CN119001800B - A beacon system and navigation method for unmanned aerial vehicle navigation - Google Patents
A beacon system and navigation method for unmanned aerial vehicle navigation Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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Abstract
The invention discloses a beacon system and a navigation method for unmanned aerial vehicle navigation, and relates to the technical field of unmanned aerial vehicle navigation. The method comprises the steps of collecting performance parameters generated by a beacon node nearest to an unmanned aerial vehicle based on an initial position, calculating a beacon node setting distance according to the performance parameters in a target area where the unmanned aerial vehicle flies, arranging a plurality of beacon nodes on the ground in an equidistant mode, monitoring signal strength received by the beacon nodes according to the performance parameters and signal loss, comparing the received signal strength with a signal strength threshold, recording the current beacon node position when the received signal strength is smaller than the signal strength threshold, detecting an obstacle position, adjusting the beacon node to the target position according to the obstacle position, collecting unmanned aerial vehicle position information through the beacon node of the target position, selecting a path through a particle swarm optimization algorithm according to the current unmanned aerial vehicle position information, and enabling the unmanned aerial vehicle to reach a destination according to the selected optimal path.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle navigation, in particular to a beacon system for unmanned aerial vehicle navigation and a navigation method.
Background
Unmanned aircraft, simply referred to as "Unmanned AERIAL VEHICLE, UAV," is an aircraft that does not require on-board personnel, typically controlled by a remote control, a host computer, or preprogrammed instructions. At present, unmanned aerial vehicles in the front of industries can fly freely in the air in the civil and military fields, and generally have the advantages of low cost, easy deployment, flexible movement, wide field of view and the like. This makes unmanned aerial vehicles have advantages over other intelligent robots, both in complex harsh environments where humans are difficult to reach and in standardized, modular urban environments.
Unmanned aerial vehicle technology presents great potential in a plurality of fields such as agriculture, traffic, electric power, logistics and the like. By executing the tasks of pesticide spraying, air monitoring, power transmission line inspection, terminal distribution and the like, the operation efficiency is improved, the risk is reduced, and key data are provided for geological exploration and the like. With the continuous progress of technology, unmanned aerial vehicles are becoming more and more intelligent, and researchers are working to drive them towards higher, more distant targets.
In the current unmanned aerial vehicle navigation field, a traditional positioning system such as a GPS often faces the problem of weak signals or failure in an indoor or urban dense environment, which limits the application scenes of unmanned aerial vehicles. The unmanned aerial vehicle is caused to face the technical problems of inaccurate path planning, unreliable navigation and the like when the unmanned aerial vehicle executes tasks. Especially in complex environments such as large buildings, underground facilities or post-disaster rescue areas, it is therefore of great importance to develop a more reliable beacon-based navigation system.
In the prior art, publication number CN117908050B discloses a beacon system for unmanned aerial vehicle navigation and a navigation method, the beacon system for unmanned aerial vehicle navigation comprising a distance beacon, an azimuth beacon and a platform wireless receiver is arranged, and meanwhile, the corresponding navigation method is matched, so that local positioning and navigation can be provided or the beacon system can be fused with a degraded GNSS signal to provide positioning and navigation functions when the GNSS signal is bad or unavailable, position data of the beacon is transmitted to the platform wireless receiver through a radio signal, and the platform receives the signal and estimates the position of the beacon system by using the strength of the signal. However, this approach, where the beacon location is fixed, may not be effective against environmental changes, such as obstructions or signal interference, resulting in insufficient signal coverage. In a dynamic environment, the poor flexibility of the fixed beacon may not adapt to changes, influence navigation accuracy, and cannot meet the accuracy requirement of a user.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a beacon system and a navigation method for unmanned aerial vehicle navigation, which are used for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a beacon system for unmanned aerial vehicle navigation, comprising:
The performance parameter acquisition module is used for acquiring performance parameters generated by the beacon node nearest to the initial position of the unmanned aerial vehicle, wherein the performance parameters comprise signal transmitting power, transmitting and receiving antenna gain and signal loss;
The beacon node setting module is used for calculating the beacon node setting interval according to the performance parameters in a target area where the unmanned aerial vehicle flies, arranging a plurality of beacon nodes on the ground based on the calculated beacon node setting interval to form a distributed beacon network, wherein the beacon nodes can dynamically move;
the dynamic movement detection module is used for monitoring the signal intensity received by the beacon node according to the performance parameters and the signal loss, comparing the received signal intensity with a signal intensity threshold value, recording the current beacon node position when the received signal intensity is smaller than the signal intensity threshold value, detecting the obstacle position, and adjusting the beacon node to the target position according to the obstacle position;
The navigation path planning module is used for acquiring the position information of the unmanned aerial vehicle through the beacon node of the target position, selecting a path through a particle swarm optimization algorithm according to the current position information of the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to reach a destination according to the selected optimal path.
Further, performance parameters are calibrated, and the signal transmitting power isGain of transmitting and receiving antennaAndThe signal loss isArranging a plurality of beacon nodes on the ground in an equidistant mode, wherein the equidistant mode means that the distances between the adjacent beacon nodes are allThe formula according to which the beacon interval is calculated is:
In the formula, In order to set the spacing of the beacons,As a function of the wavelength of the signal,Is the signal loss.
Further, the beacon node is provided with a radio transmitter, a laser transmitter, an ultrasonic sensor and a GPS module, and is fixed on a ground robot, and the beacon node is driven to dynamically move by the ground robot.
Further, the formula according to which the signal strength received by the beacon node is detected is:
In the formula, For the received signal strength of the beacon,Representing the extra loss caused by the obstacle,AndRespectively the signal transmitting powerTransmit and receive antenna gainAndSignal path lossAnd extra lossWhereinAndAre all greater than 0 and。
Further, the received signal strength is compared with a signal strength threshold value, and the logic on which whether the beacon node position movement is required is judged according to different comparison results is that the signal strength threshold value is calibrated to be;
When (when)When the beacon node and the unmanned aerial vehicle are in the navigation state, the beacon node does not need to move, and the beacon node does not need to influence the navigation task of the unmanned aerial vehicle;
When (when) When the beacon node and the unmanned aerial vehicle are in the navigation state, the interference of the obstacle existing between the beacon node and the unmanned aerial vehicle on the signal intensity can influence the navigation task of the beacon node on the unmanned aerial vehicle, and the position of the beacon node needs to be changed.
The method for moving the beacon node is based on the fact that the beacon node measures that the position coordinates of the unmanned aerial vehicle are measured to be the following through the laser transmitter and the GPS moduleThe distance between the beacon node and the obstacle is detected through the ultrasonic sensor, and the position coordinates of the obstacle are obtained by combining a GPS moduleRecording the position coordinates of the current beacon node asThe new target position adjustment is based on the following formula:
In the formula, Representing the coordinates of the new target location,Representing the coordinates of the estimated location,The obstacle avoidance distance is indicated by the method,Is a unit vector pointing to an obstacle;
in which the unit vector points to the obstacle The formula on which the calculation is based is:
Estimating position coordinates The formula on which the calculation is based is:
In the formula, The unit vector from the beacon to the unmanned aerial vehicle is expressed as:
Wherein, Indicating the recommended distance between the beacon and the drone.
Further, selecting a path through a particle swarm optimization algorithm, defining a path of a particle representing the current position of an unmanned aerial vehicle to reach a destination, generating the path, taking a beacon node as an intermediate destination, selecting a next beacon node based on the current intermediate destination until reaching the beacon node of a final destination, recording the intermediate destination of the path, and obtaining a target path consisting of a plurality of intermediate destinations, wherein a fitness function is defined to evaluate the path quality, and the expression of the fitness function is as follows:
In the formula, Represent the firstThe fitness value corresponding to each particle is set,Represent the firstEach particle corresponds to the total length of the path,Represent the firstThe individual particles correspond to the estimated time of flight of the path,、The weight coefficients of the total length of the path and the estimated flight time of the path are respectively.
The invention also provides a navigation method for the unmanned aerial vehicle, wherein the navigation method for the unmanned aerial vehicle is used for controlling the beacon system for unmanned aerial vehicle navigation, and the specific steps comprise:
collecting performance parameters generated by a beacon node nearest to the initial position of the unmanned aerial vehicle, wherein the performance parameters comprise signal transmitting power, transmitting and receiving antenna gains and signal loss;
in a target area where the unmanned aerial vehicle flies, calculating the beacon node setting interval according to the performance parameter, and arranging a plurality of beacon nodes on the ground based on the calculated beacon node setting interval to form a distributed beacon network, wherein the beacon nodes can dynamically move;
Monitoring the signal intensity received by the beacon node according to the performance parameters and the signal loss, comparing the received signal intensity with a signal intensity threshold, recording the current beacon node position when the received signal intensity is smaller than the signal intensity threshold, detecting the obstacle position, and adjusting the beacon node to the target position according to the obstacle position;
And acquiring the position information of the unmanned aerial vehicle through the beacon node of the target position, selecting a path through a particle swarm optimization algorithm according to the current position information of the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to reach a destination according to the selected optimal path.
Compared with the prior art, the invention has the beneficial effects that:
Through arranging a plurality of beacon nodes on the ground in the target area of unmanned aerial vehicle flight according to equidistant mode, form a distributed beacon network, through arranging the beacon in the target area equidistance, can ensure the homogeneity that the signal covered, reduce the blind area, and then improve positioning accuracy. And the performance parameters generated by the beacon node closest to the initial position of the unmanned aerial vehicle are collected, the signal intensity received by the beacon node is monitored according to the performance parameters, and the performance parameter monitoring of the beacon node provides real-time feedback for the system, so that the signal coverage strategy can be corrected and adjusted, and the stability and reliability of the unmanned aerial vehicle in flight are ensured. Comparing the received signal strength with a signal strength threshold, recording the current beacon node when the received signal strength is smaller than the signal strength threshold, detecting the position of the obstacle, adjusting the beacon node to the target position according to the position of the obstacle, and judging that the obstacle possibly exists when the beacon node detects that the signal strength is lower than the threshold, recording the position of the obstacle in time and adjusting the position of the beacon. This adaptive capability enables the beacon network to maintain efficient signaling in a dynamic environment. And acquiring the position information of the unmanned aerial vehicle through the beacon node of the target position, selecting a path through a particle swarm optimization algorithm according to the current position information of the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to reach a destination according to the selected optimal path. Meanwhile, the particle swarm optimization algorithm is utilized to select the path of the unmanned aerial vehicle, so that rapid and effective path planning can be realized, a flight route is optimized, and flight efficiency is improved.
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FIG. 1 is a schematic diagram of the overall system architecture of the present invention;
FIG. 2 is a schematic flow chart of the whole method of the invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "up", "down", "left", "right" and the like are used only to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed accordingly.
Referring to fig. 1, the present invention provides a system structure:
a beacon system for unmanned aerial vehicle navigation, the specific structure comprises:
The performance parameter acquisition module is used for acquiring performance parameters generated by the beacon node nearest to the initial position of the unmanned aerial vehicle, wherein the performance parameters comprise signal transmitting power, transmitting and receiving antenna gain and signal loss;
Wherein the transmission power The acquisition method is that the beacon can be acquired through the technical specification of the beacon or actually measured in experiments. In practical applications, a standard transmitting power value is usually selected, the gain of the signal transmitting antennaExpressed generally in terms of dimensionless ratios, can be obtained from the technical document of the antenna, the gain of the signal transmitting antenna being calculated according to the formula:
Wherein, Is the decibel value of the gain;
gain of signal receiving antenna Likewise, the method for converting the technical specification into the linear value can be obtained through the technical specification of the receiving antenna, and the method is the same as the transmitting gain;
wavelength of signal Typically with the frequency of the signalCorrelation, in meters (m), wavelength can be calculated from frequency;
signal path loss Is the loss encountered by the signal in the propagation process, and the acquisition method comprises the steps of calculating by measuring the signal strength in the actual environment, referring to the existing literature or research data, especially the signal loss data in the specific environment, and calculating according to the propagation model (such as a free space loss model, an urban environment model and the like).
The beacon node setting module is used for calculating the beacon node setting interval according to the performance parameters in a target area where the unmanned aerial vehicle flies, arranging a plurality of beacon nodes on the ground based on the calculated beacon node setting interval to form a distributed beacon network, wherein the beacon nodes can dynamically move;
Calibrating performance parameters, the signal transmitting power is Gain of transmitting and receiving antennaAndThe signal loss isArranging a plurality of beacon nodes on the ground in an equidistant mode, wherein the equidistant mode means that the distances between the adjacent beacon nodes are allThe formula according to which the beacon interval is calculated is:
In the formula, In order to set the spacing of the beacons,As a function of the wavelength of the signal,Is the signal loss.
Spacing of beaconsAnd transmit powerTransmit and receive antenna gainAndWavelength of signalProportional, i.e. when the beacon generates a transmit powerTransmit and receive antenna gainAndWavelength of signalThe larger the signal coverage, the larger the spacing between the beacons may be, and inversely proportional to the signal path loss, i.e., the higher the signal loss, the closer the beacons should be. The initial layout of the beacons can more accurately cover the whole preset area through scientific calculation of signal propagation characteristics and environmental parameters, and the method is more effective than the traditional empirical layout, so that dead zones can be reduced;
The beacon node is provided with a radio transmitter, a laser transmitter, an ultrasonic sensor and a GPS module, and can be fixed on a ground robot and driven to dynamically move by the ground robot.
The dynamic movement detection module is used for monitoring the signal intensity received by the beacon node according to the performance parameters and the signal loss, comparing the received signal intensity with a signal intensity threshold value, recording the current beacon node position when the received signal intensity is smaller than the signal intensity threshold value, detecting the obstacle position, and adjusting the beacon node to the target position according to the obstacle position;
the formula according to which the signal strength received by the beacon node is monitored is as follows:
In the formula, For the received signal strength of the beacon,Representing the extra loss caused by the obstacle,AndRespectively the signal transmitting powerTransmit and receive antenna gainAndSignal path lossAnd extra lossWhereinAndAre all greater than 0 and。
Wherein extra lossThis is an additional signal loss caused by obstructions and the attenuation of the signal by different materials is different, such as metal, concrete, wood, etc. Reference is made to known material loss values, in which the air is additionally lost0, Extra loss of woodAt the position ofIn units ofAdditional wear of the brickAt the position ofAdditional loss of concreteAt the position ofAdditional loss of metalAt the position of;
Comparing the received signal strength with a signal strength threshold, and judging whether the beacon node position movement is required according to different comparison results, wherein the logic based on the comparison result is that the signal strength threshold is calibrated as follows;
When (when)When the beacon node and the unmanned aerial vehicle are in the navigation state, the beacon node does not need to move, and the beacon node does not need to influence the navigation task of the unmanned aerial vehicle;
When (when) When the beacon node and the unmanned aerial vehicle are in the navigation state, the interference of the obstacle existing between the beacon node and the unmanned aerial vehicle on the signal intensity can influence the navigation task of the beacon node on the unmanned aerial vehicle, and the position of the beacon node needs to be changed.
The method for moving the beacon node is based on the fact that the beacon node measures that the position coordinate of the unmanned aerial vehicle is measured to be through a laser transmitter and a GPS moduleThe distance between the beacon node and the obstacle is detected through the ultrasonic sensor, and the position coordinates of the obstacle are obtained by combining a GPS moduleRecording the position coordinates of the current beacon node asThe new target position adjustment is based on the following formula:
In the formula, Representing the coordinates of the new target location,Representing the coordinates of the estimated location,The obstacle avoidance distance is indicated by the method,Is a unit vector pointing to an obstacle;
in which the unit vector points to the obstacle The formula on which the calculation is based is:
Estimating position coordinates The formula on which the calculation is based is:
In the formula, The unit vector from the beacon to the unmanned aerial vehicle is expressed as:
Wherein, Indicating the recommended distance between the beacon and the drone.
The navigation path planning module is used for acquiring the position information of the unmanned aerial vehicle through the beacon node of the target position, selecting a path through a particle swarm optimization algorithm according to the current position information of the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to reach a destination according to the selected optimal path.
And dynamically adjusting the position of the beacon according to the position of the unmanned aerial vehicle, shortening the signal propagation distance as much as possible, correcting the estimated position coordinates according to the position of the obstacle, and moving the beacon to a region far away from the obstacle so as to reduce signal attenuation.
The navigation path planning module is used for acquiring the position information of the unmanned aerial vehicle through the beacon node of the target position, selecting a path through a particle swarm optimization algorithm according to the current position information of the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to reach a destination according to the selected optimal path.
Selecting a path through a particle swarm optimization algorithm, defining a path of a particle representing the current position of an unmanned aerial vehicle to reach a destination, generating the path, taking a beacon node as an intermediate destination, selecting a next beacon node based on the current intermediate destination until reaching the beacon node of a final destination, recording the intermediate destination of the path, and obtaining a target path consisting of a plurality of intermediate destinations, wherein a fitness function is defined to evaluate the path quality, and the expression of the fitness function is as follows:
In the formula, Represent the firstThe fitness value corresponding to each particle is set,Represent the firstEach particle corresponds to the total length of the path,Represent the firstThe individual particles correspond to the estimated time of flight of the path,、Weighting coefficients for the total length of the path and the estimated time of flight of the path, respectively, wherein,AndAre all greater than 0, and since the influence of the total length of the corresponding path on the fitness value is higher than the estimated flight time, the method is set。
Wherein the particle swarm optimization algorithm includes the steps of defining particles, each particle representing a path that passes through the beacon node as an intermediate destination, e.g.Representing the slaveBeacon node toInitializing particle group, setting particle quantityAnd randomly generating paths as initial particle positions, the velocity of each particle may be initialized to zero, e.g. setGenerating 10 random paths, defining a fitness function to evaluate path quality, updating particle speed according to the current speed, the personal optimal position and the global optimal position of particles, updating the position of particles, adjusting according to the speed and the current position, calculating a fitness value of each particle, updating the historical optimal position of particles and the optimal positions in all particles, performing iterative operation until a stopping condition is reached, such as setting the stopping condition to reach the maximum iteration number or convergence of fitness, and outputting the path of the optimal particles as an optimized navigation path after the iteration is finished.
And finally, the unmanned aerial vehicle arrives at the destination according to the selected optimal path.
Referring to fig. 2, the present invention further provides a navigation method for an unmanned aerial vehicle, where the navigation method for an unmanned aerial vehicle is used for controlling the beacon system for unmanned aerial vehicle navigation, and the specific steps include:
step 1, collecting performance parameters generated by a beacon node nearest to the initial position of an unmanned aerial vehicle, wherein the performance parameters comprise signal transmitting power, transmitting and receiving antenna gains and signal loss;
Step 2, in a target area where the unmanned aerial vehicle flies, calculating a beacon node setting interval according to performance parameters, and arranging a plurality of beacon nodes on the ground based on the calculated beacon node setting interval to form a distributed beacon network, wherein the beacon nodes can dynamically move;
Step 3, monitoring the signal intensity received by the beacon node according to the performance parameters and the signal loss, comparing the received signal intensity with a signal intensity threshold value, recording the current beacon node position when the received signal intensity is smaller than the signal intensity threshold value, detecting the obstacle position, and adjusting the beacon node to the target position according to the obstacle position;
And 4, acquiring the position information of the unmanned aerial vehicle through the beacon node of the target position, selecting a path through a particle swarm optimization algorithm according to the current position information of the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to reach a destination according to the selected optimal path.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (3)
1. A beacon system for unmanned aerial vehicle navigation, the specific structure comprising:
The performance parameter acquisition module is used for acquiring performance parameters generated by the beacon node nearest to the initial position of the unmanned aerial vehicle, wherein the performance parameters comprise signal transmitting power, transmitting and receiving antenna gain and signal loss;
Calibrating performance parameters, the signal transmitting power is Gain of transmitting and receiving antennaAndThe signal loss isArranging a plurality of beacon nodes on the ground in an equidistant mode, wherein the equidistant mode means that the distances between the adjacent beacon nodes are allThe formula according to which the beacon interval is calculated is:
In the formula, In order to set the spacing of the beacons,As a function of the wavelength of the signal,Is signal loss;
the beacon node is provided with a radio transmitter, a laser transmitter, an ultrasonic sensor and a GPS module;
The beacon node setting module is used for calculating the beacon node setting interval according to the performance parameters in a target area where the unmanned aerial vehicle flies, arranging a plurality of beacon nodes on the ground based on the calculated beacon node setting interval to form a distributed beacon network, wherein the beacon nodes can dynamically move;
the dynamic movement detection module is used for monitoring the signal intensity received by the beacon node according to the performance parameters and the signal loss, comparing the received signal intensity with a signal intensity threshold value, recording the current beacon node position when the received signal intensity is smaller than the signal intensity threshold value, detecting the obstacle position, and adjusting the beacon node to the target position according to the obstacle position;
the formula according to which the signal intensity received by the beacon node is detected is as follows:
In the formula, For the received signal strength of the beacon,Representing the extra loss caused by the obstacle,AndRespectively the signal transmitting powerTransmit and receive antenna gainAndSignal path lossAnd extra lossWhereinAndAre all greater than 0 and;
Comparing the received signal strength with a signal strength threshold, and judging whether the beacon node position movement is required according to different comparison results, wherein the logic based on the comparison result is that the signal strength threshold is calibrated as follows;
When (when)When the beacon node and the unmanned aerial vehicle are in the navigation state, the beacon node does not need to move, and the beacon node does not need to influence the navigation task of the unmanned aerial vehicle;
When (when) When the beacon node and the unmanned aerial vehicle interfere with the signal intensity, the beacon node can influence the navigation task of the unmanned aerial vehicle, and the position of the beacon node needs to be changed;
the method for moving the beacon node is based on the fact that the beacon node measures that the position coordinate of the unmanned aerial vehicle is measured to be through a laser transmitter and a GPS module The distance between the beacon node and the obstacle is detected through the ultrasonic sensor, and the position coordinates of the obstacle are obtained by combining a GPS moduleRecording the position coordinates of the current beacon node asThe new target position adjustment is based on the following formula:
In the formula, Representing the coordinates of the new target location,Representing the coordinates of the estimated location,The obstacle avoidance distance is indicated by the method,Is a unit vector pointing to an obstacle;
in which the unit vector points to the obstacle The formula on which the calculation is based is:
Estimating position coordinates The formula on which the calculation is based is:
In the formula, The unit vector from the beacon to the unmanned aerial vehicle is expressed as:
Wherein, Representing a recommended distance between the beacon and the drone;
The navigation path planning module is used for acquiring the position information of the unmanned aerial vehicle through the beacon node of the target position, selecting a path through a particle swarm optimization algorithm according to the current position information of the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to reach a destination according to the selected optimal path.
2. The beacon system for unmanned aerial vehicle navigation according to claim 1, wherein the path is selected by a particle swarm optimization algorithm, a path is defined, the path is generated by taking a beacon node as an intermediate destination, a next beacon node is selected based on the current intermediate destination until the beacon node of a final destination is reached, the intermediate destination of the path is recorded, and a target path formed by a plurality of intermediate destinations is obtained, wherein a fitness function is defined to evaluate the path quality, and the fitness function is expressed as follows:
In the formula, Represent the firstThe fitness value corresponding to each particle is set,Represent the firstEach particle corresponds to the total length of the path,Represent the firstThe individual particles correspond to the estimated time of flight of the path,、The weight coefficients of the total length of the path and the estimated flight time of the path are respectively.
3. A navigation method for a unmanned aerial vehicle is characterized in that the navigation method for the unmanned aerial vehicle is used for controlling the beacon system for unmanned aerial vehicle navigation according to any one of claims 1-2, and the specific steps comprise:
collecting performance parameters generated by a beacon node nearest to the initial position of the unmanned aerial vehicle, wherein the performance parameters comprise signal transmitting power, transmitting and receiving antenna gains and signal loss;
in a target area where the unmanned aerial vehicle flies, calculating the beacon node setting interval according to the performance parameter, and arranging a plurality of beacon nodes on the ground based on the calculated beacon node setting interval to form a distributed beacon network, wherein the beacon nodes can dynamically move;
Monitoring the signal intensity received by the beacon node according to the performance parameters and the signal loss, comparing the received signal intensity with a signal intensity threshold, recording the current beacon node position when the received signal intensity is smaller than the signal intensity threshold, detecting the obstacle position, and adjusting the beacon node to the target position according to the obstacle position;
And acquiring the position information of the unmanned aerial vehicle through the beacon node of the target position, selecting a path through a particle swarm optimization algorithm according to the current position information of the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to reach a destination according to the selected optimal path.
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