Unmanned aerial vehicle positioning method, system and program product based on wireless positioning beacons

Abstract

The invention provides an unmanned aerial vehicle positioning method and system based on a wireless positioning beacon and a program product, and relates to the technical field of unmanned aerial vehicle positioning, and the unmanned aerial vehicle positioning method comprises the steps: obtaining the first position information of the wireless positioning beacon, the second position information of an unmanned aerial vehicle, and the measurement distance information between the wireless positioning beacon and the unmanned aerial vehicle; determining a relative error vector of the unmanned aerial vehicle relative to the wireless positioning beacon based on the measurement distance information, the first position information and the second position information; and determining relative position information between the unmanned aerial vehicle and the wireless positioning beacon based on the relative error vector.

Description

Technical Field

[0001] This specification relates to the technical field of unmanned aerial vehicle (UAV) positioning, and particularly to a UAV positioning method, system, and computer program product based on a wireless positioning beacon. Background Art

[0002] UAVs usually cooperate with wireless positioning beacons for positioning in short - range scenarios (such as automatic following, formation flight, etc.). Since UAVs and wireless positioning beacons usually use satellite positioning systems to determine their respective position information, and the satellite positioning system has inherent or slowly drifting positioning errors (the typical positioning error is usually in the range of 2 - 5 meters), when the error is significant relative to the true distance between the two, it is easy to cause relative position and orientation errors between the UAV and the wireless positioning beacon under short - range conditions, affecting the precise navigation and flight safety of the UAV.

[0003] In view of this, some embodiments of this specification provide a UAV positioning method, system, and computer program product based on a wireless positioning beacon, which can determine the relative position information between the UAV and the wireless positioning beacon based on the positioning information and relative distance information of the wireless positioning beacon and the UAV, improving the relative positioning accuracy between the UAV and the beacon. Summary of the Invention

[0004] One or more embodiments of this specification provide a UAV positioning based on a wireless positioning beacon. The method includes: obtaining the first position information of the wireless positioning beacon, the second position information of the UAV, and the measured distance information between the wireless positioning beacon and the UAV; determining the relative error vector of the UAV relative to the wireless positioning beacon based on the measured distance information, the first position information, and the second position information; and determining the relative position information between the UAV and the wireless positioning beacon based on the relative error vector.

[0005] In some embodiments, the wireless positioning beacon includes a Bluetooth positioning beacon; there is a Bluetooth communication connection between the UAV and the Bluetooth positioning beacon, and the measured distance information includes the measured distance value between the UAV and the Bluetooth positioning beacon obtained through Bluetooth channel detection technology.

[0006] In some embodiments, the measurement error between the actual distance value and the measured distance value between the UAV and the Bluetooth positioning beacon is less than the first positioning error between the first position information and the actual position of the wireless positioning beacon; and the measurement error is less than the second positioning error between the second position information and the actual position of the UAV.

[0007] In some embodiments, the first position information of the wireless positioning beacon is determined based on a satellite positioning system; and / or the second position information of the UAV is determined based on a satellite positioning system.

[0008] In some embodiments, obtaining the first position information of the wireless positioning beacon, the second position information of the unmanned aerial vehicle (UAV), and the measured distance information between the wireless positioning beacon and the UAV includes: based on a preset sliding window size, obtaining the position coordinates of the wireless positioning beacon, the position coordinates of the UAV, and the measured distance values corresponding to multiple acquisition points included in each sliding window; the first position information includes the position coordinates of the wireless positioning beacon corresponding to a single sliding window; the second position information includes the position coordinates of the UAV corresponding to a single sliding window; and the measured distance information includes the measured distance values corresponding to a single sliding window.

[0009] In some embodiments, based on the measured distance information, the first position information, and the second position information, determining the relative error vector of the UAV relative to the wireless positioning beacon includes: based on the first position information and the second position information, determining multiple calculated distance vectors of the UAV relative to the wireless positioning beacon corresponding to the first sliding window; establishing an objective function associated with the relative error vector; and according to the measured distance values and the calculated distance vectors corresponding to the first sliding window, using a non-linear optimization algorithm to optimize the objective function, and determining the relative error vector corresponding to the first sliding window according to the optimized objective function.

[0010] In some embodiments, the objective function is associated with the difference between the norm of the calculated distance vector corresponding to the first sliding window after being corrected by the relative error vector and the measured distance value; using a non-linear optimization algorithm to optimize the objective function includes: adjusting the relative error vector so that the difference between the norm of the calculated distance vector corresponding to the first sliding window after being corrected by the adjusted relative error vector and the measured distance value is minimized.

[0011] In some embodiments, using a non-linear optimization algorithm to optimize the objective function and determining the relative error vector corresponding to the first sliding window according to the optimized objective function includes: according to the measured distance values, the calculated distance vectors, and a preset error vector corresponding to the first sliding window, using a non-linear optimization algorithm to optimize the objective function, and determining the relative error vector corresponding to the first sliding window according to the optimized objective function; or according to the measured distance values, the calculated distance vectors, and the historical relative error vector corresponding to the previous sliding window corresponding to a single sliding window, using a non-linear optimization algorithm to optimize the objective function, and determining the relative error vector corresponding to the first sliding window according to the optimized objective function.

[0012] One or more embodiments of this specification also provide a drone positioning system based on wireless positioning beacons. The system includes: a position acquisition module, configured to acquire the first position information of the wireless positioning beacon, the second position information of the drone, and the measured distance information between the wireless positioning beacon and the drone; an error determination module, configured to determine the relative error vector of the drone relative to the wireless positioning beacon based on the measured distance information, the first position information, and the second position information; and a positioning correction module, configured to determine the relative position information between the drone and the wireless positioning beacon based on the relative error vector.

[0013] Some embodiments of this specification also provide a computer program product, including computer instructions or a computer program. When at least part of the computer instructions or the computer program is executed by a processor, it can implement the drone positioning method based on wireless positioning beacons provided by any embodiment of this specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] This specification will be further described by way of exemplary embodiments, which will be described in detail through the drawings. The same reference numerals in the drawings represent the same structures or steps.

[0015] Figure 1 FIG. is a schematic diagram of a drone positioning application scenario using wireless positioning beacons according to some embodiments of this specification.

[0016] Figure 2 FIG. is an exemplary flowchart of a drone positioning method based on wireless positioning beacons according to some embodiments of this specification.

[0017] Figure 3 FIG. is an exemplary flowchart of determining the relative error vector of the drone relative to the wireless positioning beacon according to some embodiments of this specification.

[0018] Figure 4 FIG. is a schematic diagram of the functional modules of a drone positioning system based on wireless positioning beacons according to some embodiments of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To more clearly illustrate the technical solutions of the embodiments of this specification, the embodiments will be introduced in detail below with reference to the drawings. Obviously, the content described below is some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, the technical solutions or means disclosed in this specification can also be applied to other scenarios based on this technical content.

[0020] It should be understood that the "system", "device", "unit" and / or "module" used in this specification is a way to distinguish different components, elements, parts, sections or assemblies at different levels. However, if other words can achieve the same purpose, the above words can be replaced by other expressions.

[0021] Unless otherwise specified, the technical terms describing components, elements, etc. in this specification do not specifically refer to the singular, but may also include the plural. Generally speaking, terms such as "including" and "comprising" only indicate the inclusion of the clearly identified steps, elements or components, and these steps, elements and components do not constitute an exclusive list. For example, the described method or device may also include other steps or components.

[0022] Flowcharts are used in this specification to illustrate the operation steps performed by the devices or systems of related embodiments. However, unless otherwise specified, the order in which these steps are described should not be construed as a limitation on the order of step execution. Those of ordinary skill in the art can adjust the order of execution of these steps according to the knowledge and information conveyed by the embodiments of this specification. The above adjustments include, but are not limited to, swapping the order of precedence, combining multiple steps, and splitting a certain step.

[0023] Drones usually cooperate with wireless positioning beacons for positioning in close - range scenarios (such as automatic following, formation flight, etc.). Since drones and wireless positioning beacons usually use satellite positioning systems to determine their respective position information, and the satellite positioning system has inherent positioning errors (the typical positioning error is usually in the range of 2 - 5 meters), it is easy to cause relative position and orientation errors between the drone and the wireless positioning beacon under close - range conditions, affecting the precise navigation and flight safety of the drone.

[0024] In view of this, this specification provides a drone positioning method, system and computer program product based on a wireless positioning beacon.

[0025] Figure 1 It is a schematic diagram of a drone positioning application scenario using a wireless positioning beacon shown according to some embodiments of this specification. In some embodiments, as Figure 1 shown, the drone positioning application scenario 100 using a wireless positioning beacon may include a wireless positioning beacon 110, a drone 120, a processing device 130, a network 140, and a terminal device 150. Among them, the wireless positioning beacon 110, the drone 120, the processing device 130, and the terminal device 150 can perform data transmission and communication interaction through the network 140.

[0026] In some embodiments, the wireless positioning beacon 110 is a wireless device for assisting in positioning and / or implementing object tracking, and provides its own position information by transmitting specific wireless signals (such as specific radio frequency signals, Bluetooth wireless signals, ultra-wideband wireless signals, etc.). In some embodiments, the wireless positioning beacon 110 can be fixedly installed, and a reference positioning system for the drone 120 to perform relative position reference positioning can be established by setting one or more wireless positioning beacons 110. In some embodiments, the wireless positioning beacon 110 can also be a mobile device that can be held by a user or a wearable mobile device that can be worn by a user (such as a wearable positioning bracelet, etc.), which is used to provide continuously updated position information to the drone 120 for the drone to follow in flight, and is not limited herein.

[0027] In some embodiments, the drone 120 can cooperate with the wireless positioning beacon 110 for positioning and / or flight trajectory planning in close-range scenario applications. For example, in a specific instance, in the close-range scenario application of the drone 120 automatically following, the moving wireless positioning beacon 110 can be used as the following target of the drone to follow. The drone 120 can obtain the reference position information provided by the wireless positioning beacon 110 through the communication connection with the wireless positioning beacon 110, and combine the self-positioning information of the drone 120 to perform real-time planning and adjustment of the following path of the drone 120. Another example is that in another specific instance, in the close-range scenario application of drone formation flight, a reference positioning system can be established according to the positions of one or more wireless positioning beacons 110. The drone 120 can obtain the reference position information provided by the wireless positioning beacon 110 through the communication connection with one or more wireless positioning beacons 110, and combine the self-positioning information of the drone 120 to determine whether the drone 120 is at the predetermined position of the formation flight, and guide the drone 120 to move to the predetermined position of the formation flight according to the reference position information provided by the wireless positioning beacon 110.

[0028] In some embodiments, the processing device 130 may be a computer device with high computing performance, which is used to determine the relative position information of the drone 120 relative to the wireless positioning beacon 110 based on the positioning information provided by the wireless positioning beacon 110 and the drone 120. In some embodiments, the processing device 130 may be a single computer device or a computing cluster composed of multiple computer devices, so as to provide powerful computing power and efficient response for determining the relative position information of the drone 120 relative to the wireless positioning beacon 110. In some embodiments, the processing device 130 may be a server, which may be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Distribute Network), big data and artificial intelligence platforms.

[0029] In some embodiments, the network 140 may be any form of wired or wireless network, or any combination thereof. By way of example only, the network 140 may be one or more combinations of a wired network, an optical fiber network, a telecommunication network, an internal network, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), a public switched telephone network (PSTN), a Bluetooth network, etc. The network 140 may have multiple access points, and the wireless positioning beacon 110, the drone 120, the processing device 130 and the terminal device 150 may access the network 130 through the access points.

[0030] In some embodiments, the terminal device 150 may include, but is not limited to, a desktop computer, a smart phone, a laptop computer, a VR (Virtual Reality) device, a tablet computer, etc. The user may view the relative position information of the drone 120 relative to the wireless positioning beacon 110, plan the flight trajectory of the drone 120, etc. here, which is not limited.

[0031] It should be noted that Figure 1The shown UAV positioning application scenario 100 using a wireless positioning beacon is merely an example. The scenarios described in the embodiments of this specification are for more clearly illustrating the technical solutions of the embodiments of this specification, and do not constitute a limitation to the technical solutions provided by the embodiments of this specification. Those of ordinary skill in the art will know that the technical solutions provided by the embodiments of this specification are equally applicable to similar technical problems. For example, in some application scenarios, the wireless positioning beacon 110 and the processing device 130 can be integrated into one device, and the user can achieve automatic following and guiding of the UAV by holding this device. In some application scenarios, the wireless positioning beacon 110, the processing device 130, and the terminal device 150 can be integrated into one device, which is not limited herein. In some application scenarios, the processing device 130 can be integrated into the UAV 120 or the terminal device 150.

[0032] Figure 2 is an exemplary flowchart of a UAV positioning method based on a wireless positioning beacon shown according to some embodiments of this specification. In some embodiments, Figure 2 The shown process 200 can be executed by the processing device 130. In some embodiments, the process 200 can be implemented by the wireless positioning beacon-based UAV positioning system 400 deployed on the processing device 130.

[0033] In some embodiments, as Figure 2 shown, the process 200 can include the following steps.

[0034] Step 210: Obtain the first position information of the wireless positioning beacon, the second position information of the UAV, and the measured distance information between the wireless positioning beacon and the UAV. In some embodiments, step 210 can be implemented by the position acquisition module 410.

[0035] In some embodiments, there is a communication connection between the drone and the wireless positioning beacon, so that the drone can obtain the reference position information provided by the wireless positioning beacon and plan and / or adjust its own flight trajectory according to the reference position information. In some embodiments, there is a communication connection between the drone and the wireless positioning beacon, and the drone can feedback the obtained reference position information provided by the wireless positioning beacon to the remote control system of the drone, and the remote control system plans and / or adjusts the flight trajectory of the drone. In some embodiments, the remote control system of the drone can directly establish a communication connection with the wireless positioning beacon, so that the remote control system can plan and / or adjust the flight trajectory of the drone according to the reference position information provided by the wireless positioning beacon. In some embodiments, the wireless positioning beacon may include a Bluetooth positioning beacon, and there is a Bluetooth communication connection between the drone and the Bluetooth positioning beacon. In some embodiments, those skilled in the art can implement the communication connection between the drone and the wireless positioning signal through WIFI communication connection, UWB (Ultra-Wideband) communication connection, etc. according to the permission and needs of the actual application scenario, which is not limited herein.

[0036] In some embodiments, the first position information of the wireless positioning beacon can be obtained regularly or irregularly when the wireless positioning beacon is in the working state. In some embodiments, the first position information of the wireless positioning beacon can be determined based on a satellite positioning system. For example, the wireless positioning beacon can be equipped with a receiver module that can receive the broadcast signals of the satellite positioning system. The receiver module calculates and determines the first position information of the wireless positioning beacon by using the time difference of the broadcast signals broadcast by different satellites in the satellite positioning system, which carry their own position information and broadcast time information. In some embodiments, the first position information of the wireless positioning beacon can be obtained through one or any combination of satellite positioning systems such as the GPS (Global Positioning System), Beidou satellite navigation system, Galileo satellite positioning system, GLONASS (Global Navigation Satellite System) satellite navigation system, etc. Those skilled in the art can also use other available satellite positioning systems to obtain the first position information of the wireless positioning beacon, which is not limited herein. In some embodiments, there is a first positioning error between the first position information determined based on the satellite positioning system and the actual position information of the wireless positioning beacon in the real environment. The above first positioning error may be caused by the interference of the broadcast signal from the satellite to the wireless positioning beacon by the atmosphere or other environmental factors during the transmission process; it may also be caused by error factors such as a small number of satellites included in the satellite positioning system, clock asynchronization between the atomic clocks of different satellites and the internal clock of the wireless positioning beacon, and satellite orbit error, which is not limited herein. It can be understood that the error factors causing the first positioning error include environmental error factors, system error factors corresponding to the satellite positioning system, and system error factors corresponding to the wireless positioning beacon. These error factors are often difficult to be completely overcome during the actual use of the wireless positioning beacon, resulting in the first positioning error not being able to be completely eliminated in the actual application scenario. In some embodiments, the positioning accuracy of the first position information determined based on the satellite positioning system is generally in the range of meters to tens of meters, that is, the deviation between the first position information determined by the satellite positioning system and the actual position information of the wireless positioning beacon in the real environment is usually in the range of one meter to ten meters.

[0037] In some embodiments, the second position information of the drone can be determined based on a satellite positioning system. For example, the drone can be equipped with a receiver module that can receive the broadcast signals of the satellite positioning system. The receiver module calculates and determines the second position information of the drone by using the time difference of the broadcast signals broadcast by different satellites in the satellite positioning system, which carry their own position information and broadcast time information. In some embodiments, the second position information of the drone can also be obtained through one or any combination of satellite positioning systems such as the GPS satellite positioning system, the Beidou satellite navigation system, the Galileo satellite positioning system, and the GLONASS satellite navigation system. Those skilled in the art can also use other available satellite positioning systems to obtain the second position information of the drone, which is not limited herein. In some embodiments, there is a second positioning error between the second position information determined based on the satellite positioning system and the actual position information of the drone in the real environment. The above-mentioned second positioning error may be caused by the interference of the broadcast signal by the atmosphere or other environmental factors during the transmission from the satellite to the wireless positioning beacon; it may also be caused by factors such as a small number of satellites in the satellite positioning system, the clock asynchronization between the atomic clocks of different satellites and the internal clock of the wireless positioning beacon, and satellite orbit errors, which are not limited herein. It can be understood that the error factors causing the second positioning error include environmental error factors, system error factors corresponding to the satellite positioning system, and system error factors corresponding to the drone. These error factors are often difficult to be completely overcome during the actual use of the drone, resulting in the second positioning error not being completely eliminated in the actual application scenario. In some embodiments, the positioning accuracy of the second position information determined based on the satellite positioning system is generally in the range of meters to tens of meters, that is, the deviation between the second position information determined by the satellite positioning system and the actual position information of the drone in the real environment is usually within the range of one meter to ten meters.

[0038] In some embodiments, in order to further improve the acquisition accuracy of the second position information of the unmanned aerial vehicle, the Differential Global Positioning System (DGPS) algorithm or the Real-Time Kinematic (RTK) algorithm may be used to obtain the second position information of the unmanned aerial vehicle with higher positioning accuracy on the basis of the satellite positioning system. In some embodiments, the DGPS algorithm may receive the broadcast signal sent by the satellite positioning system by using a fixed base station set on the ground, compare the known base station position information with the base station positioning position obtained based on the satellite positioning system to determine the error correction information, and provide the error correction information to the unmanned aerial vehicle. During the acquisition of the second position information, the error correction information can be used to correct error factors such as the atmospheric delay of the satellite broadcast signal and the satellite orbit error to obtain the second position information with higher accuracy. In some embodiments, the RTK algorithm may be based on the carrier phase measurement of the ground base station and provide the carrier phase information to the unmanned aerial vehicle to achieve the error correction of the second position information. In some embodiments, those skilled in the art may also obtain the second position information of the unmanned aerial vehicle based on other implementable unmanned aerial vehicle positioning methods such as visual optical flow positioning and inertial navigation positioning, which are not limited herein.

[0039] In some embodiments, the acquisition sources of the first position information and the second position information include but are not limited to: the absolute longitude and latitude coordinates determined based on the satellite positioning system by deploying positioning beacons and antenna modules on the wireless positioning beacons and the unmanned aerial vehicle; the displacement obtained by integrating the absolute speed measurement of the satellites in the satellite positioning system within the time period corresponding to a single sliding window, etc., which are not limited herein. In some embodiments, the first position information and the second position information may be determined based on the fusion of the positioning data provided by multiple positioning information sources.

[0040] In some embodiments, the measured distance information between the wireless positioning beacon and the drone can be obtained through the antenna array carried by the drone. For example, in a specific instance, the drone is equipped with an antenna array for obtaining the broadcast signal emitted by the wireless positioning beacon. According to the time difference of the broadcast signal received by each antenna in the antenna array, the azimuth and relative distance of the drone relative to the wireless positioning beacon can be obtained. It can be understood that when using the antenna array to obtain the measured distance information between the wireless positioning beacon and the drone, it is necessary to carry an antenna array with a certain volume and mass on the drone to receive the broadcast signal emitted by the wireless positioning beacon, which is difficult to deploy and implement on a miniaturized drone. In some embodiments, the wireless positioning beacon can be a Bluetooth positioning beacon, and the measured distance information between the wireless positioning beacon and the drone can be determined and obtained through Bluetooth channel sounding technology. Among them, Bluetooth channel sounding technology is a technology for evaluating the characteristics of wireless channels. It can measure and analyze channel-related parameter information such as the frequency response, delay characteristics, and multipath effects of different Bluetooth communication channels to optimize the wireless communication performance between Bluetooth communication devices. In some embodiments, Bluetooth channel sounding technology is the technical support included in the Bluetooth 6.0 version released by the Bluetooth Special Interest Group (SIG): when the drone and the wireless positioning beacon are equipped with Bluetooth signal transceiver modules that support the Bluetooth 6.0 version, Bluetooth communication connection can be established between the drone and the wireless positioning beacon. At this time, high-precision distance between the wireless positioning coordinates and the drone can be obtained through Bluetooth channel sounding technology. In some embodiments, using Bluetooth channel sounding technology, the measured distance information between the wireless positioning beacon and the drone can be accurately obtained according to information such as the received Bluetooth signal strength and the time difference from the Bluetooth signal emission to the Bluetooth signal reception. In some embodiments, the measurement accuracy of the measured distance information determined based on Bluetooth channel sounding technology is generally in the range of centimeter level to sub-meter level, that is, the deviation between the measured distance information determined by Bluetooth channel sounding technology and the actual distance of the wireless positioning beacon and the drone in the real environment is usually in the range of one centimeter to ten centimeters. In some embodiments, the measured distance information between the wireless positioning beacon and the drone can also be determined based on the point-to-point ranging technology, millimeter wave ranging technology, etc. in the UWB ultra-wideband connection; the ranging technology used in the process of obtaining the measured distance information can include ranging technology based on phase difference, ranging technology based on round trip time, etc., which will not be limited here. In some embodiments, the measured distance information between the wireless positioning beacon and the drone can be obtained by combining or fusing multiple ranging modules using different ranging principles or different ranging methods, which will not be limited here.

[0041] Step 220: Determine the relative error vector of the UAV relative to the wireless positioning beacon based on the measured distance information, the first position information, and the second position information. In some embodiments, step 220 may be implemented by the error determination module 420.

[0042] In some embodiments, since there are corresponding positioning errors in the acquisition processes of the first position information and the second position information, it is difficult to directly obtain the relative position information of the UAV relative to the wireless positioning beacon based on the coordinate difference between the first position information and the second position information, which affects the positioning accuracy of the UAV in the close-range scenario application. In some embodiments, considering that the measurement accuracy of the measured distance information of the UAV relative to the wireless positioning beacon is relatively high, the measured distance information can be used to correct the relative position information of the UAV relative to the wireless positioning beacon. In some embodiments, taking the wireless positioning beacon as a Bluetooth positioning beacon as an example, the measurement error between the measured distance value and the actual distance value between the UAV and the Bluetooth positioning beacon is less than the first positioning error between the first position information and the actual position of the wireless positioning beacon, and less than the second positioning error between the second position information and the actual position of the UAV. Therefore, the measured distance value can be used to determine the relative error vector of the UAV relative to the wireless positioning beacon. The specific determination method of the relative error vector will be further described later and will not be elaborated here.

[0043] Step 230: Determine the relative position information between the UAV and the wireless positioning beacon based on the relative error vector.

[0044] In some embodiments, on the basis of determining the relative error vector, the positioning relative information of the UAV relative to the wireless positioning beacon can be first determined according to the first position information and the second position information, and then the positioning relative information can be corrected by the relative error vector to obtain the relative position information between the UAV and the wireless positioning beacon. The above relative position information can reflect the accurate relative position between the UAV and the wireless positioning beacon after being corrected by the relative error vector.

[0045] Based on the UAV positioning method provided by the foregoing process 200, the positioning information of the wireless positioning beacon and the UAV can be corrected according to the high-precision distance information between the UAV and the wireless positioning beacon to determine the high-precision relative position information between the UAV and the wireless positioning beacon, which can improve the positioning accuracy of the UAV and the accuracy of flight trajectory planning in the close-range scenario application of the UAV. The following will further explain and illustrate the specific implementation of the above process 200 in combination with specific embodiments.

[0046] In some embodiments, during the process of obtaining the first position information, the second position information, and the measured distance information, based on a preset sliding window size, the position coordinates of the wireless positioning beacons, the position coordinates of the unmanned aerial vehicle (UAV), and the measured distance values between the wireless positioning beacons and the UAV corresponding to multiple acquisition points included in each sliding window can be obtained. In some embodiments, by adopting the method of sliding window sampling to obtain the first position information, the second position information, and the measured distance information, the position changes of the UAV and the wireless positioning beacons can be continuously obtained, and the relative position information can be continuously updated according to the position changes. In some embodiments, the first position information may include the position coordinates of multiple wireless positioning beacons corresponding to a single sliding window; the second position information may include the position coordinates of multiple UAVs corresponding to a single sliding window; the measured distance information may include multiple measured distance values corresponding to a single sliding window. For example, if the sliding window size includes three acquisition points t1, t2, and t3, for a selected sliding window, the first position information may include the position coordinates of the first wireless positioning beacon obtained at acquisition point t1, the position coordinates of the second wireless positioning beacon obtained at acquisition point t2, and the position coordinates of the third wireless positioning beacon obtained at acquisition point t3. The second position information may include the position coordinates of the first UAV obtained at acquisition point t1, the position coordinates of the second UAV obtained at acquisition point t2, and the position coordinates of the third UAV obtained at acquisition point t3. The measured distance information may include the first measured distance value obtained at acquisition point t1, the second measured distance value obtained at acquisition point t2, and the third measured distance value obtained at acquisition point t3, which are not limited herein. In some embodiments, the acquired data corresponding to each acquisition point in the sliding window are all clock-calibrated and time-stamp information is aligned, which can reflect the position relationship and relative position relationship between the UAV and the wireless positioning beacon at the same moment.

[0047] Figure 3 It is an exemplary flowchart of determining the relative error vector of the UAV relative to the wireless positioning beacon according to some embodiments of this specification. In some embodiments, as Figure 3 shown, process 300 may include the following steps.

[0048] Step 310: Based on the first position information and the second position information, determine multiple calculated distance vectors of the UAV relative to the wireless positioning beacon corresponding to the first sliding window.

[0049] In some embodiments, the first position information and the second position information may be obtained by means of sliding window sampling based on a preset sliding window size. In some embodiments, those skilled in the art may set the preset sliding window size according to historical experience values. For example, the preset sliding window size may be set to 0.3 seconds, 0.5 seconds, 1 second, etc., which is not limited herein. In some embodiments, the number of acquisition points included in a single sliding window is related to the positioning frequencies of the first position information and the second position information. For example, if the positioning frequencies of the first position information and the second position information determined based on the satellite positioning system are 10 Hz, it means that the position information of the wireless positioning beacon and the drone is obtained every 0.1 seconds. When the size of a single sliding window is 1 second, the number of acquisition points included in a single sliding window is 10; when the size of a single sliding window is 0.3 seconds, the number of acquisition points included in a single sliding window is 3, which is not limited herein. In some embodiments, taking the sliding window size including three acquisition points t1, t2, and t3 as an example, for a selected first sliding window, the first position information may include the first wireless positioning beacon position coordinates obtained by acquiring point t1, the second wireless positioning beacon position coordinates obtained by acquiring point t2, and the third wireless positioning beacon position coordinates obtained by acquiring point t3. The second position information may include the first drone position coordinates obtained by acquiring point t1, the second drone position coordinates obtained by acquiring point t2, and the third drone position coordinates obtained by acquiring point t3. On this basis, the calculated distance vectors corresponding to the respective acquisition points of the first sliding window may be determined in sequence. For example, the calculated distance vector corresponding to acquisition point t1 may be a vector pointing from the first wireless positioning beacon position coordinates to the first drone position coordinates (or from the first drone position coordinates to the first wireless positioning beacon position coordinates). The calculated distance vector corresponding to acquisition point t2 may be a vector pointing from the second wireless positioning beacon position coordinates to the second drone position coordinates (or from the second drone position coordinates to the second wireless positioning beacon position coordinates). The calculated distance vector corresponding to acquisition point t3 may be a vector pointing from the third wireless positioning beacon position coordinates to the third drone position coordinates (or from the third drone position coordinates to the third wireless positioning beacon position coordinates). The above calculated distance vectors can reflect the relative position information of the drone relative to the wireless positioning beacon obtained based on the positioning information.

[0050] Step 330: Establish an objective function associated with the relative error vector.

[0051] In some embodiments, considering that in the process of obtaining the first position information of the wireless positioning beacon based on the satellite positioning system, although there is a positioning error between the first position information and the actual position of the wireless positioning beacon, the above positioning error is jointly caused by one or a combination of environmental error factors, system error factors corresponding to the satellite positioning system, and system error factors corresponding to the wireless positioning beacon. The above error factors are often fixed or slowly drift and change during the use of the wireless positioning beacon. Therefore, it can be determined that during the continuous acquisition of the first position information of the wireless positioning beacon, the first positioning error corresponding to the first position information can be fixed or slowly drift and change; similarly, the second positioning error corresponding to the second position information of the unmanned aerial vehicle can also be fixed or slowly drift and change. In some embodiments, when both the first positioning error and the second positioning error generated based on the positioning information can be considered fixed or slowly drift and change, the relative error vector of the unmanned aerial vehicle relative to the wireless positioning beacon can be considered fixed or slowly drift and change, that is, the relative error vector can be a fixed constant vector, or can be a fixed constant vector in a sliding window.

[0052] In some embodiments, a target function can be established based on the relative error vector, where the relative error vector can be represented by a fixed constant vector. In some embodiments, the established target function can be related to the difference between the norm of the calculated distance vector corresponding to the first sliding window after being corrected by the relative error vector and the measured distance value. In some embodiments, the target function can be implemented based on the following mathematical expression.

[0053]

[0054] Where, is the target function, N is the number of acquisition points corresponding to the first sliding window, P rc,i is the position information of the wireless positioning beacon corresponding to the i-th acquisition point in the first sliding window, P robot,i is the position information of the unmanned aerial vehicle corresponding to the i-th acquisition point in the first sliding window, P rc,i -P robot,i is the calculated distance vector corresponding to the i-th acquisition point in the first sliding window, p e is the relative error vector, ||P rc,i -P robot,i -p e || is the norm of the calculated distance vector corresponding to the i-th acquisition point in the first sliding window after being corrected by the relative error vector, d i is the measured distance value corresponding to the i-th acquisition point in the first sliding window.

[0055] Step 330: According to the measured distance value corresponding to the first sliding window and the calculated distance vector, use a non-linear optimization algorithm to optimize the objective function, and determine the relative error vector corresponding to the first sliding window according to the optimized objective function.

[0056] In some embodiments, when the objective function is related to the difference between the norm of the calculated distance vector corresponding to the first sliding window after being corrected by the relative error vector and the measured distance value, the non-linear optimization of the objective function can be achieved by adjusting the relative error vector, so that the difference between the norm of the calculated distance vector corresponding to the first sliding window after being corrected by the adjusted relative error vector and the measured distance value is minimized, that is, the value of the objective function is minimized. It can be understood that the smaller the difference between the norm of the calculated distance vector after being corrected by the relative error vector and the measured distance value, the closer the calculated distance vector is to the actual relative position between the UAV and the wireless positioning beacon after being corrected by the relative error vector. The relative error vector determined by the above optimization method can be used to accurately correct the positioning errors of the UAV and the wireless positioning beacon.

[0057] In some embodiments, the non-linear optimization algorithm for the objective function can include the sequential quadratic programming algorithm and the quasi-Newton algorithm. Among them, the sequential quadratic programming algorithm (SQP) is an effective algorithm for solving non-linear optimization problems, especially suitable for optimization problems with constraints, and gradually approaches the optimal solution by solving a quadratic programming (QP) sub-problem in each iteration; the quasi-Newton algorithm is a class of iterative algorithms for solving unconstrained optimization problems, especially suitable for finding the local minimum of the objective function. It does not need to calculate the Hessian matrix of the objective function, but improves the calculation and optimization efficiency by gradually updating an approximate Hessian matrix. In some embodiments, those skilled in the art can also use other suitable algorithms to achieve the non-linear optimization of the objective function, which is not limited here.

[0058] In some embodiments, during the process of optimizing the objective function using a non - linear optimization algorithm, the objective function can be optimized according to the measured distance values corresponding to the first sliding window, the calculated distance vector, and the preset error vector. The preset error vector can be set based on the historical positioning experience values between the UAV and the wireless positioning beacon to accelerate the optimization calculation process of the objective function. In some embodiments, considering that the relative error vector of the UAV with respect to the wireless positioning beacon can be considered to change slowly and drift, the historical relative error vector corresponding to the previous sliding window can be used as the initial value of the relative error vector of the current sliding window. Instead of using the preset error vector, the objective function is optimized using a non - linear optimization algorithm, which can accelerate the optimization calculation process of the objective function and make the determined relative error vector change smoothly.

[0059] Some embodiments of this specification also provide a UAV positioning system based on wireless positioning beacons. Figure 4 It is a schematic diagram of the functional modules of a UAV positioning system based on wireless positioning beacons shown in some embodiments of this specification. In some embodiments, as Figure 4 shown, the UAV positioning system 400 can include a position acquisition module 410, an error determination module 420, and a positioning correction module 430. In some embodiments, each module of the UAV positioning system 400 can be configured in the processing device 130.

[0060] In some embodiments, the position acquisition module 410 is used to acquire the position information of the wireless positioning beacon and the UAV. In some embodiments, the position acquisition module 410 can be used to acquire the first position information of the wireless positioning beacon, the second position information of the UAV, and the measured distance information between the wireless positioning beacon and the UAV respectively. In some embodiments, the position acquisition module 410 can adopt a sliding - window sampling acquisition method. Based on the preset sliding - window size, it acquires the position coordinates of the wireless positioning beacon, the position coordinates of the UAV, and the measured distance values corresponding to multiple acquisition points included in each sliding window.

[0061] In some embodiments, the error determination module 420 is used to determine the relative position error between the wireless positioning beacon and the UAV due to positioning errors. In some embodiments, the error determination module 420 can be used to determine the relative error vector of the UAV relative to the wireless positioning beacon based on the measured distance information, the first position information, and the second position information. In some embodiments, the error determination module 420 can establish an objective function associated with the relative error vector and optimize the objective function using a non - linear optimization algorithm to determine the relative error vector.

[0062] In some embodiments, the positioning correction module 430 may be used to determine the relative position information between the drone and the wireless positioning beacon based on the relative error vector. The relative position information can reflect the accurate relative position between the drone and the wireless positioning beacon after being corrected by the relative error vector.

[0063] For more content about each module, reference can be made to Figures 2 to 3 the relevant description, which will not be elaborated here. It should be understood that Figure 4 The system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented through hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above methods and systems can be implemented using computer-executable instructions and / or control codes included in a processor. For example, such codes are provided in carrier media such as magnetic disks, CDs, or DVD-ROMs, or in the memories of programmable devices. The system and its modules in this specification can be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips and transistors, or programmable hardware devices such as field programmable gate arrays and programmable logic devices, but also by software implemented by various types of processors, or by a combination of the above hardware circuits and software (e.g., firmware).

[0064] It should be noted that the above description of the system and its modules is only for convenience of description and does not limit this specification to the scope of the exemplified embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, they may, without departing from this principle, arbitrarily combine the various modules to form a subsystem connected to other modules. Or split some modules to obtain more modules or multiple units under that module. Such variations are all within the scope disclosed in this specification.

[0065] Some embodiments of this specification also provide a drone positioning device based on a wireless positioning beacon, which includes a processor and a storage medium. The storage medium stores computer program instructions, and the processor is used to execute at least part of the computer program instructions to implement the drone positioning method based on a wireless positioning beacon provided in the foregoing embodiments of this specification.

[0066] Some embodiments of this specification also provide a computer program product, including computer instructions or a computer program. When at least part of the computer instructions or the computer program is executed by a processor, it can implement the drone positioning method based on a wireless positioning beacon provided in the foregoing embodiments of this specification.

[0067] In some embodiments, the above-mentioned processor may be a combination of one or more of the following processors: central processing unit (CPU), application-specific integrated circuit (ASIC), application-specific instruction set processor (ASIP), graphics processing unit (GPU), physics processing unit (PPU), digital signal processor (DSP), field-programmable gate array (FPGA), programmable logic device (PLD), programmable logic controller (PLC), reduced instruction set computer (RISC), microprocessor, etc. In some embodiments, the processor may be the processing device 130.

[0068] The beneficial effects that the embodiments of this specification may bring include but are not limited to: (1) It is possible to correct the positioning information of the wireless positioning beacon and the drone according to the high-precision distance information between the drone and the wireless positioning beacon, so as to determine the high-precision relative position information between the drone and the wireless positioning beacon, and it is possible to improve the positioning accuracy of the drone and the accuracy of flight trajectory planning in the close-range scenario application of the drone. (2) In the process of obtaining the high-precision relative position information between the drone and the wireless positioning beacon, the Bluetooth channel detection technology is used for determination, which does not rely on expensive or large-sized antenna arrays and is more suitable for small drones. It should be noted that the beneficial effects that different embodiments may produce are different. In different embodiments, the beneficial effects that may be produced may be any one or several combinations of the above, or any other beneficial effects that may be obtained.

[0069] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are taught in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

Claims

1. A method for positioning an unmanned aerial vehicle based on a wireless positioning beacon, characterized in that The method includes: Obtaining first position information of a wireless positioning beacon, second position information of a drone, and measurement distance information between the wireless positioning beacon and the drone; Determining a relative error vector of the drone relative to the wireless positioning beacon based on the measurement distance information, the first position information, and the second position information; Determining relative position information between the drone and the wireless positioning beacon based on the relative error vector.

2. The method according to claim 1, wherein The wireless positioning beacon includes a Bluetooth positioning beacon; There is a Bluetooth communication connection between the drone and the Bluetooth positioning beacon, and the measurement distance information includes a measurement distance value between the drone and the Bluetooth positioning beacon obtained through Bluetooth channel detection technology.

3. The method according to claim 2, characterized in that, The measurement error between the actual distance value between the drone and the Bluetooth positioning beacon and the measurement distance value is less than a first positioning error between the first position information and the actual position of the wireless positioning beacon; and The measurement error is less than a second positioning error between the second position information and the actual position of the drone.

4. The method according to claim 1, characterized in that The first position information of the wireless positioning beacon is determined based on a satellite positioning system; and / or The second position information of the drone is determined based on the satellite positioning system.

5. The method according to claim 1, wherein The obtaining of the first position information of the wireless positioning beacon, the second position information of the drone, and the measurement distance information between the wireless positioning beacon and the drone includes: Based on a preset sliding window size, obtaining wireless positioning beacon position coordinates, drone position coordinates, and measurement distance values corresponding to a plurality of acquisition points included in each sliding window; The first position information includes a plurality of the wireless positioning beacon position coordinates corresponding to a single sliding window; The second position information includes a plurality of the drone position coordinates corresponding to a single sliding window; The measurement distance information includes a plurality of the measurement distance values corresponding to a single sliding window.

6. The method according to claim 5, wherein The determining of the relative error vector of the drone relative to the wireless positioning beacon based on the measurement distance information, the first position information, and the second position information includes: Determining a plurality of calculated distance vectors of the drone relative to the wireless positioning beacon corresponding to a first sliding window based on the first position information and the second position information; Establishing an objective function associated with the relative error vector; According to the measurement distance value and the calculated distance vector corresponding to the first sliding window, using a non-linear optimization algorithm to optimize the objective function, and determining the relative error vector corresponding to the first sliding window according to the optimized objective function.

7. The method according to claim 6, wherein The objective function is associated with the difference between the modulus of the calculated distance vector corresponding to the first sliding window after being corrected by the relative error vector and the measurement distance value; The optimization of the objective function using the non - linear optimization algorithm includes: adjusting the relative error vector to minimize the difference between the norm of the calculated distance vector corresponding to the first sliding window after being corrected by the adjusted relative error vector and the measured distance value.

8. The method according to claim 6, wherein The optimization of the objective function using the non - linear optimization algorithm and determining the relative error vector corresponding to the first sliding window according to the optimized objective function includes: Optimizing the objective function using the non - linear optimization algorithm according to the measured distance value, the calculated distance vector, and the preset error vector corresponding to the first sliding window, and determining the relative error vector corresponding to the first sliding window according to the optimized objective function; or Optimizing the objective function using the non - linear optimization algorithm according to the measured distance value, the calculated distance vector corresponding to a single sliding window, and the historical relative error vector corresponding to the previous sliding window, and determining the relative error vector corresponding to the first sliding window according to the optimized objective function.

9. A drone positioning system based on a wireless positioning beacon, characterized in that, The system includes: A position acquisition module for acquiring the first position information of the wireless positioning beacon, the second position information of the unmanned aerial vehicle, and the measured distance information between the wireless positioning beacon and the unmanned aerial vehicle; An error determination module for determining the relative error vector of the unmanned aerial vehicle relative to the wireless positioning beacon based on the measured distance information, the first position information, and the second position information; A positioning correction module for determining the relative position information between the unmanned aerial vehicle and the wireless positioning beacon based on the relative error vector.

10. A computer program product, including computer instructions or a computer program, when at least part of the computer instructions or the computer program is executed by a processor, can implement the method for positioning an unmanned aerial vehicle based on a wireless positioning beacon as described in any one of claims 1 to 8.

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