A silent navigation method for UAV self-seeking based on BMP

Abstract

The present invention discloses a silent navigation method for self-seeking of unmanned aerial vehicles based on BMP, comprising the following steps: dividing a target area into several interrelated blocks, and presetting the block information into the unmanned aerial vehicle system; determining the corresponding destination block and the block where the unmanned aerial vehicle is currently located in the several blocks; presetting the walking logic of the unmanned aerial vehicle from the current block to the destination block, and forming a target path; collecting the position, attitude and sensor data of the unmanned aerial vehicle in real time during flight; dynamically shifting and comparing the block where the unmanned aerial vehicle is currently located with the adjacent blocks to determine the offset, and updating the target path in real time to determine the next target block of the unmanned aerial vehicle flight; the unmanned aerial vehicle continuously repeats the dynamic shifting and comparison, flies along the target path updated in real time, and navigates to the destination. The technical solution disclosed by the present invention can accurately plan the navigation path, realize accurate navigation of the unmanned aerial vehicle in an environment where communication is limited or silence is required, and improve the reliability of navigation and the operation capability of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle navigation technology, and more specifically, to a BMP-based unmanned aerial vehicle self-seeking silent navigation method. Background Art

[0002] A drone is an unmanned aircraft controlled by a radio remote control device and a self-contained program control device. Drones are flexible to operate and highly safe, and can meet the needs of different industries. BMP (Block Map Progress) map block advancement technology.

[0003] With the widespread application of drone technology, traditional navigation methods that rely on real-time communication and external navigation signals face challenges in some special scenarios, such as environments where communication is limited or silence is required. For example, in tasks such as military reconnaissance and monitoring of specific areas, drones need to accurately navigate to the intended destination without satellite signal interference or without exposing their own position and without external navigation assistance. However, in the existing technology, the traditional navigation methods of drones are insufficient in navigation accuracy and reliability in such silent environments and cannot meet actual needs.

[0004] Therefore, how to provide a BMP-based UAV self-seeking silent navigation method that can accurately plan the navigation path, enable the UAV to achieve accurate navigation in an environment where communication is limited or silence is required, and improve the reliability of navigation and the operating capability of the UAV has become a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a BMP-based UAV self-seeking silent navigation method, which can accurately plan the navigation path, enable the UAV to achieve accurate navigation in an environment where communication is restricted or silence is required, and improve the reliability of navigation and the operating capability of the UAV.

[0006] The technical solution provided by the present invention is as follows:

[0007] The present invention provides a silent navigation method for self-seeking of an unmanned aerial vehicle based on BMP, comprising the following steps: S1, presetting spatiotemporal data: dividing a target area into a plurality of interrelated blocks, and presetting information of the plurality of interrelated blocks into an unmanned aerial vehicle system; S2, determining position information: acquiring the position coordinates of the navigation destination and the current unmanned aerial vehicle, and determining the corresponding destination block and the block where the unmanned aerial vehicle is currently located in the plurality of interrelated blocks; S3, presetting walking logic: presetting the walking logic of the unmanned aerial vehicle moving from the current block to the destination block, and forming a target path; S4, flight data collection: the unmanned aerial vehicle flies forward according to the target path, and collects the position, posture and sensor data of the unmanned aerial vehicle in real time during the flight; S5, dynamic shift comparison: performing dynamic shift comparison on the block where the unmanned aerial vehicle is currently located and the adjacent blocks, determining the offset of the current unmanned aerial vehicle relative to the target path, and updating the target path in real time according to the position of the unmanned aerial vehicle and the flight data, and determining the next target block of the unmanned aerial vehicle flight; S6, silent navigation flight: the unmanned aerial vehicle continuously repeats the dynamic shift comparison, and then performs self-seeking flight of the unmanned aerial vehicle along the updated target path, and silently navigates to the destination.

[0008] Furthermore, in a preferred embodiment of the present invention, in step S1, the step of dividing the target area into a plurality of interrelated blocks includes:

[0009] Obtain detailed geographic information of the target area;

[0010] Divide the target area into several blocks according to geographic coordinate range or terrain features;

[0011] The location information of each block, its connection relationship with adjacent blocks, and the landform features and obstacle information within the block are recorded in detail and set as block information.

[0012] Furthermore, in a preferred embodiment of the present invention, in step S1, the preset spatiotemporal data further includes:

[0013] Analyze the impact of environmental changes on navigation in different time periods, and preset the impact information into the UAV system.

[0014] Further, in a preferred embodiment of the present invention, in step S2, the step of determining the block where the drone is currently located includes:

[0015] According to the current position coordinates of the drone , obtain its positional relationship with each block;

[0016] Determine the distance from the drone to the center of each block ;

[0017] ,in For each block The location coordinates of

[0018] The block where the drone is currently located satisfy , that is, select the block with the smallest distance as the current block.

[0019] Furthermore, in a preferred embodiment of the present invention, in step S4, real-time collection of the position, attitude and sensor data of the drone during flight further includes:

[0020] Set the sensor acquisition frequency;

[0021] The acceleration data is collected through the acceleration sensor, and the angular velocity data and attitude angle data are obtained by using the gyroscope and attitude sensor respectively, so as to determine the flight attitude of the drone;

[0022] Air pressure, wind direction, altitude data and visual image data are then collected through environmental sensors.

[0023] Furthermore, in a preferred embodiment of the present invention, after the flight data is collected, the BMP-based UAV self-seeking silent navigation method further includes: establishing a motion model; wherein the step of establishing the motion model includes:

[0024] Perform time integration on the acceleration data to obtain the velocity component of the current drone ;

[0025] Combined with the air pressure, wind direction, and altitude data, analyze the impact of environmental factors on drone flight over time and location, and then establish a correction vector Quantify the impact of environmental factors;

[0026] Based on the velocity components and the correction vector, the UAV motion model is determined:

[0027] .

[0028] Furthermore, in a preferred embodiment of the present invention, before dynamically moving and comparing the block where the drone is currently located with the adjacent blocks in step S5, the method further includes:

[0029] Build the adjacency matrix between blocks ;

[0030] The adjacent relationship expression logic between blocks is determined in the adjacency relationship matrix, wherein Representation Block With Block Adjacent, Indicates non-adjacent;

[0031] According to the adjacency matrix Describe the connection relationship between blocks;

[0032] Then, according to the block where the drone is currently located, the adjacency matrix Find its adjacent block set .

[0033] Furthermore, in a preferred embodiment of the present invention, in step S5, the step of dynamically moving and comparing the block where the drone is currently located with the adjacent blocks includes:

[0034] Analyze the position relationship of the drone relative to adjacent blocks based on the drone's position, attitude data and information about the current block.

[0035] Get the current acceleration direction of the drone, determine the moving direction of the drone, and combine the boundary information of the current block and the position relationship of the adjacent blocks to determine whether the drone is about to enter an adjacent block and which adjacent block it is about to enter;

[0036] At the same time, the position and path direction of the drone are further confirmed by comparing the visual image data in the sensor data with the topographic features of the current block and adjacent blocks.

[0037] Further, in a preferred embodiment of the present invention, in step S6, the UAV flying along the target path updated in real time includes:

[0038] The UAV obtains the target path formed, continuously adjusts the flight attitude and power system to control the flight direction and speed, and performs silent flight.

[0039] Furthermore, in a preferred embodiment of the present invention, in step S6, after navigating to the destination, the process further includes destination confirmation: obtaining information about the block where the drone is currently located and the block where the destination is located, verifying the distance between the block where the drone is currently located and the block where the destination is located, and when the block where the drone is currently located coincides with the block where the destination is located, that is, , it is determined that the drone has reached the destination.

[0040] The present invention provides a BMP-based silent navigation method for unmanned aerial vehicle self-seeking, comprising the following steps: S1, preset spatiotemporal data: divide a target area into a plurality of interrelated blocks, and preset the information of the plurality of interrelated blocks into the unmanned aerial vehicle system; S2, determine position information: obtain the position coordinates of the navigation destination and the current unmanned aerial vehicle, and determine the corresponding destination block and the block where the unmanned aerial vehicle is currently located in the plurality of interrelated blocks; S3, preset walking logic: preset the walking logic of the unmanned aerial vehicle moving from the current block to the destination block, and form a target path; S4, flight data collection: the unmanned aerial vehicle flies forward according to the target path, and collects the position, posture and sensor data of the unmanned aerial vehicle in real time during the flight; S5, dynamic shift comparison: dynamically shift and compare the block where the unmanned aerial vehicle is currently located with the adjacent blocks, determine the offset of the current unmanned aerial vehicle relative to the target path, and update the target path in real time according to the position of the unmanned aerial vehicle and the flight data, and determine the next target block of the unmanned aerial vehicle flight; S6, silent navigation flight: the unmanned aerial vehicle continuously repeats the dynamic shift comparison, and then performs unmanned aerial vehicle self-seeking flight along the updated target path, and silently navigates to the destination. The BMP-based UAV self-seeking silent navigation method provided in the present invention accurately plans the path from the UAV's current position to the destination through the block association and dynamic shift comparison mechanism of BMP, and updates the path in real time through the UAV's flight position and attitude data to ensure that the UAV accurately navigates to the destination, thereby solving the problem in the prior art that it is difficult for UAVs to accurately navigate to the destination when communication is blocked and navigation is silent.In the BMP-based UAV self-seeking silent navigation method, the main process is divided into six steps. First, before the UAV takes off, according to the mission requirements and the geographic information of the target area, high-precision spatiotemporal data are preset in the storage system of the UAV. The spatiotemporal data includes several blocks divided based on the target area, and the navigation path is planned for the UAV through the information of the several blocks; then, according to the current position of the UAV and the navigation destination, the block where the UAV is currently located and the destination block are determined in the several blocks; at the same time, in the UAV take-off preparation stage, the UAV also needs to configure walking logic, and plan the target path from the block where the UAV is currently located to the destination block according to the walking logic; after determining the target path, the UAV takes off and moves forward according to the target path. When the UAV flies forward, its position changes, and the block where the UAV is currently located also changes immediately. The UAV is equipped with various sensors, and then the sensors are used to collect data such as the flight position, posture and environment of the UAV; and in the UAV During the flight, due to communication obstruction or in a silent state, the drone flight will not move accurately according to the target path. Therefore, in the next step, when the drone flies forward, the block association and dynamic shift comparison mechanism of BMP is used, combined with sensor data and several block information, to obtain the position relationship of the drone relative to the adjacent blocks, and the block where the drone is currently located is compared with its adjacent blocks to obtain the offset of the drone relative to the target path at this time. At the same time, the information of the block where the drone is currently located and the destination block is used to re-plan the target path, and the target block that the drone needs to move next is determined based on the updated target path; finally, the drone adjusts the power system, changes the flight direction to move toward the target block, and after the drone enters the target block, repeats the dynamic shift comparison process, obtains a new target path, and determines the next target block that the drone needs to move, so as to repeat the comparison and continuously update the path until the drone reaches the destination and completes its silent navigation flight operation. Therefore, compared with the prior art, the technical solution involved in the present invention can accurately plan the navigation path, so that the drone can achieve accurate navigation in an environment where communication is limited or silence is required, and improve the reliability of navigation and the operation capability of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 The present invention is a flowchart of the steps of the BMP-based UAV self-seeking silent navigation method involved in the embodiment of the present invention;

[0043] Figure 2 A flowchart of the steps of collecting the position, posture and sensor data of the drone in real time during the flight process involved in an embodiment of the present invention;

[0044] Figure 3 A flowchart of the steps of establishing a motion model according to an embodiment of the present invention;

[0045] Figure 4 This is a flow chart of the steps of dynamically moving and comparing the block where the drone is currently located with adjacent blocks according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention.

[0047] It should be noted that when an element is referred to as being "fixed on" or "set on" another element, it can be directly on the other element or indirectly set on the other element; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element.

[0048] It should be understood that the terms "length", "width", "up", "down", "front", "back", "first", "second", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0049] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" and "several" is two or more, unless otherwise clearly and specifically defined.

[0050] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0051] Please Figures 1 to 4 As shown, the present invention provides a silent navigation method for self-seeking of a UAV based on BMP, comprising the following steps: S1, preset spatiotemporal data: divide the target area into a number of interrelated blocks, and preset the information of the several interrelated blocks into the UAV system; S2, determine the position information: obtain the position coordinates of the navigation destination and the current UAV, and determine the corresponding destination block and the block where the UAV is currently located in the several interrelated blocks; S3, preset walking logic: preset the walking logic of the UAV from the current block to the destination block, and form a target path; S4, flight data collection: the UAV flies forward according to the target path, and collects the position, posture and sensor data of the UAV in real time during the flight; S5, dynamic shift comparison: dynamically shift and compare the block where the UAV is currently located with the adjacent blocks, determine the offset of the current UAV relative to the target path, and update the target path in real time according to the position and flight data of the UAV, and determine the next target block of the UAV flight; S6, silent navigation flight: the UAV continuously repeats the dynamic shift comparison, and then performs UAV self-seeking flight along the updated target path, and silently navigates to the destination. The technical solution involved in the present invention can accurately plan the navigation path, so that the UAV can achieve accurate navigation in an environment where communication is limited or silence is required, thereby improving the reliability of navigation and the operating capability of the UAV.

[0052] The following is a specific description of the BMP-based UAV self-seeking silent navigation method disclosed in the present invention in conjunction with a specific embodiment. The BMP-based UAV self-seeking silent navigation method specifically includes the following steps:

[0053] S1. Preset spatiotemporal data: divide the target area into a number of interrelated blocks, and preset the information of the several interrelated blocks into the UAV system.

[0054] Specifically, in a specific embodiment of the present invention, in step S1, presetting spatiotemporal data also includes: analyzing the impact of environmental changes on navigation in different time periods, and presetting the impact information into the UAV system.

[0055] Specifically, step S1 is to preset spatiotemporal data, which is used to provide path planning data for the UAV in a silent state; in this embodiment, the spatiotemporal data is divided into two parts: first, the target area is divided into the several blocks, and the several blocks are formed by a collection Indicates that is the total number of blocks, Indicates blocks, for each block , whose position is determined by the coordinates Determine that the several blocks are used in subsequent steps to correct the offset route and update the navigation path through the BMP block association and dynamic shift comparison mechanism; second, specifically time-related information, including information on the impact of environmental changes occurring in different time periods on navigation.

[0056] Specifically, in a specific embodiment of the present invention, in step S1, the step of dividing the target area into a number of interrelated blocks includes: obtaining detailed geographic information of the target area; dividing the target area into a number of blocks according to the geographic coordinate range or terrain features; recording in detail the location information of each block, the connection relationship with the adjacent blocks, and the landform features and obstacle information within the block, and setting it as block information.

[0057] Among them, in a specific embodiment of the present invention, the detailed geographic information of the target area can be obtained through various methods such as satellite images, terrain mapping, etc.; the location information of the several blocks includes longitude, latitude and altitude; the connection relationship between each block and the adjacent blocks includes: the number of adjacent blocks, and the boundary lines connecting the adjacent blocks.

[0058] Specifically, in a specific embodiment of the present invention, in step S1, presetting spatiotemporal data further includes: encoding the plurality of blocks.

[0059] Specifically, in a specific embodiment of the present invention, the step of encoding a plurality of blocks includes: according to the position information of the plurality of blocks, encoding the longitude and latitude of each block by 32 bits, encoding the elevation of each block by 16 bits, and combining them to form the position codes of the plurality of blocks; according to the attribute characteristics of each block, encoding the plurality of blocks by attributes; combining the position codes with the attribute codes to form a basic point code; determining the basic point range occupied by each of the blocks , and obtain the basic point code of each basic point; determine the code arrangement order, arrange the basic point codes within the range into a sequence, and obtain the basic code of each block; determine the components in each block, encode the components according to the position and attributes, and obtain the relative code of the components relative to the block; combine the basic code of each block with the relative code of its components in the block to form a nested code combination.

[0060] S2. Determine location information: obtain the location coordinates of the navigation destination and the current drone, and determine the corresponding destination block and the block where the drone is currently located in the plurality of interrelated blocks.

[0061] Specifically, in the present method, step S2 is to determine the location information, wherein the location information is the location information of the block where the drone is currently located and the destination block determined based on the current location of the drone and the destination location in the plurality of blocks.

[0062] Specifically, in a specific embodiment of the present invention, in step S2, the step of determining the block where the drone is currently located includes: according to the coordinates of the current position of the drone , obtain its position relationship with each block; determine the distance from the drone to the center of each block ; ,in For each block The location coordinates of the drone; the block where the drone is currently located satisfy , that is, select the block with the smallest distance as the current block.

[0063] Specifically, in the present method, the UAV silent navigation method realizes precise flight through the mutual correlation and dynamic shift comparison mechanism between blocks. Therefore, after the target area is divided into several blocks, it is first necessary to determine the position of the block where the UAV is currently located in the several blocks; in the embodiment of the present invention, the position of the block where the UAV is currently located can be determined by calculating the distance between the UAV and each block. Under normal circumstances, the block with the shortest distance is the block where the UAV is currently located.

[0064] S3. Preset walking logic: preset the walking logic of the drone moving from the current block to the destination block, and form a target path.

[0065] Specifically, in an embodiment of the present invention, step S3 is a preset walking logic, and the target path is determined according to the walking logic; in this method, the target path is determined according to the positional relationship between the block where the drone is currently located and the destination block. Since the position of the drone will change during the flight, the planning of the target path will change with the change of the drone's position.

[0066] Specifically, in a specific embodiment of the present invention, the target path includes: an overall walking path between the blocks, and a local walking path formed by combining the components in each block.

[0067] Specifically, in a specific embodiment of the present invention, in step S3, the walking logic includes: making the drone fly forward along the block closest to the destination as the first logic; making the drone fly forward avoiding obstacles as the second logic; the first logic and the second logic are executed simultaneously, and when an execution conflict occurs between the first logic and the second logic, and the execution conflict satisfies the first logic but does not satisfy the second logic, the second logic is executed first, and the remaining execution conflicts give priority to the first logic.

[0068] S4. Flight data collection: The UAV flies forward according to the target path, and collects the position, attitude and sensor data of the UAV in real time during the flight.

[0069] Specifically, in the present invention, after the UAV forms the target path according to the walking logic and several pieces of information, the UAV take-off preparation stage is completed, and then the UAV takes off and moves forward based on the preset target path. During the process of moving forward, it is necessary to detect the flight data and environmental data of the UAV to determine the flight status and path data of the UAV; therefore, in step S4, flight data collection is performed to obtain the UAV position, posture and sensor information.

[0070] Specifically, in a specific embodiment of the present invention, in step S4, the step of collecting the position, attitude and sensor data of the drone in real time during flight further includes: setting the sensor collection frequency; collecting acceleration data through an acceleration sensor, and using a gyroscope and an attitude sensor to obtain angular velocity data and attitude angle data, respectively, to determine the flight attitude of the drone; and then collecting air pressure, wind direction, altitude data and visual image data through an environmental sensor.

[0071] Among them, in the flight data collection step, the flight data consists of UAV flight parameters and environmental data, the UAV flight parameters include acceleration, angular velocity and attitude angle data, and the environmental data include air pressure, wind direction, altitude and visual image data; the flight attitude of the UAV is determined by the acceleration, angular velocity and attitude angle data, and the flight environment of the UAV in the air can be obtained according to the air pressure, wind direction and altitude data, and the visual image data is collected and used in the subsequent dynamic shift comparison, which is compared with the topographic features of the current block and the adjacent blocks to further determine the position and path direction of the UAV.

[0072] Specifically, in a specific embodiment of the present invention, after the flight data is collected, the BMP-based UAV self-seeking silent navigation method further includes: establishing a motion model; wherein the step of establishing the motion model includes: performing time integration on the acceleration data to obtain the velocity component of the current UAV Combined with the air pressure, wind direction, and altitude data, analyze the impact of environmental factors on drone flight over time and location, and then establish a correction vector Quantify the impact of environmental factors; determine the UAV motion model based on the velocity components and correction vectors:

[0073] .

[0074] Specifically, in a specific embodiment of the present invention, a correction vector is established The steps include: presetting different UAV flight scenarios; in different flight scenarios, obtaining first flight parameters of the UAV silent flight under normal environmental conditions; then based on different weather changes and lighting conditions, obtaining air pressure, wind direction and altitude data, and establishing environmental parameter sets respectively; then in the different flight scenarios, configuring a number of UAVs of different specifications for simulated flight, and configuring the environmental parameter sets to the UAVs; collecting second flight parameters of the UAV silent flight under the influence of environmental parameters, comparing the first flight parameters with the second flight parameters, and obtaining the influence value of the environmental parameters on the UAV flight; then axially splitting the influence value in three-dimensional coordinates, and establishing an influence value change function with the environmental parameters as variables, expressing the influence value change function in vector form, and obtaining the correction vector .

[0075] S5. Dynamic shift comparison: dynamically shift and compare the block where the drone is currently located with the adjacent blocks to determine the offset of the current drone relative to the target path, and update the target path in real time based on the drone position and flight data to determine the next target block for the drone's flight.

[0076] Specifically, in the embodiment of the present invention, step S5 is a dynamic shift comparison, which dynamically shifts and compares the current block where the drone is located with the adjacent blocks according to the current position and attitude information of the drone, combined with the preset spatiotemporal data. Specifically, based on the block association and dynamic shift comparison mechanism of BMP, by analyzing the spatial relationship between the current block and the adjacent blocks such as up, down, left, right, front and back, and the current environmental information acquired by the sensor, including comparing the topographic features in the visual image data with the topography in the map block, the offset of the drone relative to the target path is determined, and a path from the current position to the destination is gradually formed, so as to select the next target block. , and the path update can be done through a path selection function To achieve During this process, the evaluation of each block and the selection of the path are continuously updated to adapt to various changes during flight.

[0077] Specifically, in a specific embodiment of the present invention, before dynamically moving and comparing the block where the drone is currently located with the adjacent blocks in step S5, it also includes: establishing an adjacency relationship matrix between the blocks ; Determine the adjacent relationship expression logic between blocks in the adjacency relationship matrix, where Representation Block With Block Adjacent, Indicates non-adjacent; According to the adjacency matrix Describe the connection relationship between blocks; according to the block where the drone is currently located, through the adjacency matrix Find its adjacent block set .

[0078] Among them, in this method, the adjacency matrix , used to describe the connection relationship between blocks; for example, if the block With Block , Adjacent, then .

[0079] Specifically, in a specific embodiment of the present invention, in step S5, the step of dynamically moving and comparing the block where the drone is currently located with the adjacent blocks includes: analyzing the position relationship of the drone relative to the adjacent blocks based on the position and attitude data of the drone and the information of the current block; obtaining the current acceleration direction of the drone, determining the moving direction of the drone, and judging whether the drone is about to enter the adjacent block and which adjacent block it is entering based on the boundary information of the current block and the position relationship of the adjacent blocks; at the same time, comparing the visual image data in the sensor data with the topographic features of the current block and the adjacent blocks to further confirm the position and path direction of the drone.

[0080] S6. Silent navigation flight: The UAV continuously repeats the dynamic shift comparison, and then performs self-seeking flight along the updated target path, and silently navigates to the destination.

[0081] Specifically, in the specific implementation of the present invention, in step S6, the step of the UAV self-seeking flight includes: the UAV determines the coding screening rules according to the characteristics of the next target block and its components in the target path; according to the screening rules, the basic codes of each of the blocks are compared to locate the next target block and the components in the next target block; the coding nested combination of the next target block is obtained, and the coding nested combination is decoded; the UAV self-seeks the position and performs navigation flight according to the decoding information.

[0082] Specifically, in a specific embodiment of the present invention, the decoding operation step includes: determining the complexity of the drone self-seeking algorithm based on BMP ; According to the nested combination of the codes of the next target block, parse the code sequence to obtain the relative coding information of the basic point code and the target block components; obtain the number of basic point codes in the next target block , and determine its decoding complexity ; Get the number of components in the next target block , and obtain the decoding complexity of a single component ; Comprehensively analyze the complexity to obtain the overall complexity of the decoding operation ; According to the position and attribute characteristics of the components in the target path, filter out the basic point codes and relative codes that do not meet the requirements, and determine the exclusion ratio respectively , , and optimize the overall complexity of the decoding operation ; Then, according to the overall complexity, decode the position code and attribute code in the nested combination of codes to obtain decoding information.

[0083] Specifically, in a specific embodiment of the present invention, in step S6, the silent navigation flight also includes: the UAV obtains the formed target path, and continuously adjusts the flight attitude and power system to control the flight direction and speed.

[0084] Specifically, in a specific embodiment of the present invention, the step of adjusting the flight attitude and power system to control the flight direction and speed includes: determining the position relationship of the next target block relative to the block where the drone is currently located; adjusting the flight attitude angle of the drone and turning the drone based on the position relationship; and then adjusting the speed component of the drone based on the motion model and power system to move the drone toward the next target block.

[0085] Specifically, in a specific embodiment of the present invention, in step S6, after navigating to the destination, the process also includes destination confirmation: obtaining information about the block where the drone is currently located and the block where the destination is located, verifying the distance between the block where the drone is currently located and the block where the destination is located, and when the block where the drone is currently located coincides with the block where the destination is located, that is, , it is determined that the drone has reached the destination.

[0086] To sum up, the embodiment of the present invention involves a BMP-based UAV self-seeking silent navigation method, which presets high-precision spatiotemporal data, uses its own flight attitude and sensor data to perform dynamic shifting and comparison to form a path, and realizes the function of accurately navigating the UAV to the destination when communication and navigation are silent, so as to solve the problem in the prior art that UAVs are difficult to accurately navigate to the destination when communication and navigation are silent. In the BMP-based UAV self-seeking silent navigation method, the main process is divided into six steps. First, before the UAV takes off, according to the mission requirements and the geographic information of the target area, high-precision spatiotemporal data are preset in the storage system of the UAV. The spatiotemporal data includes several blocks divided based on the target area, and the navigation path is planned for the UAV through the information of the several blocks; then, according to the current position of the UAV and the navigation destination, the block where the UAV is currently located and the destination block are determined in the several blocks; at the same time, in the UAV take-off preparation stage, the UAV also needs to configure walking logic, and plan the target path from the block where the UAV is currently located to the destination block according to the walking logic; after determining the target path, the UAV takes off and moves forward according to the target path. When the UAV flies forward, its position changes, and the block where the UAV is currently located also changes immediately. The UAV is equipped with various sensors, and then the sensors are used to collect data such as the flight position, posture and environment of the UAV; and in the UAV During the flight, due to communication obstruction or in a silent state, the drone flight will not move accurately according to the target path. Therefore, in the next step, when the drone flies forward, the block association and dynamic shift comparison mechanism of BMP is used, combined with sensor data and several block information, to obtain the position relationship of the drone relative to the adjacent blocks, and the block where the drone is currently located is compared with its adjacent blocks to obtain the offset of the drone relative to the target path at this time. At the same time, the information of the block where the drone is currently located and the destination block is used to re-plan the target path, and the target block that the drone needs to move next is determined based on the updated target path; finally, the drone adjusts the power system, changes the flight direction to move toward the target block, and after the drone enters the target block, repeats the dynamic shift comparison process, obtains a new target path, and determines the next target block that the drone needs to move, so as to repeat the comparison and continuously update the path until the drone reaches the destination and completes its silent navigation flight operation. Therefore, compared with the prior art, the technical solution involved in the present invention can accurately plan the navigation path, so that the drone can achieve accurate navigation in an environment where communication is limited or silence is required, and improve the reliability of navigation and the operation capability of the drone.

[0087] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A BMP-based silent navigation method for unmanned aerial vehicle self-seeking, characterized in that: The following steps are involved: S1. Presetting spatiotemporal data: dividing the target area into a number of interrelated blocks, and presetting the information of the several interrelated blocks into the UAV system; S2. Determine location information: obtain the location coordinates of the navigation destination and the current drone, and determine the corresponding destination block and the block where the drone is currently located in the plurality of interrelated blocks; S3, preset walking logic: preset the walking logic of the drone moving from the current block to the destination block and form a target path; S4, flight data collection: the UAV flies forward according to the target path, and collects the position, attitude and sensor data of the UAV in real time during the flight; S5, dynamic shift comparison: dynamically shift and compare the block where the drone is currently located with the adjacent blocks to determine the offset of the current drone relative to the target path, and update the target path in real time according to the drone position and flight data to determine the next target block for the drone to fly; wherein, the step of dynamically shifting and comparing the block where the drone is currently located with the adjacent blocks includes: analyzing the position relationship of the drone relative to the adjacent blocks according to the position and attitude data of the drone and the information of the current block; obtaining the current acceleration direction of the drone to determine the moving direction of the drone, and judging whether the drone is about to enter the adjacent block in combination with the boundary information of the current block and the position relationship of the adjacent blocks; comparing the visual image data in the sensor data with the topographic features of the current block and the adjacent blocks, and confirming the position and path direction of the drone; S6. Silent navigation flight: The UAV continuously repeats the dynamic shift comparison, flies along the target path updated in real time, and navigates to the destination.

2. The BMP-based silent navigation method for unmanned aerial vehicle self-seeking according to claim 1 is characterized in that: In step S1, the step of dividing the target area into a number of interrelated blocks includes: Obtain detailed geographic information of the target area; Divide the target area into several blocks according to geographic coordinate range or terrain features; The location information of each block, its connection relationship with adjacent blocks, and the landform features and obstacle information within the block are recorded in detail and set as block information.

3. The BMP-based silent navigation method for UAV self-seeking according to claim 2 is characterized in that: In step S1, the preset spatiotemporal data also includes: Analyze the impact of environmental changes on navigation in different time periods, and preset the impact information into the UAV system.

4. The BMP-based silent navigation method for UAV self-seeking according to claim 1, characterized in that: In step S2, the step of determining the block where the drone is currently located includes: According to the current position coordinates of the drone , obtain its positional relationship with each block; Determine the distance from the drone to the center of each block ; ,in For each block The location coordinates of The block where the drone is currently located satisfy , that is, select the block with the smallest distance as the current block.

5. The BMP-based silent navigation method for UAV self-seeking according to claim 1, characterized in that: In step S4, real-time collection of the position, attitude and sensor data of the drone during flight includes the following steps: Set the sensor acquisition frequency; Collect acceleration data through an acceleration sensor; Obtain angular velocity data and attitude angle data through gyroscopes and attitude sensors; Air pressure, wind direction, altitude data and visual image data are collected through environmental sensors.

6. The BMP-based silent navigation method for UAV self-seeking according to claim 5, characterized in that: After the flight data is collected, the BMP-based UAV self-seeking silent navigation method further includes: establishing a motion model; wherein the step of establishing the motion model includes: Perform time integration on the acceleration data to obtain the velocity component of the current drone ; Combined with the air pressure, wind direction, and altitude data, analyze the impact of environmental factors on drone flight over time and location, and then establish a correction vector Quantify the impact of environmental factors; Based on the velocity components and the correction vector, the UAV motion model is determined: 。 7. The BMP-based silent navigation method for UAV self-seeking according to any one of claims 1 to 6, characterized in that: In step S5, before dynamically moving and comparing the block where the drone is currently located with the adjacent blocks, the following steps are also included: Build the adjacency matrix between blocks ; The adjacent relationship expression logic between blocks is determined in the adjacency relationship matrix, wherein Representation Block With Block Adjacent, Indicates non-adjacent; According to the adjacency matrix Describe the connection relationship between blocks; According to the block where the drone is currently located, the adjacency matrix Find its adjacent block set .

8. The BMP-based silent navigation method for UAV self-seeking according to claim 4, characterized in that: In step S6, the UAV flies along the target path updated in real time including: The UAV obtains the target path formed, and continuously adjusts the flight attitude and power system to control the flight direction and speed for silent flight.

9. The BMP-based silent navigation method for UAV self-seeking according to claim 8, characterized in that: In step S6, after navigating to the destination, the process also includes: destination confirmation: obtaining information about the block where the drone is currently located and the block where the destination is located, verifying the distance between the block where the drone is currently located and the block where the destination is located, and when the block where the drone is currently located coincides with the block where the destination is located, that is, , it is determined that the drone has reached the destination.

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