A relative positioning method between unmanned aerial vehicles and unmanned vehicles

Abstract

This paper proposes a relative positioning method for drones and unmanned vehicles. Instead of strategically placing multiple anchor points in the operating area, both drones and unmanned vehicles carry multiple UWB antennas. This method obtains the corresponding distance and azimuth between the drone and the vehicle, and combines this with altitude estimation to estimate the relative position of the drone and the vehicle. Furthermore, a system for UAV-UGV collaboration is proposed, integrating barometer and optical correlation flow data into an extended Kalman filter to provide sufficiently accurate and stable relative position data for feedback-controlled flight.

Description

Technical Field

[0001] The present invention belongs to the technical field of relative positioning, and in particular relates to a relative positioning method between an unmanned aerial vehicle (UAV) and an unmanned vehicle (UAV). Background Art

[0002] Relative positioning is a challenging task in the study of UAV (UAV) and unmanned vehicle (UAV) platooning, requiring formation members to obtain their relative positions in real time. Most existing relative positioning algorithms utilize wireless communication and inertial navigation technologies. However, due to their susceptibility to multipath interference, positioning accuracy is difficult to improve, and cumulative errors also affect positioning accuracy. Although some methods have been proposed to reduce the impact of cumulative errors, positioning systems based on inertial navigation are still unable to avoid the effects of excessive cumulative errors during long-term operation. Developing a method that can effectively match different distance information with azimuth angles is crucial for relative positioning. Summary of the Invention

[0003] In response to the influence of multipath interference and cumulative error, the present invention proposes a relative positioning method between UAVs and unmanned vehicles. It uses UWB communication technology combined with TOA ranging to estimate the relative distance, making the result more accurate and stable.

[0004] A relative positioning method for a UAV and an UGV, wherein the UAV and the UGV communicate via UWB, includes the following steps:

[0005] Step 1: Estimate the relative height between the UAV and the UGV ;

[0006] Step 2: Estimate the relative distance between the UAV and the UGV ;

[0007] Step 3: Estimate the relative direction between the UAV and the UGV , specifically:

[0008] Control the UAV to rotate in place for at least one circle. During the rotation, the TOA distance value d between the UAV and the UGV is measured and recorded by the measuring equipment equipped on the UAV and the UGV. At the same time, the azimuth of the UAV is recorded. , and obtain a series of one-to-one corresponding azimuth angles and TOA distance value d, and find the minimum TOA distance value ; Calculate the distance value template curve from this:

[0009]

[0010] Where r is the radius of rotation, is the theoretical angle between UAV and UGV, = +r is the distance between the UAV and the UGV;

[0011] Obtain the theoretical angle based on the distance measurement value template curve and theoretical distance value The corresponding sequence P1= , where m is the number of data sampling points during the rotation process;

[0012] The distance value between two TOA ranging nodes is obtained as follows:

[0013]

[0014] in, =d+r represents the actual distance between the UAV and the UGV, d is the distance between the electronic compass on the UAV and the UGV, is the sensor noise, θ is the direction angle measured by the built-in measurement device, the measured angle θ and the TOA ranging value The corresponding sequence P2= , where n is the amount of data collected during one rotation;

[0015] By calculating the ranging sequence value of sequence P2 and the ranging sequence value in sequence P1 The sum of the squares of the distances between the sequences P2 and P1 is minimized to obtain the matching translation k.

[0016] Assume that in the corresponding sequence P1, the UAV and UGV are facing the t-th data point in P1, then the relative direction estimation result is is the first Data points corresponding to the direction angle ,Right now: .

[0017] Preferably, in step 1, the relative height between the UAV and the UGV is estimated. The methods include:

[0018] First, use the built-in pressure sensor in the UAV to obtain the atmospheric pressure P. According to the relationship between atmospheric pressure P and height h:

[0019]

[0020] The height difference h is calculated according to the above formula, which is the relative height between the UAV and the UGV. .

[0021] in, is the standard atmospheric pressure; is the standard temperature of gas volume flow rate; represents the standard temperature lapse rate; g represents the acceleration due to gravity; M is the molar mass of dry air; is the universal gas constant.

[0022] Preferably, the relative distance between the UAV and the UGV is estimated in step 2. The methods include:

[0023] Calculate the signal propagation delay based on the time information in the data packet ;Will Multiplying by the propagation speed c, the relative distance between UAV and UGV can be obtained .

[0024] The state vector of the UAV is selected using the Extended Kalman Filter (EKF) method:

[0025] .

[0026] The present invention has the following beneficial effects:

[0027] This paper provides a relative positioning method for drones and unmanned vehicles. Both drones and unmanned vehicles carry multiple UWB antennas, rather than strategically placing multiple anchor points in the operating area. This method obtains the corresponding values ​​of the distance and azimuth between the drone and the unmanned vehicle, and combines this with altitude estimation to estimate the relative position of the drone and the unmanned vehicle. Furthermore, a system for UAV-UGV cooperative scenarios is proposed, integrating barometer and optical correlation flow data into an extended Kalman filter to provide sufficiently accurate and stable relative position data for feedback-controlled flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a relative height flow chart.

[0029] Figure 2 This is the principle diagram of two-way ranging.

[0030] Figure 3 This is a schematic diagram of the correspondence between distance and azimuth.

[0031] Figure 4 (a) and Figure 4 (b) are distance errors. and azimuth error Simulation results of the impact on relative positioning error. DETAILED DESCRIPTION

[0032] The present invention utilizes UWB communication technology in combination with TOA ranging to estimate relative distance. Both UAV and UGV carry multiple UWB antennas, instead of strategically placing multiple anchor points in the operating area, so that the corresponding values ​​of the distance and azimuth between UAV and UGV are obtained, and combined with the altitude estimation to perform UAV-UGV relative position estimation. In addition, we provide a system for UAV-UGV cooperation scenarios, and integrate the barometer and optical correlation flow data into the extended Kalman filter to provide sufficiently accurate and stable relative position data for feedback control flight. In the present invention, a relative positioning system between UAV and UGV is developed using UWB distance measurement and other airborne sensors. From the simulation results in Figure 4, it is found that this method can better achieve all-round position estimation, the positioning accuracy is less sensitive to the communication distance, and the accuracy is higher.

[0033] 1. Relative height estimation

[0034] The present invention mainly uses the MS5534B air pressure sensor installed on the UAV to monitor the air pressure at the altitude of the UAV in real time, calculates the air pressure data measured by the sensor through M0, and converts it into basic altitude data.

[0035] The MS5534B pressure sensor is an integrated circuit combining a piezoresistive pressure sensor and an ADC interface. This sensor offers high pressure accuracy, a competitive price, and excellent value. Its key components include an altitude sensor, an LPC1114 microcontroller, an FT52 STD wireless module, and a TTL-to-USB converter.

[0036] First, use the built-in pressure sensor in the UAV to estimate the relative height between the UAV and the UGV. Assuming the UGV is at an altitude of h2 and the UAV is at an altitude of h1, and the height difference between the two is h, the relative height difference can be calculated based on the relationship between atmospheric pressure p and altitude h:

[0037]

[0038] in, It is the standard atmospheric pressure, about 101.3KPa; is the standard temperature of gas volume flow, which is about 288.2K; represents the standard temperature lapse rate, which is approximately -0.01K / m; g represents the acceleration due to gravity, which is approximately 9.8m / s2; M is the molar mass of dry air, which is approximately 0.03Kg / mol; is the universal gas constant, which is 8.3 J / (mol·K).

[0039] Finally, the height difference h obtained by the above formula is the relative height between the UAV and UGV.

[0040] 2. Relative Distance Estimation

[0041] The two-way ranging method uses the round-trip time between the sending and receiving nodes to estimate the distance between the two nodes when the UAV and UGV are not precisely synchronized. Each node in the two-way ranging method has duplex communication capabilities.

[0042] Assume that A is a UAV that sends a time-stamped UWB radio message to a UGV at time m1. When the UGV receives message A, it records the current time n1 as B. To avoid UAV reception conflicts, the UGV delays a certain time slot t1 and then sends the reply message, the UGV's reception time n1, and the message transmission time n2 to the UAV. The UAV receives the data packet sent by the UGV at time m2 and then calculates the signal propagation delay based on the time information in the data packet. .Will Multiplying by the propagation speed c, we can get the distance d between the UAV and UGV.

[0043] Then, the measurement point UGV is projected onto the height plane where the UAV is located to perform relative distance According to the TOA ranging principle, the distance between the two is:

[0044]

[0045] in, is the transmission time of the UWB ranging signal, c is the propagation speed of the signal in the air, which is approximately m / s.

[0046] Bidirectional ranging can shield the impact of clock asynchrony between the UAV and UGV, improving ranging accuracy. At the same time, the complexity of the positioning system based on bidirectional ranging can be greatly reduced, making it more practical.

[0047] 3. Relative Direction Estimation

[0048] Utilizing time-of-arrival (TOA) ranging technology, this paper proposes a relative direction identification method that combines distance and bearing. Using measurement equipment carried by both the UAV and UGV, the TOA ranging value between them is measured to estimate the relative distance between the UAV and UGV. The UAV equipped with the measurement equipment rotates in place. During this rotation, the distance between the two TOA measurement nodes changes regularly. After the rotation is complete, a continuous, regularly varying TOA ranging value is obtained.

[0049] Theoretically, when the UAV faces the UGV, the ranging value will reach a local minimum; when the UAV gradually deviates from the straight line direction with the UGV until it faces away from the UGV, the ranging value will gradually increase and reach a local maximum due to factors such as occlusion and orientation; when the UAV's orientation gradually approaches the facing direction from the opposite direction of the UGV, it will show the opposite change to the above. The TOA ranging result will gradually change with rotation, and the overall change trend will reach a minimum when facing the UGV and a maximum when facing away from the UGV. While performing the TOA measurement, the RSSI was measured, but the measurement results contained a large amount of errors, and the regularity of the rotation measurement was not obvious enough. The feasibility and reliability of the relative direction identification using a combination of distance and orientation in the present invention were further verified later. Where d is the TOA ranging value, The azimuth is the angle between the azimuth and the geomagnetic north pole measured by the built-in electronic compass of the measuring device. It corresponds one-to-one with the TOA ranging value d.

[0050] In progress When matching with TOA distance value, the generation of template sequence is very important. The matching template sequence is to use the angle value corresponding to the theoretical direction , rotation radius r and TOA ranging minimum value , a series of distance and angle theoretical values ​​are generated. Theoretically, when the UAV turns to face the UGV, the distance between the measuring device and the UGV is the smallest; in practical applications, although there is a certain TOA ranging error, the minimum distance can still be measured. The approximate distance between the two is more reliable than random values ​​and is more conducive to the correspondence of subsequent measurements. Figure 3 From the parameters shown in and the preliminary calculation results, we can know that the ranging value template curve is:

[0051]

[0052] Where r is the radius of rotation, is the theoretical angle, = + r is the distance between UAV and UGV. When UAV is facing UGV, =0°, the direction angle measured by the electronic compass is ; When the rotating person faces away from the positioning target, =180°, the corresponding direction angle measured by the electronic compass is Therefore, the theoretical angle can be obtained. and theoretical distance value The corresponding sequence P1= , where m is arrive The amount of data between.

[0053] In summary, it can be seen that the distance between two TOA ranging nodes is

[0054]

[0055] in, =d+r represents the actual distance between UAV and UGV, is the sensor noise. Measure the angle θ and TOA distance value The corresponding sequence , where n is the amount of data collected during one rotation, and θ is the direction angle measured by the built-in electronic compass.

[0056] By calculating the ranging sequence value of sequence P2 and the ranging sequence value in sequence P1 The sum of the squares of the distances between them is minimized to obtain the best matching mode:

[0057]

[0058] in, represents the sequence matching pattern, and k represents the translation of the measurement sequence.

[0059] Therefore, assuming that in the corresponding sequence P1, the UAV and the UGV are facing the t-th data point in P1, the relative direction estimation result is is the first Data points corresponding to the direction angle ,Right now: .

[0060] In summary, the main purpose of this invention is to estimate the relative position of UAV with respect to UGV by using air pressure sensor and UWB. The system requires multiple UWB nodes to be installed on both the UAV and the UGV, with the UGV acting as the responder and the UAV acting as the requester. The TOA mechanism limits the number of distance measurements that can be obtained at a given time, so asynchronous fusion of information sources is required to obtain a reliable estimate. The state vector of the UAV is selected using the Extended Kalman Filter (EKF) method as described below:

[0061]

[0062] in, is the relative direction of the UAV with respect to the UGV, is the relative distance between UAV and UGV, is the relative height of the UAV relative to the UGV.

[0063] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A relative positioning method between a UAV and an unmanned vehicle, characterized in that: The UAV and UGV communicate via UWB, including the following steps: Step 1: Estimate the relative height between the UAV and the UGV Step 2: Estimate the relative distance between the UAV and the UGV Step 3: Estimate the relative direction between the UAV and the UGV Specifically: Control the UAV to rotate in place for at least one revolution. During the rotation, the measurement equipment on the UAV and the UGV measure and record the TOA distance value d between the two. At the same time, record the azimuth angle θ of the UAV to obtain a series of one-to-one correspondences between the azimuth angle θ and the TOA distance value d, and find the minimum TOA distance value d. m ; Calculate the distance value template curve from this: Where r is the rotation radius, α is the theoretical angle between UAV and UGV, and s = d m +r is the distance between the UAV and the UGV; Obtain the theoretical angle α and theoretical distance value according to the distance measurement value template curve The corresponding sequence Where m is the number of data sampling points during the rotation process; The distance value between two TOA ranging nodes is obtained as follows: in, represents the actual distance between the UAV and the UGV, d is the distance between the electronic compass configured on the UAV and the UGV, e is the sensor noise, θ is the direction angle measured by the built-in measurement device, and the measurement angle θ and TOA ranging value The corresponding sequence Where n is the amount of data collected during one rotation; By calculating the ranging sequence value of sequence P2 and the ranging sequence value in sequence P1 The sum of the squares of the distances between the sequences P2 and P1 is minimized to obtain the matching translation k. Assume that in the corresponding sequence P1, the UAV and UGV are facing the t-th data point in P1, then the relative direction estimation result is is the direction angle θ corresponding to the t+kth data point in sequence P2 t+k ,Right now:

2. The relative positioning method between a UAV and an unmanned vehicle according to claim 1, characterized in that: In step 1, the relative height between the UAV and the UGV is estimated. The methods include: First, use the built-in pressure sensor in the UAV to obtain the atmospheric pressure P. According to the relationship between atmospheric pressure P and height h: The height difference h is calculated according to the above formula, which is the relative height between the UAV and the UGV. ; Where P0 is the standard atmospheric pressure; T0 is the standard temperature of the gas volume flow rate; L r represents the standard temperature lapse rate; g represents the acceleration due to gravity; M is the molar mass of dry air; and R0 is the universal gas constant.

3. The relative positioning method between a UAV and an unmanned vehicle according to claim 1, characterized in that: In step 2, the relative distance between the UAV and the UGV is estimated. The methods include: Calculate the signal propagation delay t based on the time information in the data packet p ; t p Multiplying by the propagation speed c, the relative distance between UAV and UGV can be obtained 4. The relative positioning method between a UAV and an unmanned vehicle according to claim 1, wherein: The state vector of the UAV is selected using the Extended Kalman Filter (EKF) method:

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