloud, which then compares it with existing grid information.

[0246] If the information already exists in the current grid, ignore it;

[0247] Otherwise, (1) update the cloud-side global grid information, (2) broadcast to all aircraft, and (3) update the local grid data after the aircraft receives the information.

[0248] II. Overall Planning.

[0249] 2.1 Route planning before multi-aircraft takeoff.

[0250] Route planning is carried out uniformly by the cloud side, such as Figure 4 As shown, Figure 4This is a schematic diagram illustrating the unified cloud-side route planning in an embodiment of this application. The specific steps are as follows:

[0251] 1. Initialize the global placeholder grid and assign grid information values ​​to known obstacles.

[0252] 2. Number the drone swarm according to any rule to form a sequence.

[0253] 3. Traverse the drone sequence and perform the following steps:

[0254] (1) Based on the existing occupancy grid and the current UAV start / end point information, global planning is carried out according to the shortest path priority principle. The planning algorithm can use industry-standard algorithms (A* or Dijkstra, which are not limited here) to generate global paths.

[0255] (2) Set the global path grid to Type 1, and record the aircraft ID in the path grid.

[0256] (3) Update global grid information.

[0257] 4. After traversing all aircraft, send the route to the corresponding numbered aircraft.

[0258] 5. Broadcast the global grid to all aircraft.

[0259] 2.2 Planning during multi-aircraft flight.

[0260] Since all occupancy map information has been synchronized at the cloud side, when an aircraft encounters an unknown obstacle detected by its own sensors or needs to return to base or change its target for special reasons, the aircraft itself can replan its route. However, to ensure the uniqueness of aircraft passing through a certain local space at the same time and to ensure flight safety, this process must be authorized by the cloud side. The specific steps are as follows:

[0261] 1. If aircraft A needs to adjust its route, it will perform local planning based on its existing local grid and the principle of shortest path priority. The planning algorithm can use industry-standard algorithms (either artificial potential field method or dynamic window approach algorithm, which is not limited here) to generate a new path.

[0262] 2. Compare the differences between the old and new paths, referring to "Local Grid Pre-Update" in section 1.4.2 above, and request cloud-side updates;

[0263] 3. When a cloud-side response is received, (1) if the cloud-side response is successful, the local grid is updated and a new route is executed; (2) if the cloud-side response is unsuccessful, the aircraft hovers and waits for the cloud-side to broadcast new grid information before proceeding to step 1.

[0264] The process for aircraft A is as follows: Figure 5 As shown, Figure 5 This is a flowchart illustrating aircraft A according to an embodiment of this application.

[0265] Cloud-side process as follows Figure 6 As shown, Figure 6 This is a schematic diagram of the cloud-side process in an embodiment of this application.

[0266] This application proposes a cloud, network, and terminal architecture for connected UAVs, a multi-level architecture mode for UAV operation and management platforms, intelligent management and intelligent route planning design for UAVs in the air based on the 5G-based Beidou UAV intelligent air transportation system, route space allocation and release strategies for the 5G-based Beidou UAV intelligent air transportation system, and pre-takeoff and in-flight planning strategies for multiple UAVs.

[0267] This application, through a 5G-based BeiDou-based intelligent aerial transportation system for unmanned aerial vehicles (UAVs), establishes strategies for route space allocation and release, as well as pre-takeoff and in-flight planning for multiple UAVs. This ensures intelligent and autonomous flight for a massive number of UAVs, achieving a leap from pilot-controlled aircraft to intelligent flight planning. It proposes a strategy of dividing the airspace into a four-dimensional grid using latitude, longitude, altitude, and time, and adding a timer to improve allocation efficiency while maintaining global consistency in airspace division.

[0268] This application utilizes a "cloud-network-terminal" system architecture for intelligent aerial transportation of UAVs based on 5G and BeiDou navigation. The cloud serves as the intelligent management platform for UAVs, the network comprises a mobile 5G network and a BeiDou network, and the terminal is a 5G+BeiDou airborne communication terminal. The 5G+BeiDou intelligent management platform enables overall control of the UAVs.

[0269] This application adopts a timed release strategy for airspace grid occupancy. The advantage of this mechanism is that the time a grid is locked by a certain aircraft is kept as short as possible, which helps to improve the efficiency and flexibility of the system in allocating grids; at the same time, it minimizes the probability of the same grid being occupied by multiple aircraft.

[0270] To implement the method of this application embodiment, this application embodiment also provides a UAV route planning device 700, which is installed on the UAV route planning equipment. Figure 7 This is a schematic diagram of a UAV route planning device according to an embodiment of this application; as shown Figure 7 As shown, it includes:

[0271] The first determining unit 701 is used to determine a first grid bitmap corresponding to at least one UAV flight path planning area; the first grid bitmap includes identification information of at least one grid; the identification information indicates whether the grid will be released after a preset time when it is occupied;

[0272] The acquisition unit 702 is used to acquire the planning request information of the first drone when the first drone needs to adjust its flight path; the first drone is any one of the at least one drones.

[0273] The second determining unit 703 is used to determine the second grid bitmap corresponding to the at least one UAV route planning area based on the identification information and the planning request information;

[0274] The judgment unit 704 is used to determine whether the first UAV needs to adjust its flight path based on the first grid bitmap and the second grid bitmap, and obtain a judgment result.

[0275] The third determining unit 705 is used to determine whether the first UAV travels according to the adjusted route based on the judgment result.

[0276] Here, in one embodiment, the first determining unit 701 is further configured to: acquire spatial information corresponding to the at least one UAV flight path planning area; perform grid segmentation processing on the spatial information to obtain first grid information corresponding to the spatial information; determine the identification information of the grid based on the first grid information; if the identification information indicates that the grid is occupied, after the preset time, re-determine the identification information of the grid based on the first grid information; if the re-determined identification information of the grid indicates that the grid is released, obtain second grid information corresponding to the acquired spatial information; and generate the first grid bitmap based on the second grid information.

[0277] Here, in one embodiment, the first determining unit 701 is further configured to obtain, based on the spatial information, a first volume parameter corresponding to the overall airspace occupied by the at least one UAV, a second volume parameter of the at least one UAV itself, a speed parameter of the at least one UAV flight, and a first quantity parameter corresponding to the at least one UAV; and determine the grid segmentation granularity parameter corresponding to the spatial information based on at least one of the first volume parameter, the second volume parameter, the speed parameter, and the first quantity parameter.

[0278] The spatial information is divided into grids according to the grid division granularity parameters to obtain the first grid information.

[0279] Here, in one embodiment, the first determining unit 701 is further configured to input the first image dataset and the first image data into the initial image-text model to generate initial image description text; and construct the second image description text based on the initial image description text and images under at least one preset severe weather scenario.

[0280] Here, in one embodiment, the first determining unit 701 is further configured to obtain the location information of each grid based on the first grid information; determine the base duration and incremental duration of a preset timer corresponding to each grid; determine the duration information corresponding to the preset timer according to the base duration and the incremental duration; and use the location information and the duration information as the identification information.

[0281] Here, in one embodiment, the first determining unit 701 is further configured to obtain a first quantity parameter corresponding to the at least one UAV and a second quantity parameter corresponding to the grid; determine a probability parameter of the at least one UAV flying over the grid based on the first quantity parameter and the second quantity parameter; and determine the reference duration based on the probability parameter.

[0282] Here, in one embodiment, the first determining unit 701 is further configured to obtain the speed parameters of the first UAV passing through the first grid, the distance parameters between the first grid and the second grid occupied by the second UAV closest to the first grid, and the grid parameters of UAVs other than the first UAV within a preset area; the first grid is any grid in each grid; the preset area is determined based on the distance parameters; and the incremental duration is determined based on the speed parameters, the distance parameters, and the grid parameters.

[0283] Here, in one embodiment, the judgment unit 704 is further configured to compare the first grid bitmap and the second grid bitmap to obtain a comparison result; if the comparison result shows that the target grids corresponding to the flight path that the first UAV needs to adjust are all free, the judgment result is determined to be that the flight path that the first UAV needs to adjust has been passed; if the comparison result shows that the target grids corresponding to the flight path that the first UAV needs to adjust are not all free, the judgment result is determined to be that the flight path that the first UAV needs to adjust has not been passed.

[0284] The third determining unit 705 is further configured to determine that the first UAV will travel along the adjusted route if the determination result is that the first UAV needs to adjust its route and the route is passed; and to determine that the first UAV cannot travel along the adjusted route if the determination result is that the first UAV needs to adjust its route and the route is not passed.

[0285] Here, in one embodiment, if the determination result indicates that the first UAV needs to adjust its route and has not passed; the device 700 further includes a sending unit, used to send response information of the planning request information to the first UAV; the response information is used for the first UAV to travel according to the route corresponding to the first grid bitmap; or,

[0286] If the determination result indicates that the first UAV needs to adjust its flight path to pass, the device 700 further includes an update unit and a broadcast unit; wherein,

[0287] The update unit is used to update the first grid bitmap to the second grid bitmap;

[0288] The broadcasting unit is used to broadcast the second grid bitmap to the at least one UAV; the second grid bitmap is used by the first UAV to travel according to the adjusted route.

[0289] Here, in one embodiment, the acquisition unit 702 is further configured to acquire the difference information between the third grid bitmap after the grid is released and the first grid bitmap when the redefined identification information of the grid indicates that the grid has been released;

[0290] The judgment unit 704 is also used to determine whether the route corresponding to the at least one UAV has been passed based on the difference information;

[0291] The updating unit is further configured to update the first grid bitmap with the third grid bitmap and broadcast the third grid bitmap to the at least one drone when the flight path corresponding to the at least one drone is passed; the third grid bitmap is used to determine the flight path corresponding to the at least one drone.

[0292] It should be noted that the UAV route planning device provided in the above embodiments is only illustrated by the division of the above-described program modules when performing UAV route planning. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the UAV route planning device and the UAV route planning method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0293] Based on the hardware implementation of the above program modules, this application embodiment also provides an electronic device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the steps in the UAV route planning method provided in the above embodiment.

[0294] Correspondingly, this application provides a computer program product, which, when executed by a processor, implements the steps in the UAV route planning method provided in the above embodiments.

[0295] Correspondingly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the UAV route planning method provided in the above embodiments.

[0296] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0297] It should be noted that, Figure 8 This is a schematic diagram of the hardware structure of the UAV route planning device in this application embodiment, such as... Figure 8 As shown, the hardware entity of the UAV route planning device 800 includes a processor 801 and a memory 803. Optionally, the UAV route planning device 800 may also include a communication interface 802.

[0298] It is understood that memory 803 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 803 described in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0299] The methods disclosed in the embodiments of this application can be applied to or implemented by processor 801. Processor 801 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 801 or by instructions in software form. The processor 801 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 801 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in memory 803. Processor 801 reads the information in memory 803 and combines it with its hardware to complete the steps of the aforementioned method.

[0300] In an exemplary embodiment, the device may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0301] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential 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 this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0302] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0303] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0304] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0305] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0306] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for unmanned aerial vehicle (UAV) route planning, characterized in that, include: Determine the first grid bitmap corresponding to at least one UAV flight path planning area; The first grid bitmap includes identification information for at least one grid; The identification information indicates whether the grid will be released after a preset time when it is occupied; If the first drone needs to adjust its flight path, obtain the planning request information of the first drone; The first drone is any one of the at least one drones; Determine the second grid bitmap corresponding to the at least one UAV route planning area based on the identification information and the planning request information; Based on the first grid bitmap and the second grid bitmap, it is determined whether the first UAV needs to adjust its flight path and whether it has passed through, and the determination result is obtained; Based on the judgment result, determine whether the first UAV travels according to the adjusted route.

2. The method according to claim 1, characterized in that, The step of determining the first grid bitmap corresponding to at least one UAV flight path planning area includes: Obtain spatial information corresponding to the at least one UAV flight path planning area; The spatial information is subjected to grid segmentation to obtain the first grid information corresponding to the spatial information; The identification information of the grid is determined based on the first grid information; If the identification information indicates that the grid is occupied, after the preset time, the identification information of the grid is re-determined based on the first grid information; If the redefined grid identification information indicates that the grid has been released, then the second grid information corresponding to the spatial information is obtained; The first grid bitmap is generated based on the second grid information.

3. The method according to claim 2, characterized in that, The step of performing grid segmentation on the spatial information to obtain the first grid information corresponding to the spatial information includes: Based on the spatial information, obtain the first volume parameter corresponding to the overall airspace occupied by the at least one UAV, the second volume parameter of the at least one UAV itself, the speed parameter of the at least one UAV flight, and the first quantity parameter corresponding to the at least one UAV; The grid segmentation granularity parameter corresponding to the spatial information is determined based on at least one of the first volume parameter, the second volume parameter, the velocity parameter, and the first quantity parameter. The spatial information is divided into grids according to the grid division granularity parameters to obtain the first grid information.

4. The method according to claim 2, characterized in that, Determining the identification information of the grid based on the first grid information includes: The location information of each grid is obtained based on the first grid information; Determine the base duration and incremental duration of the preset timer corresponding to each grid; The duration information corresponding to the preset timer is determined based on the base duration and the incremental duration; The location information and the duration information are used as the identification information.

5. The method according to claim 4, characterized in that, Determining the base duration of the preset timer corresponding to each grid includes: Obtain a first quantity parameter corresponding to the at least one UAV and a second quantity parameter corresponding to the grid; The probability parameters of the at least one UAV flying over the grid are determined based on the first quantity parameter and the second quantity parameter; The baseline duration is determined based on the probability parameters.

6. The method according to claim 4, characterized in that, Determining the incremental duration of the preset timer corresponding to each grid includes: The system acquires the speed parameters of the first drone passing through the first grid, the distance parameters between the first grid and the second grid occupied by the closest second drone to the first grid, and the grid parameters occupied by drones other than the first drone within a preset area; the first grid is any grid in each grid; the preset area is determined based on the distance parameters. The incremental duration is determined based on the velocity parameter, the distance parameter, and the grid parameter.

7. The method according to claim 1, characterized in that, The step of determining whether the first UAV needs to adjust its flight path based on the first grid bitmap and the second grid bitmap has been passed, and obtaining the determination result, includes: The first grid bitmap and the second grid bitmap are compared to obtain the comparison result; If the comparison results show that the target grids corresponding to the flight path adjustment of the first UAV are all empty, it is determined that the judgment result is that the flight path adjustment of the first UAV has been passed. If the comparison results show that the target grid corresponding to the flight path adjustment of the first UAV is not all empty, it is determined that the judgment result is that the flight path adjustment of the first UAV has not been passed. Determining whether the first UAV follows the adjusted flight path based on the judgment result includes: If the determination result is that the first UAV needs to adjust its route and the route is passed, it is determined that the first UAV will travel according to the adjusted route. If the determination result is that the first UAV needs to adjust its route but the route is not passed, it is determined that the first UAV cannot travel according to the adjusted route.

8. The method according to claim 7, characterized in that, If the determination result is that the first UAV needs to adjust its flight path and has not passed; the method further includes: A response message is sent to the first UAV to request the planning information; the response message is used for the first UAV to travel along the corresponding route in the first grid bitmap; or... If the determination result indicates that the first UAV needs to adjust its flight path to pass, the method further includes: Update the first grid bitmap to the second grid bitmap; The second grid bitmap is broadcast to the at least one UAV; the second grid bitmap is used by the first UAV to travel along the adjusted route.

9. The method according to claim 2, characterized in that, The method further includes: If the redefined identifier information of the grid indicates that the grid has been released, obtain the difference information between the third grid bitmap after the grid has been released and the first grid bitmap. Based on the difference information, it is determined whether the flight path corresponding to the at least one UAV has been passed; If the flight path corresponding to at least one UAV is passed, the third grid bitmap updates the first grid bitmap, and the third grid bitmap is broadcast to the at least one UAV; the third grid bitmap is used to determine the flight path corresponding to the at least one UAV.

10. A flight path planning device for unmanned aerial vehicles (UAVs), characterized in that, include: The first determining unit is used to determine a first grid bitmap corresponding to at least one UAV flight path planning area; The first grid bitmap includes identification information for at least one grid; The identification information indicates whether the grid will be released after a preset time when it is occupied; The acquisition unit is used to acquire the planning request information of the first UAV when the first UAV needs to adjust its flight path. The first drone is any one of the at least one drones; The second determining unit is used to determine the second grid bitmap corresponding to the at least one UAV route planning area based on the identification information and the planning request information; The judgment unit is used to determine, based on the first grid bitmap and the second grid bitmap, whether the first UAV needs to adjust its flight path and whether it has been passed, and to obtain a judgment result; The third determining unit is used to determine whether the first UAV travels along the adjusted route based on the judgment result.

11. A UAV route planning system, characterized in that, The system includes at least one drone, a network device, and a cloud platform; each drone covers the communications radiated by the network device. The cloud platform is used to determine a first grid bitmap corresponding to at least one UAV flight path planning area; The first grid bitmap includes identification information for at least one grid; the identification information indicates whether the grid will be released after a preset time when it is occupied; in the case that the first UAV needs to adjust its flight path, the planning request information of the first UAV is obtained; The first drone is any one of the at least one drones; Determine the second grid bitmap corresponding to the at least one UAV route planning area based on the identification information and the planning request information; Based on the first grid bitmap and the second grid bitmap, it is determined whether the first UAV needs to adjust its route and whether the route is passed, and a judgment result is obtained; based on the judgment result, it is determined whether the first UAV travels according to the adjusted route.

12. A flight path planning device for unmanned aerial vehicles (UAVs), characterized in that, include: A first processor and a first memory for storing computer programs capable of running on the processor. Wherein, when the first processor is used to run the computer program, it performs the steps of the method according to any one of claims 1 to 9.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

14. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

Images