CN-121973203-A - Control system and method of full-automatic bionic sampler robot

CN121973203ACN 121973203 ACN121973203 ACN 121973203ACN-121973203-A

Abstract

The invention provides a control system and a control method of a full-automatic bionic sampler robot, which initiates a mechanical bionic sampling technology, completely simulates standard sampling actions of a sampling operator with abundant experience through a precise mechanical transmission and intelligent control system, and realizes non-disturbance, depth-fixing, fixed-point and full-process automatic sample collection. Through a precise mechanical structure and an intelligent control system, the standard action flow of a grain sampler with abundant experience when deep sampling is executed is completely simulated and optimized, so that the sampling technology is changed from a passive suction mode to an active, accurate and disturbance-free acquisition mode. The whole system is uniformly scheduled by a central control unit (PLC). The operator only needs to set sampling parameters on the touch screen of the control room, and the system can automatically complete the whole process from vehicle identification, point location planning, sampling execution and sample conveying. All the operation steps are electronically recorded, so that the complete standardization, intelligent programming and traceability of the sampling process are realized.

Inventors

SHEN LIANGBO
HU MEITING
DING JIANGTAO
MO XIAOBO
LI YEYAN

Assignees

杭州市粮食收储有限公司良渚分公司

Dates

Publication Date: 20260505
Application Date: 20260128

Claims (10)

1. A full-automatic bionic sampler robot control system is characterized in that a sensing-decision-executing three-layer intelligent control architecture is adopted: The sensing layer comprises a laser radar for acquiring three-dimensional point cloud data of a vehicle, a grating ruler displacement sensor for measuring depth information of a sampling head, a pressure sensor for measuring grain pressure in a sampling tube, an image sensor for shooting an image in the sampling tube, a proximity switch for detecting the opening and closing state of a sampling port and an encoder for measuring the rotating speed and the rotating angle of a motor; The decision layer takes a PLC as a core and integrates an edge calculation module, is used for processing perception layer data and generating a control instruction, and comprises a point position planning module, a depth positioning module, a filling identification module and a self-adaptive PID control module, wherein the point position planning module is used for generating a sampling point position plan by adopting an improved genetic algorithm based on laser radar point cloud data, the depth positioning module is used for fusing grating ruler and encoder data and realizing depth positioning error compensation by adopting a Kalman filtering algorithm, the filling identification module is used for identifying a sample filling state by a convolutional neural network model based on image sensor data, and the self-adaptive PID control module is used for dynamically adjusting the insertion speed of a sampling head according to pressure sensor feedback; the execution layer comprises a vertical motion platform for realizing lifting of the sampling head, a mechanical arm for driving the sampling head to move to a target point position, a bionic sampling head for opening and closing a sampling port and a sample conveying device for conveying samples.
2. The full-automatic bionic sampling machine robot control system according to claim 1, wherein the bionic sampling head comprises a sampling tube and a mechanical opening and closing device, the mechanical opening and closing device is driven by a miniature servo motor to drive a sampling port baffle to rotate to realize opening and closing, and a sealing ring is arranged between the baffle and the sampling tube.
3. The full-automatic bionic sampler robot control system according to claim 1, wherein the vertical motion platform adopts a ball screw pair driven by a servo motor, and the servo motor is controlled by a position-speed double closed-loop PID control algorithm.
4. The full-automatic biomimetic sampler robot control system according to claim 1, wherein the PLC of the decision layer adopts siemens S7-1200 series, and the PLC communicates with the servo driver through a CANopen protocol, communicates with the sensor through a Modbus RTU protocol, and communicates with the terminal touch screen through an ethernet TCP/IP protocol.
5. The full-automatic bionic sampler robot control system according to claim 1 is characterized in that a software system of the decision layer adopts a real-time operation system and comprises a driving layer, a middleware layer and an application layer, wherein the middleware layer adopts an OPC UA protocol to construct a data bus, and the application layer comprises a vehicle parameter scanning and point position planning module, a depth positioning and error compensating module, an opening and closing time control module, a sample filling state identification module and a motion track optimization module.
6. The fully automatic biomimetic sampler robot control system of claim 5 wherein the depth positioning and error compensation module uses an extended kalman filtering algorithm and the state vector includes the sampler depth, the movement speed and the transmission gap compensation amount to fuse the position data of the grating ruler and the speed data of the encoder.
7. A full-automatic bionic sampling robot control method applied to the control system according to any one of claims 1-6, comprising the following steps: S1, initializing a system, namely performing equipment self-checking, sensor calibration and communication test to enable the system to enter a standby state; S2, vehicle scanning and point location planning, namely scanning a vehicle through a laser radar and acquiring three-dimensional point cloud data, and generating uniformly distributed sampling point location coordinates based on an improved genetic algorithm; s3, inserting and controlling the sampling head, namely controlling the mechanical arm to move the sampling head to the position above the target point, controlling the vertical motion platform to insert the sampling head into the grain pile to the target depth according to a preset speed curve, and controlling the bionic sampling head to open a sampling port when the target depth is reached; s4, sample filling monitoring, namely monitoring the filling state of the sample in the sampling tube in real time through an image sensor and a pressure sensor, and controlling the bionic sampling head to close the sampling port when the filling rate is recognized to reach a set threshold value and the pressure reaches a corresponding threshold value; s5, pulling out the sampling head and conveying the sample, namely controlling the vertical motion platform to pull out the sampling head according to a preset speed curve, controlling the mechanical arm to move the sampling head to the position above the sample conveying device, starting the sampling port to discharge the sample, and completing conveying by the sample conveying device; s6, repeating the steps S3 to S5 until sampling of all planning points is completed, resetting the system and generating a report.
8. The method according to claim 7, wherein in the process of inserting the sampler head in the step S3, the depth measurement data of the grating ruler and the speed data of the encoder are fused in real time by using an extended kalman filter algorithm, and the transmission gap and the temperature drift are dynamically compensated to realize accurate depth positioning.
9. The method for controlling a fully automatic bionic sampling robot according to claim 7, wherein in the step S4, the internal image of the sampling tube collected by the image sensor is identified by a convolutional neural network model to obtain a real-time filling rate, and the filling state of the sample is confirmed by a dual-threshold judgment method in combination with the measured value of the pressure sensor.
10. The method according to claim 7, wherein in the steps S3 and S5, the predetermined speed curve is an S-shaped speed curve, and the servo motor is controlled by the adaptive PID control algorithm to track the speed curve, so as to realize smooth insertion and extraction of the sampling head.

Description

Control system and method of full-automatic bionic sampler robot Technical Field The invention relates to the technical field of industrial robots, in particular to a control system and method of a full-automatic bionic sampler robot, electronic equipment and a computer readable storage medium. Background The scientificity, representativeness and authenticity of the vehicle-to-warehouse sampling sample serve as a starting point of quality inspection, and the accuracy of a subsequent detection result is directly determined. For a long time, the traditional manual sampling and electric negative pressure automatic sampling modes have the defects of low manual sampling efficiency, high labor intensity and serious influence from subjective factors in practical application, while the negative pressure automatic sampling realizes automation, but causes sample distortion due to strong negative pressure disturbance, and especially causes the problems of excessive suction of impurities and breakage of chaff in grains such as rice, high off-grain brown rice rate, excessive impurity misjudgment and the like. At present, two sampling modes, namely manual sampling and electric negative pressure automatic sampling (the existing equipment is a full-automatic grain sampling machine) are mainly adopted in a grain purchasing site. The manual sampling relies on the sampler to hold the sampler, and samples are inserted point by point in the grain pile at the top of the vehicle. The mode is extremely high in labor intensity, a sampler needs to frequently ascend, serious potential safety hazards exist, and selection, insertion depth, sampling method and the like of the sampler are highly dependent on personal experience, so that representative fluctuation of samples is large, and standardization and traceability are difficult to achieve. To solve the problem of low efficiency of manual sampling, an electric negative pressure automatic sampling machine is generated. The equipment generates strong negative pressure air flow through the vacuum pump to suck grains into the sampling tube from the grain pile. Although realizing automatic operation and reducing labor cost, the working principle of the device brings new technical defects. The strong negative pressure air flow can severely disturb the grain pile in the sampling process, so that lighter impurities (such as dust, fragments and shrunken grains) are preferentially sucked in, and the false phenomenon of exceeding the standard of the impurities is often caused. Meanwhile, friction between high-speed air flow and the turning of the pipeline is extremely easy to cause the damage, the coarse peeling and the peeling of the rice shells, so that new brown rice is generated, and the key index of the purchase price of 'off-grain brown rice rate' is seriously distorted. The quality deviation caused by the sampling mode can not truly reflect the original state of grains, and the detection data of different vehicles in the same batch have obvious difference. Therefore, a novel sampling technology which can realize automation and high efficiency, can maintain the original state of a sample to the maximum extent and ensure the true and reliable detection result is urgently needed in the grain industry. Disclosure of Invention In order to solve the technical problems in the prior art, the invention provides the following technical scheme: in one aspect, a full-automatic bionic sampler robot control system is provided, and a perception-decision-execution three-layer intelligent control architecture is adopted: The sensing layer comprises a laser radar for acquiring three-dimensional point cloud data of a vehicle, a grating ruler displacement sensor for measuring depth information of a sampling head, a pressure sensor for measuring grain pressure in a sampling tube, an image sensor for shooting an image in the sampling tube, a proximity switch for detecting the opening and closing state of a sampling port and an encoder for measuring the rotating speed and the rotating angle of a motor; The decision layer takes a PLC as a core and integrates an edge calculation module, is used for processing perception layer data and generating a control instruction, and comprises a point position planning module, a depth positioning module, a filling identification module and a self-adaptive PID control module, wherein the point position planning module is used for generating a sampling point position plan by adopting an improved genetic algorithm based on laser radar point cloud data, the depth positioning module is used for fusing grating ruler and encoder data and realizing depth positioning error compensation by adopting a Kalman filtering algorithm, the filling identification module is used for identifying a sample filling state by a convolutional neural network model based on image sensor data, and the self-adaptive PID control module is used for dynamically adjusting the insertion speed of a sampling h