CN-121977574-A - Unmanned aerial vehicle self-adaptive navigation method and system based on general sense integrated signal quality

CN121977574ACN 121977574 ACN121977574 ACN 121977574ACN-121977574-A

Abstract

An unmanned aerial vehicle self-adaptive navigation method and system based on general sense integrated signal quality, the method firstly obtains a three-dimensional geographic coordinate system and constructs a three-dimensional voxel grid; then, acquiring communication indexes and radar perception echo data, and constructing a local physical environment model according to the communication indexes and the radar perception echo data to generate an environment perception correction coefficient; based on the environment perception correction coefficient and the communication index, calculating the grid connection reliability score and carrying out weight dynamic correction; then, generating a low-altitude multidimensional digital map based on the grid connection reliability score; finally, based on the low-altitude multidimensional digital map, implementing an unmanned aerial vehicle self-adaptive navigation strategy; according to the invention, by means of the active sensing capability of the base station, the physical characteristics of the low-altitude environment are inverted through radar echo, a prediction mechanism with mutual assistance of sense of general assistance is constructed, and the no-fly zone is identified in advance from the signal propagation dimension, so that the unmanned aerial vehicle path planning stage is guided to actively avoid the signal blind zone, the problems of communication interruption and navigation disordering in the complex environment are solved, and the safety and reliability of low-altitude flight are improved.

Inventors

LIU ZIPING
YE FEI
LEI BO
CAO LINGZHI
CHEN SHUAILIN
LUO MING
LI ZIYU
YU HAODONG

Assignees

湖南省测绘科技研究所
长沙眸瑞空间智能科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260228

Claims (10)

1. An unmanned aerial vehicle self-adaptive navigation method based on general sense integrated signal quality is characterized by comprising the following steps: acquiring a three-dimensional geographic coordinate system of a low-altitude airspace, and constructing a three-dimensional voxel grid; Acquiring downlink communication indexes and radar perceived echo data of each grid region in a three-dimensional voxel grid, wherein the communication indexes comprise signal-to-noise ratio and reference signal receiving power; constructing a local physical environment model based on radar perception echo data, and generating an environment perception correction coefficient based on the local physical environment model; Calculating grid connection reliability scores and carrying out weight dynamic correction based on the environment perception correction coefficients and the communication indexes; based on the grid connection reliability score, carrying out signal intensity division on the three-dimensional voxel grid to generate a low-altitude multidimensional digital map; Based on the low-altitude multidimensional digital map, the unmanned aerial vehicle self-adaptive navigation strategy is implemented.
2. The unmanned aerial vehicle adaptive navigation method based on the sense-of-general integrated signal quality according to claim 1, wherein: The construction of the local physical environment model based on radar perception echo data specifically comprises the following steps: transmitting an integrated waveform to each grid area based on the communication perception integrated base station, receiving radar scattering cross-sectional areas and analyzing to obtain sparse point cloud echo data; Denoising the sparse point cloud echo data, identifying a main obstacle cluster, and performing plane equation fitting on the main obstacle cluster to obtain a local physical environment model, wherein the expression of the plane equation fitting is as follows: ; Wherein: Coordinates of any point on the plane; Is a three-dimensional vector, and represents the normal direction of the surface of the obstacle in the three-dimensional space; Is a scalar quantity, representing the offset distance of the obstacle surface relative to the origin of the spatial coordinates in the normal direction.
3. The unmanned aerial vehicle adaptive navigation method based on the sense-of-general integrated signal quality according to claim 2, wherein: The generating the environment perception correction coefficient based on the local physical environment model specifically comprises the following steps: communication perception integrated base station antenna position is used as virtual ray starting point Taking the center of the grid area as a target point Constructing a virtual ray equation, and defining a bounding box formed by point cloud extreme points of an obstacle cluster as a physical boundary range of the obstacle, wherein the expression of the virtual ray equation is as follows: ; Wherein: Is an intersection point parameter; a direction vector pointing from the communication perception integrated base station to the center of the grid area is represented; substituting the virtual ray equation into the plane equation to calculate the intersection point parameter The expression is as follows: ; Wherein: Fitting a normal vector to the plane equation; Is a constant term; Is a position vector of any point on a plane; if the intersection point parameters of the virtual ray and the plane equation Satisfy the following requirements And the coordinates of the intersection point If the grid is positioned in the bounding box, judging that the grid is positioned in a signal shadow area caused by physical shielding; for the grid in the signal shadow region, the electromagnetic wave diffraction loss is calculated based on the blade diffraction model, and the expression is as follows: ; Wherein: is electromagnetic wave diffraction loss; is a Fresnel diffraction parameter; a kernel function that is a fresnel integral; Based on the electromagnetic wave diffraction loss and Doppler frequency shift, calculating and generating an environment perception correction coefficient, wherein the expression is as follows: ; Wherein: the correction coefficient is the environmental perception; 、 are normalized weight factors; Is a signal loss tolerance threshold; is the Doppler shift variance; is the maximum doppler shift variance.
4. The unmanned aerial vehicle adaptive navigation method based on the sense-of-general integrated signal quality according to claim 3, wherein: Based on the environment perception correction coefficient and the communication index, calculating the grid connection reliability score and carrying out weight dynamic correction, and specifically comprising the following steps: based on the environmental perception correction coefficient, the signal-to-noise ratio and the reference signal received power, calculating a grid connection reliability score, wherein the expression is as follows: ; Wherein: Scoring the reliability of the grid connection; Is the signal to noise ratio; Receiving power for a reference signal; 、 all are communication index weight distribution coefficients; is a theoretical communication weight; is an environmental perception weight; Based on real measurement communication comprehensive indexes fed back in real time by the unmanned aerial vehicle in the flight process, calculating a deviation value of the reliability score of the grid connection, wherein the expression is as follows: ; ; Wherein: Is a deviation value; The comprehensive communication index is actually measured; Signal to noise ratio is measured for reality; Receiving power for a true measurement reference signal; Is a signal-to-noise ratio reference value; receiving a power reference value for a reference signal; If it is If the deviation exceeds the preset deviation threshold, automatically triggering feedback compensation to dynamically correct the weight, and reducing the theoretical communication weight Synchronously increasing environmental awareness weights Up to And (3) with And tend to be consistent.
5. The unmanned aerial vehicle adaptive navigation method based on the sense-of-general integrated signal quality according to claim 4, wherein: The generation of the low-altitude multidimensional digital map specifically comprises the following steps: Dividing the three-dimensional voxel grid into a strong connection grid, a weak connection grid and a blind area grid based on the grid connection reliability score and a preset score threshold interval: If it is The method is characterized in that the signal quality is higher and the signal is divided into strong connection grids; If it is The signal quality is expressed as fluctuation and divided into weak connection grids; If it is The signal quality is poor and the blind area grids are divided; And marking the strong connection grids, the weak connection grids and the blind area grids respectively by adopting different colors to form a low-altitude multidimensional digital map.
6. The unmanned aerial vehicle adaptive navigation method based on the sense-of-general integrated signal quality according to claim 5, wherein: Based on the low-altitude multidimensional digital map, implementing the unmanned aerial vehicle self-adaptive navigation strategy specifically comprises the following steps: the unmanned aerial vehicle loads a low-altitude multidimensional digital map, and matches the current position with grid attributes in real time to carry out path planning: If the current and to-be-entered target grid is a strong connection grid, a strong network connection mode is adopted to lock a 5G/6G communication link, and real-time dynamic carrier phase differential positioning and real-time image transmission are adopted; If the target grid to be entered is a weak connection grid, a weak network connection mode is adopted to generate an early warning trigger signal, the image transmission rate is automatically reduced, and an onboard vision odometer or a laser radar is activated to perform positioning compensation; if a blind area grid exists in front of the planned path, adopting a blind area obstacle avoidance mode, regarding the blind area grid as an unvented entity obstacle, and recalculating the detour track.
7. The unmanned aerial vehicle adaptive navigation method based on the sense-of-general integrated signal quality according to claim 6, wherein: the unmanned aerial vehicle self-adaptive navigation strategy also comprises a high-rise canyon traversing mode, and specifically comprises the following steps: If the planned path needs to traverse the high building or the canyon gap, calculating an effective communication time window based on the local physical environment model, wherein the expression is as follows: ; Wherein: Is an effective communication time window; Is a gap between tall buildings or canyons; The current flight speed of the unmanned aerial vehicle is; if a communication time window is valid And if the signal handshake time delay is smaller than the preset signal handshake time delay, switching to a connectionless transmission protocol, and preloading key navigation data before entering a high-rise or canyon gap.
8. An unmanned aerial vehicle adaptive navigation system based on a sense-of-general integrated signal quality, characterized in that the system is applied to the method of any one of claims 1-7, said system comprising: The three-dimensional voxel grid construction module (1) is used for acquiring a three-dimensional geographic coordinate system of a low-altitude airspace and constructing a three-dimensional voxel grid; The communication and radar data acquisition module (2) is used for acquiring downlink communication indexes and radar perceived echo data of each grid region in the three-dimensional voxel grid, wherein the communication indexes comprise signal to noise ratio and reference signal receiving power; the correction coefficient generation module (3) is used for constructing a local physical environment model based on radar perceived echo data and generating an environment perceived correction coefficient based on the local physical environment model; the reliability scoring module (4) is used for calculating the grid connection reliability score and carrying out weight dynamic correction based on the environment perception correction coefficient and the communication index; The digital map generation module (5) is used for dividing the signal intensity of the three-dimensional voxel grid based on the grid connection reliability score to generate a low-altitude multidimensional digital map; And the navigation strategy implementation module (6) is used for implementing the unmanned aerial vehicle self-adaptive navigation strategy based on the low-altitude multidimensional digital map.
9. Unmanned aerial vehicle self-adaptation navigation equipment based on lead to sense integration signal quality, its characterized in that: The device comprises a processor (7) and a memory (8); -said memory (8) is adapted to store computer program code (81) and to transmit said computer program code (81) to said processor (7); The processor (7) is configured to perform the unmanned aerial vehicle adaptive navigation method based on the integrated signal quality of any of claims 1-7 according to instructions in the computer program code (81).
10. A computer-readable storage medium, characterized by: The computer-readable storage medium has stored therein computer-executable instructions that, when executed on a computer, implement the method for adaptive navigation of unmanned aerial vehicle based on integrated signal quality of any one of claims 1-7.

Description

Unmanned aerial vehicle self-adaptive navigation method and system based on general sense integrated signal quality Technical Field The invention relates to an unmanned aerial vehicle navigation means, belongs to the technical field of wireless communication, and particularly relates to an unmanned aerial vehicle self-adaptive navigation method and system based on universal integrated signal quality. Background Along with the rapid development of low-altitude economy, unmanned aerial vehicles are increasingly popular in urban logistics, security inspection and other scenes. However, the urban low-altitude environment is extremely complex, and signal shielding (shadow effect) and multipath reflection caused by high-rise forestation cause that the traditional two-dimensional plane-based cellular network coverage is difficult to meet the three-dimensional flight requirement of the unmanned aerial vehicle. The conventional physical obstacle avoidance solves the problem that an unmanned aerial vehicle cannot be guided to actively avoid a signal blind area in a path planning layer due to collision risk between the unmanned aerial vehicle and an entity obstacle, the risk of communication runaway occurs before the physical collision risk occurs due to the fact that the entity obstacle shields the signal, the conventional network coverage map only provides RSRP (received power) reference, lacks multi-dimensional evaluation on connection stability, cannot provide refined avoidance or mode switching guidance for the unmanned aerial vehicle, cannot solve the problem of communication interruption and navigation of the unmanned aerial vehicle in a complex environment, and has a certain potential safety hazard. Therefore, a safe and reliable means is needed to solve the above-mentioned drawbacks in the prior art. Disclosure of Invention The invention aims to overcome the defects and problems in the prior art and provide a safe and reliable unmanned aerial vehicle self-adaptive navigation method and system based on the sense-of-general integrated signal quality. In order to achieve the purpose, the technical scheme of the invention is that the unmanned aerial vehicle self-adaptive navigation method based on the sense-of-general integrated signal quality comprises the following steps: acquiring a three-dimensional geographic coordinate system of a low-altitude airspace, and constructing a three-dimensional voxel grid; Acquiring downlink communication indexes and radar perceived echo data of each grid region in a three-dimensional voxel grid, wherein the communication indexes comprise signal-to-noise ratio and reference signal receiving power; constructing a local physical environment model based on radar perception echo data, and generating an environment perception correction coefficient based on the local physical environment model; Calculating grid connection reliability scores and carrying out weight dynamic correction based on the environment perception correction coefficients and the communication indexes; based on the grid connection reliability score, carrying out signal intensity division on the three-dimensional voxel grid to generate a low-altitude multidimensional digital map; Based on the low-altitude multidimensional digital map, the unmanned aerial vehicle self-adaptive navigation strategy is implemented. Optionally, the constructing a local physical environment model based on the radar perceived echo data specifically includes: transmitting an integrated waveform to each grid area based on the communication perception integrated base station, receiving radar scattering cross-sectional areas and analyzing to obtain sparse point cloud echo data; Denoising the sparse point cloud echo data, identifying a main obstacle cluster, and performing plane equation fitting on the main obstacle cluster to obtain a local physical environment model, wherein the expression of the plane equation fitting is as follows: ; Wherein: Coordinates of any point on the plane; Is a three-dimensional vector, and represents the normal direction of the surface of the obstacle in the three-dimensional space; Is a scalar quantity, representing the offset distance of the obstacle surface relative to the origin of the spatial coordinates in the normal direction. Optionally, the generating the environmental perception correction coefficient based on the local physical environment model specifically includes: communication perception integrated base station antenna position is used as virtual ray starting point Taking the center of the grid area as a target pointConstructing a virtual ray equation, and defining a bounding box formed by point cloud extreme points of an obstacle cluster as a physical boundary range of the obstacle, wherein the expression of the virtual ray equation is as follows: ; Wherein: Is an intersection point parameter; a direction vector pointing from the communication perception integrated base station to the center