Autonomous navigation control method for facility agriculture

Abstract

The invention relates to an autonomous navigation control method for facility agriculture, and the method comprises the following steps: collecting a yaw angle of a ridger through a one-way axis gyroscope, and carrying out the Kalman filtering; according to the expected angle and the filtered yaw angle, a course control quantity is obtained through a PID controller; and distributing the control quantity to an execution mechanism of the ridger by using a mixed control unit so as to realize autonomous navigation control of the ridger. Deviation can be reduced, and the walking straightness and stability of the agricultural machine can be improved. When the movement of the agricultural machine chassis deviates, the inertial navigation element gyroscope can detect the deviation of the movement of the agricultural machine, and the system can adjust the steering signal of the motor in real time when controlling the movement of the chassis.

Description

Technical Field

[0001] This invention belongs to the field of agricultural machinery control, specifically an autonomous navigation method for agricultural machinery. Background Technology

[0002] The application of new technologies such as electric walking chassis and inertial navigation and metering technology demonstrates the trend of agricultural machinery design towards automation and intelligence. However, for complex terrains such as orchards and hilly areas, agricultural machinery control using Bluetooth or traditional flight control suffers from short signal transmission distances, requiring the machine to operate near the operator. In farmland with greater distances or significant elevation differences, signal instability and delays can cause operational deviations.

[0003] While visual positioning technology has been applied to the agricultural machinery field, it has also been pointed out that visual navigation suffers from problems such as high cost, signal delay, and technical complexity, which hinder its widespread adoption.

[0004] Current agricultural machinery navigation largely relies on GPS / IMU fusion positioning, but high-precision GPS is expensive and easily obstructed, while low-precision solutions struggle to meet the demands of complex path tracking. Kalman filtering technology and flight control systems based on the ExpressLRS protocol are used for attitude optimization in the UAV field, but model adaptation has not been performed for the low-speed, high-load conditions of agricultural machinery.

[0005] Therefore, there is an urgent need for a simplified navigation method that can enable precise navigation of agricultural machinery in greenhouses where GPS signals are unstable. Summary of the Invention

[0006] The purpose of this invention is to provide a low-cost, high-precision heading control method and system for agricultural machinery. By combining a single-axis gyroscope with Kalman filtering optimization, a heading PID algorithm, and a mixer, the invention solves the problems of noise suppression, dynamic response optimization, and control quantity allocation efficiency. This autonomous navigation method improves the stability of automatic driving and path tracking accuracy of agricultural machinery without relying on GPS signals.

[0007] The technical solution adopted by the present invention to achieve the above objectives is: an autonomous navigation control method for facility agriculture, comprising the following steps:

[0008] The yaw angle of the ridging machine is acquired by a one-axis gyroscope and then subjected to Kalman filtering.

[0009] Based on the desired angle and the filtered yaw angle, the heading control quantity is obtained through a PID controller;

[0010] The control quantity is distributed to the actuators of the ridging machine by the mixed control unit to realize the autonomous navigation control of agricultural machinery.

[0011] The process of acquiring the yaw angle of the agricultural machinery using a single-axis gyroscope includes the following steps:

[0012] The output angular velocity of the unidirectional gyroscope is as follows:

[0013] ω output =K·ω inpur +b+n

[0014] In the formula, K is the sensitivity of the unidirectional gyroscope, ω input b is the actual input angular velocity, n is the bias, and n is the measurement noise.

[0015] Integrating the output angular velocity of the unidirectional gyroscope yields the angle at that moment. Therefore, the yaw angle θ around the z-axis over time t is the integral of the angular velocity.

[0016]

[0017] The method of distributing control quantities to the actuators of agricultural machinery using a hybrid control unit includes the following steps:

[0018] The two input channel signals are converted into throttle and steering signals, respectively;

[0019] For the first channel, the heading control quantity received through the PID controller is used as a steering signal and distributed to the left and right drive wheels of the agricultural machinery;

[0020] For the second channel, the speed data obtained from the left and right drive wheel drives of the agricultural machinery is subtracted from the speed given by the remote control, and then the control quantity obtained through their respective closed-loop control is used as the throttle signal and distributed to the left and right drive wheel drives of the agricultural machinery.

[0021] For the first channel, the received heading control quantity obtained through the PID controller is distributed to the left and right drive wheel drives of the agricultural machinery, as follows:

[0022] For single-axis control, the output of the mixer is:

[0023] u left =u thrust +u[t]

[0024] u right =u thrust -u[t]

[0025] Among them, u left and u right These are the PWM control values ​​for the left and right front drive wheel drives of the agricultural machinery chassis, respectively; u[t] is the heading control value output by the PID controller. thrust It is the total thrust control quantity, used to characterize the throttle command sent by the remote controller.

[0026] The desired angle is obtained from the command issued by the remote control.

[0027] The ExpressLRS is used as a remote controller to send throttle and steering commands to control the drive wheel of the agricultural machinery.

[0028] An autonomous navigation and control system for facility agriculture, applied to a remote controller, includes:

[0029] The filtering unit is used to acquire the yaw angle of the agricultural machinery collected by the unidirectional axis gyroscope and perform Kalman filtering;

[0030] The PID control unit is used to obtain the heading control quantity through the PID controller based on the desired angle and the filtered yaw angle.

[0031] The hybrid control unit is used to distribute control quantities to the actuators of agricultural machinery in order to achieve autonomous navigation control of agricultural machinery.

[0032] The mixing unit is used to convert the two input channel signals into throttle and steering signals, respectively.

[0033] The first channel is used to take the heading control quantity obtained by the PID controller and distribute it as a steering signal to the left and right drive wheel drives of the agricultural machinery.

[0034] The second channel is used to calculate the difference between the speed data obtained from the left and right drive wheels of the agricultural machinery and the speed given by the remote control, and then use the control quantity obtained by the respective closed-loop control as the throttle signal to distribute to the left and right drive wheels of the agricultural machinery.

[0035] The first channel is used to distribute the received heading control quantity obtained through the PID controller to the left and right drive wheel drives of the agricultural machinery, as follows:

[0036] For single-axis control, the output of the mixer is:

[0037] u left =u thrust +u[t]

[0038] u right =u thrust -u[t]

[0039] Among them, u left and u right These are the PWM control values ​​for the left and right front drive wheel drives of the agricultural machinery chassis, respectively; u[t] is the heading control value output by the PID controller. thrust It is the total thrust control quantity, used to characterize the throttle command sent by the remote controller.

[0040] The agricultural machinery in question is a ridging machine.

[0041] The present invention has the following beneficial effects and advantages:

[0042] 1. For agricultural machinery with low-speed, high-inertia characteristics, the inertial measurement unit (IMU) is a core component ensuring automatic navigation and attitude monitoring without external assistance. Through its built-in unidirectional gyroscope, accelerometer, and control methods, it measures the angular velocity of the agricultural machinery around its vertical axis to track changes in orientation and acceleration. When operating independently, this device can efficiently identify the object's attitude angle and analyze acceleration. The autonomous navigation system can reduce deviations and improve the straightness and stability of the agricultural machinery's movement. When deviations occur in the movement of the agricultural machinery chassis, the gyroscope in the inertial navigation element detects the deviation. The system adjusts the motor's steering signal in real time while controlling chassis movement, and the Kalman fusion algorithm ensures detection accuracy.

[0043] 2. In the special scenario of a greenhouse with unstable GPS signals, only the ridging machine needs to travel in a straight line along the ridges, so only the heading angle needs to be read and adjusted. Since only a single yaw angle needs to be calibrated, a unidirectional gyroscope is used. When a deviation in the machine's position is detected, the system will adjust the value of the steering signal, thereby causing the drive wheels of the front track of the agricultural machine to adjust in real time.

[0044] 3. By integrating the single-axis IMU with the Kalman filter algorithm and adapting the model to the low-speed, high-load conditions of agricultural machinery, the trajectory deviation of lateral yaw can be reduced, thereby improving the accuracy of operation.

[0045] 4. This invention employs a hybrid control unit to convert the two input channel signals into "throttle" and "steering" signals respectively. The speed control loop's instructions are distributed to the driver's "throttle," and the attitude control loop's instructions are distributed to the driver's "steering." The gyroscope only needs to control the attitude channel. Specifically, the error-adjusted "steering" signal is transmitted to the driver based on the PID heading control value. Therefore, only yaw angle control is needed to achieve autonomous navigation for low-speed straight-line travel. Attached Figure Description

[0046] Figure 1 Schematic diagram of the driving mechanism of the ridging machine of the present invention;

[0047] Among them, 1. Motor;

[0048] Figure 2 Block diagram of an autonomous navigation control system according to an embodiment of the present invention;

[0049] Figure 3 Schematic diagram of the working principle of the autonomous navigation control system of this invention;

[0050] Figure 4 The working principle diagram of the mixing control unit of the present invention. Detailed Implementation

[0051] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0052] The navigation control system of this invention is an inertial navigation system based on ExpressLRS. The ExpressLRS protocol provides low-latency, high-refresh-rate, interference-resistant, and more reliable signal communication for remote control of agricultural machinery. The ExpressLRS-based navigation system corrects deviations caused by environmental factors and enables the ridging machine to travel in a straight line. Its working space is a greenhouse where GPS is limited. Using a built-in inertial measurement unit, this autonomous navigation system determines the attitude, orientation, and velocity of the vehicle in inertial space by calibrating reference directions and initial position data. This method supports autonomous navigation and control of low-speed agricultural machinery. Inertial navigation typically determines its position and orientation through inertial sensors. Specifically, accelerometers, gyroscopes, and inertial measurement units are used for inference calculations. Modern gyroscopes are generally manufactured based on microelectromechanical systems (MEMS) technology. These electronic gyroscopes feature compact design, low cost, low power consumption, and ease of integrating digital and intelligent functions.

[0053] The inertial navigation control system is located in the control box at the front. When the speeds of the drive motors on both sides are consistent, the system will not produce a yaw action. When the yaw angle of the vehicle's position deviates—that is, when the vehicle yaws to the left—because the former rotates clockwise, when a reaction force is applied to the central axis of the fuselage in a counter-clockwise direction, it will cause the vehicle to yaw counter-clockwise. Conversely, if the direction of the force is opposite, it will cause the vehicle to yaw clockwise. Long-distance, high-speed, and low-latency remote control can be achieved through wireless transmission via the ExpressLRS protocol. Finally, by employing an autonomous inertial navigation system based on a Kalman filter optimization algorithm, using the steering angle as the control target and pulse width modulation (PWM) signals for control, the ridging machine can achieve straight-line travel, improving the accuracy and stability of machine positioning and navigation. A schematic diagram of the electric chassis control device is shown below. Figure 1 As shown.

[0054] The ridging machine needs a power unit to move, which can be either hydraulic or electric. This machine uses an electric drive mode. The power of an electric ridging machine mainly consists of two components: first, the motor that drives the frame; and second, the electronic speed controller that controls the motor's speed. If an electronic speed controller is used to control the chassis operation, the following problems may occur:

[0055] (1) There is a stall problem. When the electronic speed controller drives the geared motor, the lack of feedback device for motor speed makes it impossible to form a closed-loop control. When the track slips or other situations occur, the speed will be uncontrollable because the electronic speed controller cannot detect the motor position.

[0056] (2) Overload damage to electronic speed controllers. Electronic speed controllers are generally used in the control of fixed-wing and multi-rotor drones, which are characterized by their light weight, small size, and light load. However, when used to control agricultural machinery, the following problems may occur: When the agricultural machinery turns or travels on roads with a large slope, the working torque of the brushless motor in the chassis increases, the load increases, and the temperature of the electronic speed controller rises rapidly. In extreme stall conditions, the electronic speed controller may be damaged due to internal circuit breakdown caused by overload of the chassis motor.

[0057] (3) Signal instability and delay. The navigation system based on electronic speed control will experience signal attenuation at long distances and in environments with greenhouses. During the test, signal instability and delay were found.

[0058] First, the speed control device was changed from an electronic speed controller to a driver, which made the control of the brushless motor more stable and significantly reduced the failure rate. Second, Hall feedback control further stabilized the motor speed. Finally, a high-frequency head based on the ExpressLRS protocol was added. This expansion module can enhance the signal transmission distance and signal strength, representing an integrated innovation in agricultural machinery.

[0059] Autonomous navigation employs an inertial navigation control method based on the ExpressLRS protocol. The optimized navigation system control mainly includes a control system (communication antenna, STM32F1 main controller, keypad display terminal), a steering mechanism (receiver, mixing converter, drive, etc.), and an expansion module (LNB) to improve control distance and signal strength. The structural block diagram and schematic diagram are shown below. Figure 2 , Figure 3 As shown.

[0060] An inertial measurement unit (IMU) enables autonomous navigation without external assistance. The core components of attitude monitoring rely on built-in gyroscopes and accelerometers to monitor changes in an object's orientation and acceleration. When operating independently, this device can efficiently identify an object's attitude angle and analyze its acceleration. When the agricultural machinery chassis yaws, the gyroscope detects the deviation in the machine's movement. Upon detecting this deviation, the system adjusts the steering signal value, thus providing real-time positional correction as the machinery moves.

[0061] In inertial navigation based on the ExpressLRS protocol, an IMU microcontroller is installed at the front of the ridging machine. The IMU is a single-axis gyroscope, and the yaw angle information of the ridging machine is obtained through the IMU microcontroller. Accelerometers and gyroscopes integrated in the IMU collect information such as the speed and acceleration of the ridging machine. After preprocessing such as error compensation, the attitude information of the ridging machine in its own coordinate system is obtained. Then, a heading PID control method is used to calculate the control command for the machine's steering using a heading control and correction algorithm.

[0062] The navigation control system mainly consists of a single-axis gyroscope, a heading PID controller, and a mixer. The controlled objects are two brushless motors that drive the front drive wheel of the chassis.

[0063] The inertial measurement unit (IMU) of this machine uses a unidirectional gyroscope, and its working principle is based on the law of angular momentum conservation. Specifically, for a rigid body rotating about a fixed axis, its angular momentum L can be expressed by equation (4-1):

[0064] L=Iω (4-1)

[0065] In the formula: I is the moment of inertia; ω is the angular velocity;

[0066] When a gyroscope is subjected to an external torque M, its angular momentum will change, and the equation of motion is given by equation (4-2):

[0067]

[0068] In practical applications, the motion of a gyroscope is more complex, requiring consideration of the combined effects of Coriolis and centrifugal forces. Euler's equations, a set of dynamic equations describing the rotation of a rigid body about a fixed point, are applicable to gyroscopes. In this invention, the gyroscope rotates about the z-axis, and the Euler equations can be simplified to equation (4-3):

[0069]

[0070] This invention only requires the ridging machine to travel in a straight line along the ridge, so only the heading angle needs to be read and adjusted. Since only a single yaw angle needs to be calibrated, a unidirectional gyroscope is used. For a unidirectional gyroscope, it is usually assumed that it rotates around the z-axis (i.e., ω3) and satisfies I1=I2, then the equation is further simplified to equation (4-4):

[0071]

[0072] The output angular velocity of a single-axis gyroscope is shown in equation (4-5).

[0073] ω output =K·ω input +b+n (4-5)

[0074] In the formula: K is the sensitivity of the gyroscope, ω input ω is the actual input angular velocity, n is the bias (zero bias), and n is the measurement noise.

[0075] Integrating the angular velocity yields the angle at that moment. Therefore, the yaw angle about the z-axis over time t is the integral of the angular velocity, as shown in equation (4-6).

[0076]

[0077] Because sensors inherently possess drift and accumulated errors, Kalman filters can continuously optimize and fuse new data through a probabilistic model, reducing the impact of these errors on navigation results. Based on a state-space model, the angular velocity and integral angle output by the gyroscope are dynamically corrected to suppress noise and drift.

[0078] In practical applications of attitude update, Kalman filtering is used to optimize and predict angle estimates, thereby reducing the impact of noise and the environment.

[0079] Obtain the angular velocity ω output by the IMU output [t], and obtain the current angle θ(t) by integrating the velocity.

[0080] Calculate the desired angle θ desired The error between [t] and the actual angle θ[t]:

[0081] e[t]=θ desired [t]-θ[t] (4-7)

[0082] The heading PID control algorithm is calculated based on the current angle deviation (target value minus actual value), integral error (accumulated past errors), and rate of change (change in error).

[0083] The output control quantity u[t] is sent to the actuator (the brushless motors of the two drive wheels) to adjust the system attitude.

[0084] Save the current angle θ(t) and error e[t] for updating the state and for the next calculation.

[0085] The hybrid control conversion module converts the two input channel signals into "throttle" and "steering" signals respectively. The receiver receives the attitude channel signal, which is corrected by the PID heading control algorithm of the IMU components and then transmitted to the hybrid control conversion module. The hybrid control conversion module then transmits the error-adjusted "steering" signal to the driver. Therefore, only yaw angle control is needed to achieve autonomous navigation for low-speed straight-line travel.

[0086] The left joystick on the remote control is set to "throttle," and the right joystick is set to "steering." When the joysticks are operated, the chassis will execute corresponding actions based on the input commands, including forward, backward, left turn in place, and right turn in place. Through different directions and force inputs from the joysticks, the control system can accurately interpret and convert them into corresponding chassis movement commands, thereby achieving flexible direction and speed control. As the joystick offset angle increases, the chassis speed will also increase accordingly. However, if the joystick is in any position other than these four basic directions, the chassis may need to move forward and backward while also moving left and right. Therefore, hybrid control must be considered, that is, effectively combining forward and backward control with left and right control to achieve more flexible and precise chassis movement control. In this case, the key is to handle the speed synthesis in different directions well, ensuring that the chassis can smoothly execute compound actions.

[0087] The function of a mixer is to distribute the control quantity u(t) output by the PID controller to various actuators (the drivers of brushless motors). The design of the mixer depends on the specific structure of the system. The principle of mixing is as follows: Figure 4 As shown.

[0088] The control flow of a mixing module involves sending instructions with a normalized range between -1 and +1 to a specific controller (e.g., an attitude controller). The mixed output is then sent to a driver, where the normalized value is converted back to the specified value. The output of the mixing module can be represented as:

[0089]

[0090] Where u1, u2, u3, and u4 are the control variables for the four motors. roll ,u pitch ,u yaw These control variables correspond to roll, pitch, and yaw, respectively. thrust It is the total thrust control quantity.

[0091] For single-axis control, the output of the mixer can be simplified as follows:

[0092]

[0093] Among them, u left and u right is the PWM control quantity of the left and right drive wheel drivers. u[t] is the heading control quantity output by the PID controller.

[0094] The control quantity u output by the mixer left and u right It is sent to the actuator (the driver of the brushless motor) to adjust the system attitude.

[0095] Save the current heading angle θ(t) and error e(t) for the calculation of the next state, and update the state.

[0096] The control model, which combines a single-axis gyroscope, Kalman filter optimization, heading PID algorithm, and mixer, is implemented through the following steps:

[0097] (1) Use Kalman filters to improve the angle estimation provided by the gyroscope.

[0098] (2) The PID controller algorithm is used to calculate the heading control quantity.

[0099] (3) Use a mixer to distribute control quantities to the actuators.

[0100] (4) Output control variables and update the status.

[0101] The hybrid control unit distributes the received instruction attitude data and speed data to the motor drive, distributes the instructions from the speed control loop to the "throttle" of the driver, and distributes the instructions from the attitude control loop to the "steering" of the driver, while the gyroscope only needs to control the attitude channel.

[0102] This algorithm is suitable for systems requiring high heading control accuracy. By leveraging data fusion and optimization with a Kalman filter, noise interference and drift issues can be significantly reduced, thereby improving the system's control accuracy and stability. This enables the interpretation of the ridging machine's attitude and autonomous navigation correction.

[0103] This implementation uses ExpressLRS (Express Low-Rate Serial), a lightweight data link layer protocol belonging to open-source radio control systems. It is primarily used for remotely controlling unmanned vehicles, transmitting low-rate telemetry data and command information. It provides an efficient and reliable way to remotely control model aircraft and other devices such as drones. ExpressLRS employs a simple frame structure design, designed to reduce bandwidth requirements and simplify the communication process.

[0104] The navigation control flow based on the xpressLRS protocol is as follows:

[0105] Step 1: At the receiving end, the receiver includes a decoding module to receive communication signals from the transmitting end based on the ExpressLRS protocol. The receiver receives communication signals from two channels: "Throttle" and "Steering". The "Steering" signal is obtained by acquiring the angular velocity of the agricultural machinery in real time through a single-axis gyroscope in series and integrating it to calculate the original heading angle.

[0106] Step 2: Optimize the original heading angle using a Kalman filter. The process includes:

[0107] State prediction: Predict the current heading angle based on the state at the previous moment and the system model.

[0108] Measurement update: Combine gyroscope measurements to update the state estimate.

[0109] Step 3: Calculate the error between the desired heading angle and the actual heading angle, and generate a control quantity using a PID controller.

[0110] Step 4: Input the control quantity into the adaptive mixer and distribute it to the actuator (left / right brushless motor) according to the characteristics of the agricultural machinery steering mechanism.

[0111] Step 5: At the transmitting end, the path tracking accuracy is monitored in real time via a remote control as the hardware carrier, and the PID parameters and mixer allocation ratio are dynamically adjusted.

[0112] Software implementation

[0113] Kalman filter parameters:

[0114] PID initial parameters: K p =1.2,K i =0.05,K d =0.3.

[0115] Test Results

[0116] Static test: Angle drift decreased from ±2° / min to ±0.5° / min.

[0117] Dynamic test: Path tracking deviation is less than 0.1m (speed 1km / h).

Claims

1. An autonomous navigation control method for facility agriculture, characterized in that, Includes the following steps: The yaw angle of the ridging machine is acquired by a one-axis gyroscope and then subjected to Kalman filtering. Based on the desired angle and the filtered yaw angle, the heading control quantity is obtained through a PID controller; The control quantity is distributed to the actuators of the ridging machine by the mixed control unit to realize the autonomous navigation control of agricultural machinery.

2. The autonomous navigation control method for facility agriculture according to claim 1, characterized in that, The process of acquiring the yaw angle of the agricultural machinery using a single-axis gyroscope includes the following steps: The output angular velocity of the unidirectional gyroscope is as follows: oh output =K·ω input +b+n In the formula, K is the sensitivity of the unidirectional gyroscope, ω input b is the actual input angular velocity, n is the bias, and n is the measurement noise. Integrating the output angular velocity of the unidirectional gyroscope yields the angle at that moment. Therefore, the yaw angle θ around the z-axis over time t is the integral of the angular velocity.

3. The autonomous navigation control method for facility agriculture according to claim 1, characterized in that, The method of distributing control quantities to the actuators of agricultural machinery using a hybrid control unit includes the following steps: The two input channel signals are converted into throttle and steering signals, respectively; For the first channel, the heading control quantity received through the PID controller is used as a steering signal and distributed to the left and right drive wheels of the agricultural machinery; For the second channel, the speed data obtained from the left and right drive wheel drives of the agricultural machinery is subtracted from the speed given by the remote control, and then the control quantity obtained through their respective closed-loop control is used as the throttle signal and distributed to the left and right drive wheel drives of the agricultural machinery.

4. The autonomous navigation control method for facility agriculture according to claim 3, characterized in that, For the first channel, the received heading control quantity obtained through the PID controller is distributed to the left and right drive wheel drives of the agricultural machinery, as follows: For single-axis control, the output of the mixer is: u left =u thrust +u[t] u right =u thrust -u[t] Among them, u left and u right These are the PWM control values ​​for the left and right front drive wheel drives of the agricultural machinery chassis, respectively; u[t] is the heading control value output by the PID controller. thrust It is the total thrust control quantity, used to characterize the throttle command sent by the remote control.

5. The autonomous navigation control method for facility agriculture according to claim 1, characterized in that, The desired angle is obtained from the command issued by the remote control.

6. The autonomous navigation control method for facility agriculture according to claim 1, characterized in that, The ExpressLRS is used as a remote controller to send throttle and steering commands to control the drive wheel of the agricultural machinery.

7. An autonomous navigation control system for facility agriculture, characterized in that, Applied to remote controls, including: The filtering unit is used to acquire the yaw angle of the agricultural machinery collected by the unidirectional axis gyroscope and perform Kalman filtering; The PID control unit is used to obtain the heading control quantity through the PID controller based on the desired angle and the filtered yaw angle. The hybrid control unit is used to distribute control quantities to the actuators of agricultural machinery in order to achieve autonomous navigation control of agricultural machinery.

8. The autonomous navigation control system for facility agriculture according to claim 7, characterized in that, The mixing unit is used to convert the two input channel signals into throttle and steering signals, respectively. The first channel is used to take the heading control quantity obtained by the PID controller and distribute it as a steering signal to the left and right drive wheel drives of the agricultural machinery. The second channel is used to calculate the difference between the speed data obtained from the left and right drive wheels of the agricultural machinery and the speed given by the remote control, and then use the control quantity obtained by the respective closed-loop control as the throttle signal to distribute to the left and right drive wheels of the agricultural machinery.

9. An autonomous navigation control system for facility agriculture according to claim 8, characterized in that, The first channel is used to distribute the received heading control quantity obtained through the PID controller to the left and right drive wheel drives of the agricultural machinery, as follows: For single-axis control, the output of the mixer is: u left =u thrust +u[t] u right =u thrust -u[t] Among them, u left and u right These are the PWM control values ​​for the left and right front drive wheel drives of the agricultural machinery chassis, respectively; u[t] is the heading control value output by the PID controller. thrust It is the total thrust control quantity, used to characterize the throttle command sent by the remote control.

10. An autonomous navigation control system for facility agriculture according to claim 1, characterized in that, The agricultural machinery in question is a ridging machine.

Images