A delay compensation control method for precise landing of a drone

Abstract

The present application belongs to the field of unmanned aerial vehicle precise landing control, and specifically proposes an unmanned aerial vehicle precise landing delay compensation control method; first, the sampling time and output time of the on-board sensor of the unmanned aerial vehicle are obtained, the input and output time of the calculation module is calculated, and the input and output time of the control instruction transmission process is calculated, so as to obtain the delay time length of the whole process; then, the segmented approximate accumulation method is used to calculate the position of the unmanned aerial vehicle in the delay stage; then, the moving target position of the unmanned aerial vehicle is corrected; finally, according to the corrected target position, the speed and acceleration of the unmanned aerial vehicle are determined in combination with the rule base. Using the position compensation method, the position deviation of the unmanned aerial vehicle in the delay stage can be corrected, which provides a basis for position control. The fuzzy control strategy better suppresses the overshoot, reduces the horizontal position oscillation of the unmanned aerial vehicle during landing, and shortens the landing time.

Description

Technical Field

[0001] This invention belongs to the field of precision landing control for unmanned aerial vehicles (UAVs), specifically relating to a method for autonomous landing delay compensation control of UAVs, which is particularly suitable for landing control of small UAVs. Background Technology

[0002] Electric vertical takeoff and landing (VTOL) drones are rapidly developing and being applied in many fields. During autonomous missions, they often need to land precisely at designated locations, such as in last-mile delivery. Precise landing relies on multi-sensor fusion for detection and perception, using sensors including cameras, ultrasonic sensors, and laser sensors. This data is then processed to calculate the target position. However, multi-sensor fusion, processing, and transmission inevitably introduce latency, which in turn causes control lag, resulting in drone position oscillations during landing, increasing landing time, and raising the probability of landing failure.

[0003] Currently, among domestic technologies related to delay compensation control, patent CN112099532A discloses a delay compensation method and system for image-guided aircraft. This patent involves installing a strapdown seeker, guidance filter, autopilot, and angular rate gyroscope on the aircraft. These designs require the aircraft itself to have sufficient payload capacity. Furthermore, its delay compensation method involves a large computational load, placing high demands on the performance of the onboard computer and resulting in high power consumption; therefore, it is not suitable for the navigation control of small UAVs. An article published in the Journal of Dynamics and Control in February 2022 discloses a real-time indoor navigation system for UAVs based on visual delay compensation. This system compensates for visual data and fuses the compensated visual data with IMU data. While this technology improves positioning accuracy and real-time performance, it does not involve detailed flight control strategies and is not suitable for autonomous and precise landing of UAVs. Summary of the Invention

[0004] The purpose of this invention is to provide a method for autonomous landing delay compensation control of unmanned aerial vehicles (UAVs) that addresses the problem of repeated position oscillations during the terminal landing process, thereby improving landing speed and landing success rate.

[0005] The technical solution of this invention: To solve the above-mentioned technical problems, this invention proposes a method for autonomous landing delay compensation control of unmanned aerial vehicles, comprising the following steps:

[0006] Step S1: Obtain the sampling and output times of the UAV's onboard sensors, the input and output times of the calculation module, and the input and output times of the control command transmission process, thereby obtaining the entire process delay duration;

[0007] Step S2: Calculate the position of the UAV during the delay stage using a piecewise approximate accumulation method;

[0008] Step S3: Correct the position of the moving target of the UAV;

[0009] Step S4: Based on the corrected target position and the rule base, determine the speed and acceleration of the drone's movement.

[0010] Furthermore, in step S1, the delay duration of each stage can be obtained by using timestamps or modeling methods.

[0011] Furthermore, in step S1, the airborne sensors include at least a camera, an ultrasonic sensor, and a laser rangefinder.

[0012] Furthermore, in step S1, the total delay time is the cumulative result of the delay times of each step.

[0013] Furthermore, in step S2, the compensation process is as follows:

[0014] First, analyze the sampling frequency of the UAV's onboard sensors, the control frequency of the flight control computer, and the signal timing of the computation process, and draw a timing diagram.

[0015] Then, the actual delay duration of the UAV control signal is obtained from the timing diagram;

[0016] Then, the displacement of the drone during the delay phase is calculated.

[0017] Furthermore, the method for calculating the displacement of the drone during the delay phase is as follows:

[0018]

[0019]

[0020] In the formula, dx is the distance the drone moves along the x-axis during the delay process, dy is the distance the drone moves along the y-axis during the delay process, n is the number of segments during the delay period, and v xi It is the drone's velocity along the x-axis, v yi It is the y-axis velocity of the drone, a xi It is the acceleration of the drone along the x-axis, a yi It is the acceleration of the drone along the y-axis, Δt i It is a time interval.

[0021] Furthermore, in step S3, the specific correction process is as follows:

[0022] x new =x ori -dx, where dx is the distance the drone moves in the x-direction during the delay process, x ori It is the original horizontal axis position, x new This is the new horizontal axis position;

[0023] y new =y ori -dy, where dy is the distance the drone moves in the y-direction during the delay process. ori It is the original vertical axis position, y new This is the new vertical axis position.

[0024] Furthermore, when determining the speed and acceleration of the drone, different speeds and accelerations are required depending on the different altitudes and positional deviations during the drone's landing process.

[0025] Furthermore, the method for establishing the rule base is as follows: First, determine the control parameters based on the final control effect of the UAV; then, select the variables that affect the control parameters as input variables of the rule base, and the control parameters as output variables of the rule base; next, determine the universe of discourse of the control parameters and control variables; finally, establish a fuzzy control rule table, which is the rule base. Specifically, a two-dimensional fuzzy rule base can be constructed using horizontal position deviation and altitude deviation.

[0026] The domain of study was determined based on flight test data and empirical summaries.

[0027] The beneficial effects of the present invention: The autonomous landing delay compensation control method of the present invention can effectively solve the problems of position oscillation and slow landing caused by delay during the autonomous landing process of the UAV.

[0028] Using this position compensation method, the positional deviation of the UAV during the delay phase can be corrected, providing a basis for position control.

[0029] The fuzzy control strategy effectively suppressed overshoot, reduced horizontal position oscillations during UAV landing, and shortened landing time.

[0030] The control method proposed in this invention has low computational load, low requirements for the performance of airborne computers, low power consumption, and low requirements for the payload capacity of aircraft, making it particularly suitable for the landing control of small unmanned aerial vehicles. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating the implementation of the method proposed in this invention.

[0032] Figure 2 This is a system block diagram of the UAV proposed in this invention;

[0033] Figure 3 This is a timing diagram of the method proposed in this invention;

[0034] Figure 4 To compensate for the previous actual field flight simulation results;

[0035] Figure 5 The simulation results are those obtained after compensation using the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0037] The invention will now be described in detail with reference to the accompanying drawings, taking the landing control of a certain type of unmanned aerial vehicle as an example.

[0038] To compensate for position drift during the delay phase, a fuzzy control algorithm is used to eliminate repeated position oscillations. The specific execution process is as follows: Figure 1 As shown, the sampling and output times of the UAV's onboard sensors, the input and output times of the calculation module, and the input and output times of the control command transmission process are first obtained to obtain the entire process delay. Then, the position of the UAV during the delay stage is calculated by using a segmented approximate accumulation method. Next, the position of the UAV moving target is corrected. Finally, based on the corrected target position and the rule base, the speed and acceleration of the UAV are determined.

[0039] The following was specifically executed:

[0040] Step 1: Delay Duration Estimation

[0041] like Figure 2 As shown, the connections between components and the signal flow are illustrated. During system operation, delays occur in various stages, including image acquisition, target recognition, flight control status acquisition (filtering), inter-node communication, control latency (calculated by the onboard computer), and command feedback. Based on the ROS system, timestamp technology is used to record the time when data packets are sent and received in each stage, ultimately resulting in a total delay of approximately 400ms.

[0042] Step 2: Target Location Compensation

[0043] The airborne camera acquires images and converts them to an image coordinate system, then to the aircraft coordinate system, and finally outputs them. The image processing output frequency is 4Hz, while the airborne computer sends external control signals to the flight controller at a frequency of 20Hz. Therefore, the effective signal frequency actually received by the flight controller is 4Hz. Before receiving the next external control signal, the drone flies according to the previously received control signal.

[0044] The first control command has no delay, the second control command has a 200ms delay, and the third control command has a 400ms delay.

[0045] xnew =x ori -dx, where dx is the distance the drone moves in the x-direction during the delay process, x ori It is the original position of the horizontal axis, x new This is the new horizontal axis position;

[0046] y new =y ori -dy, where dy is the distance the drone moves in the y-direction during the delay process. ori It is the original vertical axis position, y new This is the new vertical axis position.

[0047] The method for calculating the displacement of the drone during the delay phase is as follows:

[0048]

[0049]

[0050] In the formula, dx is the distance the drone moves along the x-axis during the delay process, dy is the distance the drone moves along the y-axis during the delay process, n is the number of segments during the delay period, and v xi It is the drone's velocity along the x-axis, v yi It is the y-axis velocity of the drone, a xi It is the acceleration of the drone along the x-axis, a yi It is the acceleration of the drone along the y-axis, Δt i It is a time interval.

[0051] Step 3: Fuzzy Control Strategy

[0052] During the precise descent at the terminal stage, the determination of the control variables velocity v and acceleration a mainly depends on the horizontal deviation ΔL and altitude deviation ΔH. A two-dimensional fuzzy controller is adopted, using the horizontal deviation ΔL and altitude deviation ΔH as input variables, and the control variables velocity v and acceleration a as output control variables. The fuzzy set universe of discourse for the horizontal deviation ΔL is {NB, NM, NS, ZO, PS, PM, PB}, the fuzzy set universe of discourse for the altitude deviation ΔH is {ZO, PS, PM, PB}, the universe of discourse for the control variable velocity v is {NB, NM, NS, ZO, PS, PM, PB}, and the universe of discourse for the control variable acceleration a is {NB, NM, NS, ZO, PS, PM, PB}. See Table 1 for the parameter universe of discourse and language definitions, where NB represents negative large, NM represents negative medium, NS represents negative small, ZO represents zero, PS represents positive small, PM represents positive medium, and PB represents positive large.

[0053] Table 1. Parameter universe and language definition

[0054] parameter Domain Language definition ΔL [-2,2] {NB,NM,NS,ZO,PS,PM ΔH [0,5] {ZO,PS,PM,PB} v [-2,2] {NB,NM,NS,ZO,PS,PM a [-1,1] {NB,NM,NS,ZO,PS,PM

[0055] The specific values ​​of the universe of discourse ΔL are [-2, -1, -0.5, 0, 0.5, 1, 2], and ΔH = HH. set The specific values ​​of the universe of discourse for ΔH are [0, 0.2, 2.5, 5].

[0056] Table 2. Velocity Fuzzy Control Rules

[0057]

[0058] Table 3 Fuzzy Control Rules for Acceleration a

[0059]

[0060] Results Comparison Analysis

[0061] This invention conducted numerous field flight tests without position compensation in the early stages, and selected one of the more typical flight results, such as... Figure 4 As shown, the changing trends of the x, y, and z axes were recorded. The entire precise landing process at the end took about 40 seconds; the landing time was long, and the horizontal position oscillated repeatedly.

[0062] like Figure 5 As shown, the simulation results after implementing position compensation and fuzzy control strategies in Matlab / Simulink show that the entire precise terminal landing process takes about 15 seconds. Compared with the uncompensated case, the landing time is reduced by about 60%, the position control is precise, there is no obvious position oscillation, and the control effect is better.

[0063] The outstanding features of this invention are as follows:

[0064] (1) Using this position compensation method, the position offset of the UAV during the delay phase can be corrected, providing a basis for position control.

[0065] (2) The fuzzy control strategy effectively suppressed overshoot, reduced the oscillation of the horizontal position during the landing of the UAV, and shortened the landing time.

[0066] The above description is merely a specific embodiment of the present invention, providing a detailed description of the invention. Parts not covered herein are conventional techniques. However, the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for autonomous landing delay compensation control of unmanned aerial vehicles (UAVs), characterized in that, The compensation control method includes the following steps: Step S1: Obtain the sampling and output times of the UAV's onboard sensors, the input and output times of the calculation module, and the input and output times of the control command transmission process, thereby obtaining the entire process delay duration; Step S2: Calculate the position of the UAV during the delay stage using a piecewise approximate accumulation method; The compensation process is as follows: First, analyze the sampling frequency of the UAV's onboard sensors, the control frequency of the flight control computer, and the signal timing of the computation process, and draw a timing diagram. Then, the actual delay duration of the UAV control signal is obtained from the timing diagram; Next, the displacement of the drone during the delay phase is calculated; the method for calculating the displacement of the drone during the delay phase is as follows: In the formula, dx is the distance the UAV moves along the x-axis during the delay process, dy is the distance the UAV moves along the y-axis during the delay process, n is the number of segments during the delay period, and v xi It is the drone's velocity along the x-axis, v yi It is the y-axis velocity of the drone, a xi It is the acceleration of the drone along the x-axis, a yi It is the acceleration of the drone along the y-axis, Δt i It is a time interval; Step S3: Correct the position of the moving target of the UAV; Step S4: Based on the corrected target position and the rule base, determine the speed and acceleration of the drone's movement.

2. The method for autonomous landing delay compensation control of unmanned aerial vehicles as described in claim 1, characterized in that, In step S1, the delay duration of each stage can be obtained by using timestamps or modeling methods.

3. The method for autonomous landing delay compensation control of unmanned aerial vehicles as described in claim 2, characterized in that, In step S1, the airborne sensors include at least a camera, an ultrasonic sensor, and a laser rangefinder.

4. The method for autonomous landing delay compensation control of unmanned aerial vehicles as described in claim 1 or 2, characterized in that, In step S1, the total delay time is the cumulative result of the delay times of each step.

5. The method for autonomous landing delay compensation control of unmanned aerial vehicles as described in claim 1, characterized in that, In step S3, the specific correction process is as follows: x new =x ori -dx, where dx is the distance the drone moves in the x-direction during the delay process, x ori It is the original horizontal axis position, x new This is the new horizontal axis position; y new =y ori -dy, where dy is the distance the drone moves in the y-direction during the delay process. ori It is the original vertical axis position, y new This is the new vertical axis position.

6. The method for autonomous landing delay compensation control of unmanned aerial vehicles as described in claim 1, characterized in that, When determining the speed and acceleration of a drone, different speeds and accelerations are required depending on the different altitudes and positional deviations during the drone's landing process.

7. The method for autonomous landing delay compensation control of unmanned aerial vehicles as described in claim 1, characterized in that, The rule base establishment method is as follows: First, determine the control parameters based on the final control effect of the UAV; then, select the variables that affect the control parameters as the input variables of the rule base, and the control parameters as the output variables of the rule base; next, determine the domain of discourse of the control parameters and control variables; finally, establish a fuzzy control rule table, which is the rule base.

8. The method for autonomous landing delay compensation control of unmanned aerial vehicles as described in claim 7, characterized in that, A two-dimensional fuzzy rule base is constructed based on horizontal position deviation and height deviation.

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