A beacon system and navigation method for unmanned aerial vehicle navigation

Abstract

The present invention discloses a beacon system and a navigation method for unmanned aerial vehicle navigation, and the present invention relates to the field of unmanned aerial vehicle navigation technology. The method comprises the following steps: collecting performance parameters generated by the beacon node closest to the unmanned aerial vehicle based on the initial position; in the target area where the unmanned aerial vehicle flies, calculating the beacon node setting interval according to the performance parameters, and arranging a plurality of beacon nodes on the ground in an equidistant manner; monitoring the signal strength received by the beacon node according to the performance parameters and signal loss, comparing the received signal strength with the signal strength threshold, and when the received signal strength is less than the signal strength threshold, recording the current beacon node position, detecting the obstacle position, and adjusting the beacon node to the target position according to the obstacle position; collecting the unmanned aerial vehicle position information through the beacon node at the target position, selecting the path according to the current unmanned aerial vehicle position information through the particle swarm optimization algorithm, and the unmanned aerial vehicle reaches the destination according to the selected optimal path.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle navigation, and in particular to a beacon system and a navigation method for unmanned aerial vehicle navigation. Background Art

[0002] Unmanned aerial vehicles (UAVs) are aircraft that do not require onboard personnel and are usually controlled by remote controls, autonomous computers, or pre-programmed instructions. Currently, in both civil and military fields, cutting-edge drones are able to fly freely in the air and generally have the advantages of low cost, easy deployment, flexible movement, and wide field of vision. This makes drones have advantages that other intelligent robots do not have, whether in complex and harsh environments that are difficult for humans to enter or in standardized and patterned urban environments.

[0003] Drone technology has shown great potential in many fields such as agriculture, transportation, electricity and logistics. They improve operational efficiency, reduce risks and provide key data for geological exploration by performing tasks such as pesticide spraying, aerial monitoring, power line inspection and terminal delivery. With the continuous advancement of technology, drones are becoming more and more intelligent, and researchers are working to promote their development to higher and farther goals.

[0004] In the current field of drone navigation, traditional positioning systems such as GPS often face the problem of weak signals or failure in indoor or densely populated urban environments, which limits the application scenarios of drones. As a result, drones face technical difficulties such as inaccurate path planning and unreliable navigation when performing tasks. Especially in complex environments such as large buildings, underground facilities or post-disaster rescue areas, it is particularly important to develop a more reliable beacon-based navigation system.

[0005] In the prior art, the publication number CN117908050B discloses a beacon system and navigation method for drone navigation. By setting a beacon system for drone navigation including a distance beacon, an azimuth beacon and a platform wireless receiver, and matching the corresponding navigation method, it can provide local positioning and navigation when the GNSS signal is poor or unavailable, or merge with the degraded GNSS signal to provide positioning and navigation functions; the position data of the beacon is transmitted to the platform wireless receiver via a radio signal, and the platform receives the signal and uses the signal strength to estimate its own position. This method has the technical effects of simple structure, low production cost, convenient layout, easy implementation, low power consumption, etc. However, the beacon position of this solution is fixed, and it may not be able to effectively respond to environmental changes, such as obstacles or signal interference, resulting in insufficient signal coverage. In a dynamic environment, the poor flexibility of fixed beacons may not be able to adapt to changes, affecting navigation accuracy, resulting in failure to meet the user's accuracy requirements.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The object of the present invention is to provide a beacon system and a navigation method for unmanned aerial vehicle navigation to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A beacon system for unmanned aerial vehicle navigation, specifically comprising:

[0010] A performance parameter collection module is used to collect performance parameters generated by the beacon node closest to the initial position of the UAV, the performance parameters including signal transmission power, transmission and receiving antenna gain and signal loss;

[0011] A beacon node setting module is used to calculate the beacon node setting interval according to the performance parameters in the target area where the UAV flies, and arrange a number 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 be dynamically moved;

[0012] A dynamic movement detection module is used to monitor the signal strength received by the beacon node according to the performance parameters and signal loss, compare the received signal strength with the signal strength threshold, and when the received signal strength is less than the signal strength threshold, record the current beacon node position, detect the obstacle position, and adjust the beacon node to the target position according to the obstacle position;

[0013] The navigation path planning module is used to collect the UAV location information through the beacon node at the target location. According to the current UAV location information, the path is selected through the particle swarm optimization algorithm. The UAV reaches the destination according to the selected optimal path.

[0014] Furthermore, the performance parameters are calibrated, and the signal transmission power is , the transmitting and receiving antenna gains are and , the signal loss is , a number of beacon nodes are arranged on the ground in an equidistant manner, where the distance between adjacent beacon nodes is , where the beacon spacing is calculated based on the formula:

[0015]

[0016] In the formula, To set the beacon spacing, is the signal wavelength, For signal loss.

[0017] Furthermore, the beacon node is equipped with the following components: a radio transmitter, a laser transmitter, an ultrasonic sensor and a GPS module. The beacon node is fixed on a ground robot, and the ground robot drives the beacon node to move dynamically.

[0018] Furthermore, the formula for detecting the signal strength received by the beacon node is:

[0019]

[0020] In the formula, is the signal strength received by the beacon node, represents the additional loss caused by obstacles, and The signal transmission power is , transmit and receive antenna gain and , signal path loss and additional losses The weight coefficient of and are greater than 0 and .

[0021] Furthermore, the received signal strength is compared with the signal strength threshold, and the logic for determining whether the beacon node needs to be moved is as follows: the signal strength threshold is ;

[0022] when When , it means that there is no obstacle between the beacon node and the UAV or the interference of the obstacle on the signal strength will not affect the navigation task of the beacon node to the UAV; there is no need to move the beacon node;

[0023] when When , it means that the interference of obstacles between the beacon node and the UAV on the signal strength will affect the navigation task of the beacon node to the UAV, and the position of the beacon node needs to be changed.

[0024] Furthermore, the method for moving the beacon node is as follows: the beacon node measures the position coordinates of the drone through the laser transmitter and GPS module: , detect the distance between the beacon node and the obstacle through the ultrasonic sensor, and obtain the location coordinates of the obstacle by combining with the GPS module , record the location coordinates of the current beacon node as , the formula for the new target position adjustment is:

[0025]

[0026] In the formula, Represents the new target position coordinates, Represents the estimated position coordinates, Indicates the obstacle avoidance distance. is the unit vector pointing towards the obstacle;

[0027] The unit vector pointing to the obstacle is The calculation is based on the formula:

[0028]

[0029] Estimated location coordinates The calculation is based on the formula:

[0030]

[0031] In the formula, is the unit vector from the beacon to the drone, and its expression formula is:

[0032]

[0033] in, Indicates the recommended distance between the beacon and the drone.

[0034] Furthermore, the path is selected by particle swarm optimization algorithm, and a particle is defined to represent a path from the current position of the drone to the destination. The path is generated with the beacon node as the intermediate destination. The next beacon node is selected based on the current intermediate destination until the beacon node where the final destination is located is reached. The intermediate destinations along the way are recorded to obtain the target path composed of several intermediate destinations. The fitness function is defined to evaluate the path quality. The expression of the fitness function is:

[0035]

[0036] In the formula, Indicates The fitness value corresponding to each particle is Indicates The total length of the path corresponding to particles is Indicates The estimated flight time of each particle corresponding to the path, , They are the weight coefficients for the total path length and the estimated flight time of the path respectively.

[0037] The present invention also provides a navigation method for an unmanned aerial vehicle, wherein the navigation method for an unmanned aerial vehicle is used to control the beacon system for navigation of the unmanned aerial vehicle, and the specific steps include:

[0038] Collecting performance parameters generated by the beacon node closest to the initial position of the UAV, the performance parameters including signal transmission power, transmission and receiving antenna gain and signal loss;

[0039] In the target area where the UAV flies, a beacon node setting interval is calculated according to the performance parameters, and a plurality of beacon nodes are arranged on the ground based on the calculated beacon node setting interval to form a distributed beacon network, wherein the beacon nodes can be dynamically moved;

[0040] Monitor the signal strength received by the beacon node according to the performance parameters and signal loss, compare the received signal strength with the signal strength threshold, and when the received signal strength is less than the signal strength threshold, record the current beacon node position, detect the obstacle position, and adjust the beacon node to the target position according to the obstacle position;

[0041] The drone location information is collected through the beacon node at the target location. According to the current drone location information, the path is selected through the particle swarm optimization algorithm, and the drone reaches the destination according to the selected optimal path.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] By arranging several beacon nodes on the ground in an equidistant manner in the target area of ​​the UAV flight, a distributed beacon network is formed. By arranging beacons in the target area at an equidistant manner, the uniformity of signal coverage can be ensured, blind areas can be reduced, and positioning accuracy can be improved. The performance parameters generated by the beacon node closest to the initial position of the UAV are collected, and the signal strength received by the beacon node is monitored according to the performance parameters. The performance parameter monitoring of the beacon node provides real-time feedback for the system, which helps to correct and adjust the signal coverage strategy and ensure the stability and reliability of the UAV during flight. The received signal strength is compared with the signal strength threshold. When the received signal strength is less than the signal strength threshold, the current beacon node is recorded, the obstacle position is detected, and the beacon node is adjusted to the target position according to the obstacle position; when the beacon node detects that the signal strength is lower than the threshold, it can be determined that there may be an obstacle, and the obstacle position is recorded in time and the beacon position is adjusted. This adaptive capability enables the beacon network to maintain efficient signal transmission in a dynamic environment. The UAV position information is collected by the beacon node at the target position. According to the current UAV position information, the path is selected by the particle swarm optimization algorithm, and the UAV reaches the destination according to the selected optimal path. At the same time, the particle swarm optimization algorithm with its fast convergence characteristics is used to select the path of the UAV, which can achieve fast and effective path planning, optimize the flight route and improve flight efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the overall system structure of the present invention;

[0045] Figure 2 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0047] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words 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 only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0048] See also Figure 1 , the present invention provides a system structure:

[0049] A beacon system for unmanned aerial vehicle navigation, the specific structure includes:

[0050] A performance parameter collection module is used to collect performance parameters generated by the beacon node closest to the initial position of the UAV, the performance parameters including signal transmission power, transmission and receiving antenna gain and signal loss;

[0051] The transmission power The method of obtaining is: it can be obtained from the technical specifications of the beacon, or it can be measured in the experiment. In practical applications, a standard transmission power value is usually selected; the gain of the signal transmission antenna , usually expressed as a dimensionless ratio, can be obtained from the antenna's technical documentation. The formula for calculating the gain of a signal transmitting antenna is:

[0052]

[0053] in, is the decibel value of gain;

[0054] Signal receiving antenna gain Again, this can be obtained from the technical specifications of the receiving antenna, and the method of converting to a linear value is the same as the transmit gain;

[0055] Wavelength of the signal Usually related to the frequency of the signal Related, the unit is meter (m), the wavelength can be calculated from the frequency;

[0056] Signal path loss It is the loss encountered by the signal during propagation. The methods for obtaining it include: calculating by measuring the signal strength in the actual environment; referring to existing literature or research data, especially the signal loss data in a specific environment; calculating according to the propagation model (such as the free space loss model, urban environment model, etc.).

[0057] A beacon node setting module is used to calculate the beacon node setting interval according to the performance parameters in the target area where the UAV flies, and arrange a number 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 be dynamically moved;

[0058] Calibrate the performance parameters, the signal transmission power is , the transmitting and receiving antenna gains are and , the signal loss is , a number of beacon nodes are arranged on the ground in an equidistant manner, where the distance between adjacent beacon nodes is , where the beacon spacing is calculated based on the formula:

[0059]

[0060] In the formula, To set the beacon spacing, is the signal wavelength, For signal loss.

[0061] Beacon spacing With transmit power , transmit and receive antenna gain and , signal wavelength Proportional, that is, when the transmission power generated by the beacon node , transmit and receive antenna gain and , signal wavelength The larger the signal coverage area, the larger the spacing between beacon nodes can be. It is inversely proportional to the signal path loss, that is, the higher the signal loss, the closer the beacon nodes should be set. Through scientific calculations of signal propagation characteristics and environmental parameters, the initial layout of beacons can cover the entire predetermined area more accurately. This method is more effective than traditional empirical layout and can reduce blind spots.

[0062] The beacon node is equipped with the following components: a radio transmitter, a laser transmitter, an ultrasonic sensor and a GPS module. The beacon node can be fixed on a ground robot, and the ground robot drives the beacon node to move dynamically.

[0063] A dynamic movement detection module is used to monitor the signal strength received by the beacon node according to the performance parameters and signal loss, compare the received signal strength with the signal strength threshold, and when the received signal strength is less than the signal strength threshold, record the current beacon node position, detect the obstacle position, and adjust the beacon node to the target position according to the obstacle position;

[0064] The formula for monitoring the signal strength received by the beacon node is:

[0065]

[0066] In the formula, is the signal strength received by the beacon node, represents the additional loss caused by obstacles, and The signal transmission power is , transmit and receive antenna gain and , signal path loss and additional losses The weight coefficient of and are greater than 0 and .

[0067] The additional loss , which is the additional signal loss caused by obstacles. Different materials have different attenuation on the signal, such as metal, concrete, wood, etc. Refer to some known material loss values, among which the additional loss of air is 0, additional wood loss exist , the unit is , additional loss of bricks exist , additional loss of concrete exist , additional loss of metal exist ;

[0068] The received signal strength is compared with the signal strength threshold. The logic for determining whether the beacon node needs to be moved is as follows: ;

[0069] when When , it means that there is no obstacle between the beacon node and the UAV or the interference of the obstacle on the signal strength will not affect the navigation task of the beacon node to the UAV; there is no need to move the beacon node;

[0070] when When , it means that the interference of obstacles between the beacon node and the UAV on the signal strength will affect the navigation task of the beacon node to the UAV, and the position of the beacon node needs to be changed.

[0071] The method for moving the beacon node is as follows: the beacon node measures the position coordinates of the drone through the laser transmitter and GPS module: , detect the distance between the beacon node and the obstacle through the ultrasonic sensor, and obtain the location coordinates of the obstacle by combining with the GPS module , record the location coordinates of the current beacon node as , the formula for the new target position adjustment is:

[0072]

[0073] In the formula, Represents the new target position coordinates, Represents the estimated position coordinates, Indicates the obstacle avoidance distance. is the unit vector pointing towards the obstacle;

[0074] The unit vector pointing to the obstacle is The calculation is based on the formula:

[0075]

[0076] Estimated location coordinates The calculation is based on the formula:

[0077]

[0078] In the formula, is the unit vector from the beacon to the drone, and its expression formula is:

[0079]

[0080] in, Indicates the recommended distance between the beacon and the drone.

[0081] The navigation path planning module is used to collect the UAV location information through the beacon node at the target location. According to the current UAV location information, the path is selected through the particle swarm optimization algorithm. The UAV reaches the destination according to the selected optimal path.

[0082] According to the position of the drone, the beacon position is dynamically adjusted to shorten the signal propagation distance as much as possible. Then the estimated position coordinates are corrected according to the position of the obstacle, and the beacon is moved to an area away from the obstacle to reduce signal attenuation.

[0083] The navigation path planning module is used to collect the UAV location information through the beacon node at the target location. According to the current UAV location information, the path is selected through the particle swarm optimization algorithm. The UAV reaches the destination according to the selected optimal path.

[0084] The path is selected by the particle swarm optimization algorithm. A particle is defined to represent a path from the current position of the drone to the destination. The path is generated with the beacon node as the intermediate destination. The next beacon node is selected based on the current intermediate destination until the beacon node where the final destination is located is reached. The intermediate destinations along the way are recorded to obtain the target path composed of several intermediate destinations. The fitness function is defined to evaluate the path quality. The expression of the fitness function is:

[0085]

[0086] In the formula, Indicates The fitness value corresponding to each particle is Indicates The total length of the path corresponding to particles is Indicates The estimated flight time of each particle corresponding to the path, , are the weight coefficients of the total path length and the estimated flight time of the path, respectively, where: and are all greater than 0, and since the total length of the corresponding path has a greater impact on the fitness value than the estimated flight time, we set .

[0087] Among them, the steps of the particle swarm optimization algorithm include: defining particles, each particle represents a path, and the path is composed of beacon nodes as intermediate destinations, such as , indicating that from Beacon node to The path of the beacon node; Initialize the particle swarm: set the number of particles , and randomly generate a path as the initial particle position. The speed of each particle can be initialized to zero, such as , then 10 random paths are generated; a fitness function is defined to evaluate the path quality; the particle speed is updated according to the particle's current speed, personal best position and global best position, the particle's position is updated, and it is adjusted according to the speed and current position; the fitness value of each particle is calculated, the particle's historical best position and the best position among all particles are updated, and an iterative operation is performed until the stopping condition is reached, such as setting the stopping condition to reach the maximum number of iterations or fitness convergence; after the iteration, the path of the best particle is output as the optimized navigation path.

[0088] Finally, the drone reaches the destination according to the selected optimal path.

[0089] See also Figure 2 The present invention also provides a navigation method for an unmanned aerial vehicle, wherein the navigation method for an unmanned aerial vehicle is used to control the beacon system for navigation of the unmanned aerial vehicle, and the specific steps include:

[0090] Step 1: Collect performance parameters generated by the beacon node closest to the initial position of the drone, including signal transmission power, transmission and receiving antenna gain, and signal loss;

[0091] Step 2: In the target area where the UAV flies, the beacon node setting interval is calculated according to the performance parameters, and based on the calculated beacon node setting interval, a number of beacon nodes are arranged on the ground to form a distributed beacon network, wherein the beacon nodes can be dynamically moved;

[0092] Step 3: Monitor the signal strength received by the beacon node according to the performance parameters and signal loss, compare the received signal strength with the signal strength threshold, and when the received signal strength is less than the signal strength threshold, record the current beacon node position, detect the obstacle position, and adjust the beacon node to the target position according to the obstacle position;

[0093] Step 4: The drone location information is collected through the beacon node at the target location. According to the current drone location information, the path is selected through the particle swarm optimization algorithm, and the drone reaches the destination according to the selected optimal path.

[0094] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0095] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0096] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple 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.

[0097] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A beacon system for unmanned aerial vehicle navigation, characterized in that: The specific structure includes: A performance parameter collection module is used to collect performance parameters generated by the beacon node closest to the initial position of the UAV, the performance parameters including signal transmission power, transmission and receiving antenna gain and signal loss; Calibrate the performance parameters, the signal transmission power is , the transmitting and receiving antenna gains are and , the signal loss is , a number of beacon nodes are arranged on the ground in an equidistant manner, where the distance between adjacent beacon nodes is , where the beacon spacing is calculated based on the formula: In the formula, To set the beacon spacing, is the signal wavelength, is signal loss; The beacon node is equipped with the following components: a radio transmitter, a laser transmitter, an ultrasonic sensor and a GPS module; A beacon node setting module is used to calculate the beacon node setting interval according to the performance parameters in the target area where the UAV flies, and arrange a number 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 be dynamically moved; A dynamic movement detection module is used to monitor the signal strength received by the beacon node according to the performance parameters and signal loss, compare the received signal strength with the signal strength threshold, and when the received signal strength is less than the signal strength threshold, record the current beacon node position, detect the obstacle position, and adjust the beacon node to the target position according to the obstacle position; The formula for detecting the signal strength received by the beacon node is: In the formula, is the signal strength received by the beacon node, represents the additional loss caused by obstacles, and The signal transmission power is , transmit and receive antenna gain and , signal path loss and additional losses The weight coefficient of and are greater than 0 and ; The received signal strength is compared with the signal strength threshold. The logic for determining whether the beacon node needs to be moved is as follows: ; when When , it means that there is no obstacle between the beacon node and the UAV or the interference of the obstacle on the signal strength will not affect the navigation task of the beacon node to the UAV; there is no need to move the beacon node; when When , it means that the interference of obstacles between the beacon node and the UAV on the signal strength will affect the navigation task of the beacon node to the UAV, and the position of the beacon node needs to be changed; The method for moving the beacon node is as follows: the beacon node measures the position coordinates of the drone through the laser transmitter and GPS module: , detect the distance between the beacon node and the obstacle through the ultrasonic sensor, and obtain the location coordinates of the obstacle by combining with the GPS module , record the location coordinates of the current beacon node as , the formula for the new target position adjustment is: In the formula, Represents the new target position coordinates, Represents the estimated position coordinates, Indicates the obstacle avoidance distance. is the unit vector pointing towards the obstacle; The unit vector pointing to the obstacle is The calculation is based on the formula: Estimated location coordinates The calculation is based on the formula: In the formula, is the unit vector from the beacon to the drone, and its expression formula is: in, Indicates the recommended distance between the beacon and the drone; The navigation path planning module is used to collect the UAV location information through the beacon node at the target location. According to the current UAV location information, the path is selected through the particle swarm optimization algorithm. The UAV reaches the destination according to the selected optimal path.

2. A beacon system for drone navigation according to claim 1, characterized in that: The path is selected by the particle swarm optimization algorithm. A particle is defined to represent a path from the current position of the drone to the destination. The path is generated with the beacon node as the intermediate destination. The next beacon node is selected based on the current intermediate destination until the beacon node where the final destination is located is reached. The intermediate destinations along the way are recorded to obtain the target path composed of several intermediate destinations. The fitness function is defined to evaluate the path quality. The expression of the fitness function is: In the formula, Indicates The fitness value corresponding to each particle is Indicates The total path length corresponding to particles is Indicates The estimated flight time of each particle corresponding to the path, , They are the weight coefficients for the total path length and the estimated flight time of the path respectively.

3. A navigation method for an unmanned aerial vehicle, characterized in that: The navigation method for a drone is used to control the beacon system for drone navigation according to any one of claims 1 to 2, and the specific steps include: Collecting performance parameters generated by the beacon node closest to the initial position of the UAV, the performance parameters including signal transmission power, transmission and receiving antenna gain and signal loss; In the target area where the UAV flies, a beacon node setting interval is calculated according to the performance parameters, and a plurality of beacon nodes are arranged on the ground based on the calculated beacon node setting interval to form a distributed beacon network, wherein the beacon nodes can be dynamically moved; Monitor the signal strength received by the beacon node according to the performance parameters and signal loss, compare the received signal strength with the signal strength threshold, and when the received signal strength is less than the signal strength threshold, record the current beacon node position, detect the obstacle position, and adjust the beacon node to the target position according to the obstacle position; The drone location information is collected through the beacon node at the target location. According to the current drone location information, the path is selected through the particle swarm optimization algorithm, and the drone reaches the destination according to the selected optimal path.

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