Real-time adjustment control system and method for operation response parameters of corn castration machine

Abstract

The invention relates to the technical field of agricultural machinery automation control, in particular to a corn castration machine operation response parameter real-time adjustment control system and method, and the system comprises a dynamic sensing module, a plurality of multi-axis mechanical arms and end actuators thereof, and a predictive central processing and control module. The dynamic sensing module is used for acquiring a real-time kinematics state of a top growing point of a target plant in a disturbance environment; and the predictive central processing and control module is configured to predict the motion range of the top growing point of the target plant in a preset time in the future based on the real-time kinematics state, and complementarily control the action of the multi-axis mechanical arm based on the predicted motion range so as to grab and remove the top growing point. According to the system, passive following is converted into active pre-judgment, system delay can be effectively overcome, accurate positioning of a high-speed swinging target is achieved, and working efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agricultural machinery automation control, and in particular to a system and method capable of adapting to disturbance environments such as strong winds, achieving precise and efficient removal of corn tassels through dynamic prediction and adaptive control. BACKGROUND

[0002] Corn detasseling is a key link in corn hybrid seed production, and its purpose is to remove the tassels of female plants to prevent self-pollination and ensure seed purity. In actual production, corn detasseling mainly relies on manual or traditional mechanical detasseling machines.

[0003] In recent years, with the development of robotics and artificial intelligence, agricultural robots that use visual perception for precise identification and detasseling have emerged. However, these robots are mostly applied in static or quasi-static environments, and their operating objects are usually relatively static. Since the corn core tassel is a lightweight, flexible rod structure, it will produce high-frequency, irregular and large-amplitude swings in natural wind fields and other disturbance environments. The existing control strategy of agricultural robots is mostly based on real-time error feedback, and the system response speed lags far behind the rapid swings of the target, resulting in positioning failure and missed grabbing, and making it impossible to effectively work in real and variable field environments.

[0004] Therefore, how to solve the problem of precise identification, stable tracking and reliable operation of flexible and dynamic targets in strong disturbance environments is a technical bottleneck that needs to be solved to realize automatic and intelligent corn detasseling. SUMMARY

[0005] The present application aims to solve the problems in the background art and provides a corn detasseling machine operation response parameter real-time adjustment control system and method. The system can significantly improve the success rate and efficiency of automatic removal of the top growth point of corn in wind fields and other disturbance environments through dynamic prediction and adaptive control.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A corn detasseling machine operation response parameter real-time adjustment control system, comprising a dynamic perception module, a plurality of multi-axis robot arms and their end effectors, and a predictive central processing and control module. The dynamic perception module is used to obtain the real-time kinematic state of the target plant top growth point in the disturbance environment; the predictive central processing and control module is configured to predict the motion range of the target plant top growth point within a predetermined time in the future based on the real-time kinematic state, and to compensatorily control the action of the multi-axis robot arm based on the predicted motion range to perform grabbing and removal operations on the top growth point.

[0007] Further, the dynamic perception module comprises a three-dimensional vision sensor and a three-dimensional wind speed and direction meter.

[0008] Further, the predictive central processing and control module extracts the motion trajectory, speed, acceleration, dominant vibration frequency and amplitude of the target by calculating and spectrum analyzing the continuous multi-frame pose information collected by the three-dimensional vision sensor.

[0009] Further, the predictive central processing and control module establishes a dynamic prediction model for describing the motion of the target, and uses a state estimation algorithm such as Kalman filtering to fuse model prediction and sensor observation to generate an optimized prediction of the future motion range.

[0010] Further, the predictive central processing and control module uses a predictive servo control strategy to take a specific point in the optimized predicted motion range as the target instruction point of the motion of the mechanical arm.

[0011] Further, the end effector is a composite structure comprising a guide cover, an inflatable air bag and a negative pressure generation and control unit, and the target is adsorbed and actively stabilized by negative pressure airflow.

[0012] Further, the predictive central processing and control module controls the size of the negative pressure airflow in a closed loop according to the range value of the predicted motion range.

[0013] Further, the multi-axis mechanical arm integrates a force sensor for force feedback monitoring during adsorption to realize safety decision-making.

[0014] The present application also provides a control method corresponding to the above-mentioned system, comprising the steps of dynamic state acquisition, motion range prediction, mechanical arm compensatory control, active stabilization and adsorption, and removal operation.

[0015] The present application has the advantages that by introducing a compensatory control strategy based on dynamic prediction, the system changes from "passive following" to "active prediction", which can effectively overcome system delay and realize accurate positioning of high-speed swinging targets; by designing a unique active stabilization end effector, the system can actively suppress target swing before grabbing through negative pressure, creating favorable conditions for reliable operation; through multi-sensor fusion and adaptive adjustment, the system can adapt to changing wind field environment in real time, significantly improving all-weather operation capability, successful de-male rate and operation efficiency, while reducing damage to plants. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows.

[0017] Figure 1 The overall structure schematic diagram provided for the embodiment 1 of the application.

[0018] Figure 2 The control method flow chart diagram provided for the embodiment 2 of the application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the application will be clearly and completely described in combination with the drawings in the embodiments of the application.

[0020] Embodiment one Referring to Figure 1 The corn detasseling machine operation response parameter real-time adjustment control system provided in the embodiment is arranged on a four-wheel driven high wheelbase mobile platform 1, the platform can adapt to the field terrain in the late growth period of corn, and the platform is also equipped with a laser radar and a GPS autonomous navigation system.

[0021] Referring to Figure 1 The application comprises a dynamic perception module 2, one or more multi-axis mechanical arms 3, an end effector 4 connected to the end of the mechanical arm, and a predictive central processing and control module 5.

[0022] The dynamic perception module 2 comprises: At least one three-dimensional vision sensor 21: for example, an Intel RealSense D435i depth camera, working at 60 fps or higher frame rate, installed at a high position of the mobile platform 1, used to obtain the three-dimensional space coordinates (X, Y, Z) and the attitude (pitch, yaw, roll) of the target corn top growth point (i.e. the core male ear) in real time.

[0023] An environmental sensor 22: at least one three-dimensional ultrasonic anemometer, installed on the top of the mobile platform 1, used to measure the instantaneous wind speed and direction of the operation point in real time.

[0024] The end effector 4 is a composite structure, comprising: A guide cover 41: a downwardly open horn-shaped cover made of light high molecular material, with smooth inner wall, used to preliminarily physically guide and correct the swaying corn tip when the mechanical arm approaches.

[0025] An inflatable air bag 42: an annular air bag made of silica gel is arranged around the inside of the guide cover 41. The air bag is connected to a micro air pump.

[0026] A negative pressure generation and control unit 43: controls a high-flow turbine air blower, which is communicated with the central area of the guide cover 41 through a pipeline. A high-speed electromagnetic valve is arranged on the pipeline. The central opening of the guide cover, i.e. the sealing port, is provided with a flexible sealing lip.

[0027] The working logic of the predictive central processing and control module 5 is as follows: First step: dynamic state acquisition and calculation The controller receives continuous image frames from the three-dimensional visual sensor 21 in real time. By running the AI model, the corn tip is identified, and the pose information of each frame is calculated in combination with the depth map. By performing difference operation on the pose data of consecutive frames, the real-time motion trajectory, velocity and acceleration vector of the target point are obtained. At the same time, the pose time series in this period of time is subjected to fast Fourier transform, and the motion range, dominant vibration frequency and maximum amplitude of the target in the current wind field are extracted.

[0028] Second step: prediction and decision A dynamic prediction model is established inside the controller, for example, the corn plant is regarded as a second-order damped vibration system driven by wind force (measured by an anemometer) with a certain natural frequency. The specific natural frequency is obtained by fast Fourier transform analysis.

[0029] Further, an extended Kalman filter algorithm is used to fuse the prediction results of the dynamic model with the next frame of actual observation results of the visual sensor. This process aims to filter out sensor noise and output a more smooth and accurate optimized prediction of the target motion range within the next 100-200 milliseconds.

[0030] Preferably, the controller adopts a predictive servo control strategy. The controller directly commands the robot arm to track the "predicted position after 150 milliseconds". The specific target point can be set as the point in the predicted motion range closest to the end effector.

[0031] Third step: adaptive negative pressure regulation When the controller predicts that the robot arm is about to reach the target position, the range value (i.e. amplitude) of the predicted motion range is evaluated.

[0032] If the amplitude is less than the preset threshold, it indicates that the wind disturbance is small, the predicted motion range is small, and only a small negative pressure is needed to capture the core male ear. At this time, the controller will control the turbine blower of the negative pressure generation unit 43 to reduce the output power. It should be noted that the output power of the turbine blower is always greater than the output power of the turbine blower required to pull the core male ear off the main stem.

[0033] If the amplitude is greater than the preset threshold, it indicates that the wind disturbance is severe, the predicted motion range is large, and a large negative pressure is needed to capture the core male ear. At this time, the controller will control the turbine blower of the negative pressure generation unit 43 to increase the output power.

[0034] When the guide cover 41 is located at the predicted motion range position, the high-speed valve is opened instantaneously, generating a strong negative pressure airflow that "sucks" the swinging corn tip to and stabilizes it in the central area of the guide cover. The size of the negative pressure is determined by the evaluation value of the motion range, the greater the amplitude, the higher the output power of the turbo fan, and the greater the negative pressure generated to ensure effective and rapid grabbing of the corn tip.

[0035] At the same time, the micro air pump slightly inflates the inflatable air bag 42, causing it to expand and fill the partial gap between the inner wall of the guide cover and the corn tip. This is not for clamping, but to narrow the air leakage channel, greatly enhancing the sealing of the central area, thereby significantly improving the adsorption and stabilization effect of the negative pressure. The inflatable air bag 42 is connected to a solenoid valve for air exhaust of the inflatable air bag 42 after the removal action is completed.

[0036] Step 4: Force feedback The multi-axis robot arm 3 integrates a force sensor. During the process of negative pressure adsorption, the controller monitors the force sensor readings in real time.

[0037] In the initial stage, the force reading is small.

[0038] When the corn tip is successfully adsorbed and forms a good fit with the sealing port, the force sensor will detect a significant rise in pulling force caused by suction.

[0039] The controller sets a preset threshold. When the force feedback data exceeds the threshold, the controller confirms that the adsorption is successful and can trigger the next step of removal operation.

[0040] If the force feedback does not reach the threshold for a long time, or the force feedback is abnormally large, the system determines that this attempt has failed, releases the negative pressure, and controls the robot arm to retreat and find the next target, avoiding dead loops and damage to the equipment.

[0041] Example Two The present embodiment provides a real-time adjustment control method for corn detasseling machine operation response parameters based on the system described in Example One. Referring to Figure 2 , the method comprises the following steps: Step S101: Dynamic state acquisition. After the system is started, the mobile platform 1 travels in the field, and the dynamic perception module 2 continuously scans and acquires the real-time kinematic state of the target corn top growth point in front.

[0042] Step S201: Motion range prediction. The central processing and control module 5 processes the acquired state data, as described in Example One, to predict the motion range of the target in a short period of time in the future.

[0043] Step S301: compensatory control of the robot arm. Based on the prediction result, the controller controls the robot arm 3 with the end effector 4 to approach the target in a high speed in a manner predicted in advance, using a predictive servo strategy.

[0044] Step S401: active stabilization and adsorption. After approaching the target, according to the predicted motion range uncertainty, it is decided to start a negative pressure airflow and airbag gap adjustment of how much intensity to actively suppress and adsorb the target.

[0045] Step S501: removal operation. After confirming the successful adsorption through the force sensor, the removal action is performed. This action can be: a) further increasing the negative pressure pulse to directly pull the tassel into the suction; b) while maintaining the adsorption, performing a quick, spiral upward pulling action by the robot arm.

[0046] The above description is only a preferred embodiment of the present application, and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A real-time adjustment control system for operating response parameters of a corn detassling machine, characterized by, Comprising: a dynamic perception module for acquiring real-time kinematic state of the target plant top growing point in a disturbed environment; one or more multi-axis robotic arms, the end of which is connected with an end effector; and a predictive central processing and control module, which is configured to: predict the motion range of the target plant top growing point in a future preset time based on the real-time kinematic state; and compensatorily control the action of the multi-axis robotic arm based on the predicted motion range to perform grasping and removal operation on the top growing point in the disturbed environment.

2. The system of claim 1, wherein, The dynamic perception module comprises: a three-dimensional vision sensor for acquiring real-time pose information of the target plant top growing point in three-dimensional space, the pose information including position coordinates and attitude angles; an environmental sensor for acquiring physical parameters of the surrounding environment during system operation; the environmental sensor is a three-dimensional wind speed and direction instrument for real-time measurement of wind speed and direction parameters.

3. The system of claim 2, wherein, The predictive central processing and control module is configured to: calculate real-time kinematic parameters of the target plant top growing point based on multiple frames of pose information collected by the three-dimensional vision sensor at consecutive time points, the kinematic parameters including motion trajectory, velocity and / or acceleration; perform spectral analysis on the time series of the pose information to extract the motion range, dominant vibration frequency and amplitude of the target plant top growing point in the current wind field environment.

4. The system of claim 3, wherein, The predictive central processing and control module is further configured to: establish a dynamic prediction model for describing the motion of the target plant top growing point; use a state estimation algorithm to fuse the predicted state of the dynamic prediction model and the actual observed state of the three-dimensional vision sensor to generate an optimized prediction of the motion range of the target plant top growing point in a future preset time period.

5. The system of claim 4, wherein, The state estimation algorithm is a Kalman filter algorithm or its extended form.

6. The system of claim 4 or 5, wherein, When controlling the multi-axis robotic arm, the predictive central processing and control module uses a predictive servo control strategy to take the position point closest to the end effector in the optimized predicted motion range as the target instruction point for the motion of the multi-axis robotic arm to compensate for system response delay.

7. The system of claim 6, wherein: the end effector comprises: a guide cover for guiding and correcting the target attitude; one or more inflatable airbags made of flexible material arranged inside the guide cover, the airbags achieving surrounding gap adjustment of the target plant top growing point through inflation to improve the adsorption force of negative pressure on the target plant; and a negative pressure generation and control unit coaxially arranged with the guide cover, the negative pressure generation and control unit being configured to: generate a controlled negative pressure airflow when the guide cover approaches a preset negative pressure adsorption range of the target plant top growing point, to adsorb and stabilize the target plant top growing point in the central region of the guide cover, thereby suppressing the swinging of the target plant top growing point due to external airflow disturbance.

8. The system of claim 7, wherein, The predictive central processing and control module is further configured to: Real-time evaluation of the range value of the optimized predicted motion range; control the generation of a negative pressure airflow that can adsorb the top growing point of the target plant at any position point within the optimized predicted motion range.

9. The system of claim 8, wherein, The multi-axis robot arm integrates a force sensor, and the predictive central processing and control module is further configured to: Real-time acquisition of force feedback data of the force sensor during the process of adsorbing the top growing point of the target plant by the end effector; When the force feedback data value exceeds the preset threshold, the multi-axis robot arm with the end effector is adjusted to approach the next target plant top growing point in real time.

10. A method of real-time adjustment control of a corn detassling machine operation response parameter, Characterized in that, Comprising the following steps: Step S101: Dynamic state acquisition step: through the dynamic perception module deployed on the mobile platform, the kinematic state of the target plant top growing point in the disturbed environment is acquired in real time; Step S201: Motion range prediction step: using the predictive central processing and control module, based on the real-time kinematic state, the motion range of the target plant top growing point in the future within a preset time is predicted; Step S301: Robot arm compensatory control step: based on the predicted motion range, the action of one or more multi-axis robot arms is compensatory controlled to make the end effector connected thereto approach the motion range; Step S401: Active stabilization and adsorption step: after the end effector approaches the motion range, a controlled negative pressure airflow is generated by the end effector to adsorb the top growing point of the target plant and suppress the swing of the top growing point due to external disturbance; Step S501: Removal operation step: after adsorption is completed, the top growing point is removed from the plant main stem through the subsequent action of the end effector and / or the multi-axis robot arm.

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