CN115903857A - RFID-based unmanned grain surface inspection device and positioning method - Google Patents
RFID-based unmanned grain surface inspection device and positioning method Download PDFInfo
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- CN115903857A CN115903857A CN202310147222.5A CN202310147222A CN115903857A CN 115903857 A CN115903857 A CN 115903857A CN 202310147222 A CN202310147222 A CN 202310147222A CN 115903857 A CN115903857 A CN 115903857A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention discloses an unmanned grain surface inspection device based on RFID and a positioning method, relates to the field of grain surface inspection, and aims to improve the positioning accuracy of inspection equipment while ensuring the integrity of grain surfaces. The grain surface unmanned inspection device based on the RFID comprises a processor, a driving system, a navigation positioning system and an information acquisition system, wherein the navigation positioning system comprises an inertial positioning unit, a visual navigation camera, an RFID read-write unit and an encoder. The RFID-based grain surface unmanned inspection device positioning method comprises the following steps: setting an inspection route based on the distribution condition of the granary walkway boards, and laying RFID tags on the inspection route; acquiring an actual position based on an inertial positioning unit and an encoder; identifying information of the RFID label to obtain a current coordinate position; and verifying the actual position by adopting the current coordinate position, and correcting the error of the actual position. By adopting the mode, the positioning precision of the inspection equipment can be improved.
Description
Technical Field
The invention relates to the field of grain surface inspection, in particular to an unmanned grain surface inspection device based on RFID and a positioning method.
Background
In the prior art, the following two modes are adopted for positioning the inspection equipment during the inspection of the grain surface:
1. scanning the surrounding environment through a laser radar, and constructing a map through an SLAM algorithm (positioning and mapping at the same time), wherein the working principle is as follows: the routing inspection equipment starts from an unknown place in an unknown environment, positions and postures of the routing inspection equipment per se through repeatedly observed map features in the movement process, and constructs a map in an incremental mode according to the positions of the routing inspection equipment per se, so that the purposes of positioning and map construction are achieved simultaneously. However, the SLAM algorithm requires that the inspection equipment directly walk on the grain surface, which can cause damage to the grain surface and is not suitable for positioning unmanned inspection equipment for the grain surface. And no obvious map features exist in the granary, so that the granary cannot be accurately positioned.
2. A fingerprint network is established through a plurality of RFID fingerprints, the position of the robot is calculated by comparing signal strength values of the robot and a plurality of RFID signal sources, and the position calibration of the robot is realized. This approach has 3 problems: firstly, when the positioning area is large, the collected radio frequency fingerprints have high sparsity, so that the positioning accuracy is not high; secondly, the method requires the robot to walk on the grain surface, and the surface of the granary is damaged; thirdly, the method needs to lay a great number of RFID labels, the cost is high, the positioning algorithm is complex, and the calculation is time-consuming.
Disclosure of Invention
In order to ensure that the grain surface is complete and improve the positioning precision of the inspection equipment, the application provides an unmanned inspection device and a positioning method for the grain surface based on RFID.
The technical scheme adopted by the invention for solving the problems is as follows:
unmanned inspection device of grain face based on RFID, including the treater, respectively with treater signal connection's actuating system, navigation positioning system and information acquisition system, be provided with the motor among the actuating system, navigation positioning system includes: the device comprises an inertial positioning unit, a visual navigation camera, an RFID read-write unit and an encoder, wherein the encoder is installed on a motor, and the inertial positioning unit, the visual navigation camera, the RFID read-write unit and the encoder are in signal connection with a processor.
Further, the inertial positioning unit comprises: an accelerometer and a gyroscope.
Further, information acquisition system includes pest monitoring camera, camera support and light filling lamp, pest monitoring camera sets up on camera support.
Further, the ultrasonic distance measuring sensor is also included.
Furthermore, the system also comprises a temperature, humidity and gas concentration monitoring system.
A grain surface unmanned inspection device positioning method based on RFID is applied to a grain surface unmanned inspection device based on RFID and comprises the following steps:
step 2, acquiring an actual position based on an inertial positioning unit and an encoder;
step 3, identifying information of the RFID tag in the polling process by the grain surface unmanned polling device based on the RFID to obtain the current coordinate position;
and 4, checking the actual position by adopting the current coordinate position, and correcting the error of the actual position.
Further, the step 2 specifically includes:
step 21, obtaining the position X based on the inertial positioning unit k ；
Step 22, obtaining a position Z based on the encoder k ；
Step 23, fusing X through the estimation and correction process of Kalman filtering algorithm k And Z k And obtaining the actual position.
Further, the method also comprises a step 5 of shooting the grain surface regularly and giving position information during shooting.
Further, in the step 5, the grain is shot at regular intervals and specifically designated or shot at a fixed distance.
Further, the method also comprises a step 6 of carrying out pest identification based on the image shot in the step 5 and determining pest occurrence positions.
Compared with the prior art, the invention has the beneficial effects that: the inspection route of the inspection equipment in the granary is preset, so that the inspection equipment works along the specified route, and the grain surface cannot be damaged. The RFID reader-writer intermittently reads the ground tag information to obtain a current coordinate position and pose change instruction, the current coordinate position and the pose change instruction are checked with an actual position obtained by the inertial positioning unit and the encoder, and the position error of the actual position is corrected, so that the pose information of the inspection equipment is more accurate.
Drawings
FIG. 1 is a schematic structural diagram of an RFID-based grain surface unmanned inspection device;
FIG. 2 is a rear view of the RFID-based unmanned grain surface inspection device;
FIG. 3 is a schematic diagram of the internal structure of the unmanned grain inspection device based on RFID;
FIG. 4 is a flow chart of a method for positioning an unmanned grain inspection device based on RFID;
FIG. 5 is a schematic diagram of a routing inspection route;
fig. 6 is an enlarged view of a portion a in fig. 1.
Reference numerals are as follows: 101. a pest monitoring camera; 102. a camera head bracket; 103. a light supplement lamp; 104. a wheel; 105. an RFID reader; 106. an ultrasonic ranging sensor; 107. a visual navigation camera; 108. a box body of inspection equipment; 201. an integrated sensor; 202. a motor; 301. an RFID control board; 302. a microprocessor; 303. a motor driver; 304. a lower computer microprocessor; 305. a battery.
Description of the preferred embodiment
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Unmanned inspection device of grain face based on RFID, including the treater, respectively with treater signal connection's actuating system, navigation positioning system and information acquisition system, be provided with the motor among the actuating system, navigation positioning system includes: the device comprises an inertial positioning unit, a visual navigation camera, an RFID reading and writing unit and an encoder, wherein the encoder is installed on a motor, and the inertial positioning unit, the visual navigation camera, the RFID reading and writing unit and the encoder are all in signal connection with a processor.
As shown in fig. 1-3, in the present embodiment, the processor is a microprocessor. The driving system includes a lower computer microprocessor 304, a motor driver 303, a motor 202, a battery 305, and wheels 104. The motor 202 is connected with the wheel 104 and drives the inspection equipment to move. The lower computer microprocessor 304 is connected to the microprocessor 302 and the motor driver 303, and a battery 305 is a power source.
The navigation positioning system comprises an inertial positioning unit, a visual navigation camera 107, an encoder and an RFID read-write unit, wherein the RFID read-write unit comprises an RFID reader-writer 105 and an RFID card control board 301. The encoder is installed on motor 202, and the encoder can count the number of turns of rotation of motor in real time, according to the number of turns of rotation of motor and patrol and examine the diameter of equipping the wheel and confirm to patrol and examine the distance of equipping the removal.
The inertial positioning unit can obtain inertial information through an accelerometer and a gyroscope, wherein the gyroscope can obtain angular velocities of all axes, and the accelerometer can obtain accelerations in x, y and z directions. And then converting the coordinate system of the inertial positioning unit into a world coordinate system through a coordinate conversion matrix to obtain the attitude and the coordinate information of the inspection equipment. The inertial positioning unit is mounted on the microprocessor of the lower computer.
And fusing the coordinates obtained by the inertial positioning unit and the encoder in the process of prediction and correction through a Kalman filtering algorithm to obtain the actual position of the inspection equipment.
Position X obtained by inertial positioning unit k Position Z obtained by the encoder as a prediction value k As a measure of the value of the measurement,
in the formula, A, B and H are state change matrixes; w k Exciting noise, V, for system processes k In order to observe the noise, it is,
for system control input, alpha and omega are respectively acceleration and angular velocity and are provided by an inertial positioning unit; />
Coordinates obtained for the inertial positioning unit; wherein P is x1 ，P y1 Respectively as abscissa and ordinate>
Is a coordinate matrix->
Is transferred and is taken out>
Coordinates obtained for the encoder, where P x2 ，P y2 Respectively as abscissa and ordinate>
Is a coordinate matrix->
Transposing; x k-1 Coordinates obtained for the k-1 st measurement of the inertial positioning unit.
The method comprises the following specific steps:
a. firstly, the estimation process is carried out
Solving prior estimation:
；
solving prior error covariance:
;
b. then performing a correction process
Calculating a Kalman gain:
;
solving posterior estimation:
；
updating the error covariance:
;
in the formula, Q and R are process noise covariance matrixes; i is an identity matrix;
estimating a covariance matrix for the prior; p is k Estimating a covariance matrix for the posteriori; k k Is a Kalman gain>
The estimated value of the prior state in the k step is obtained; />
Is the posterior state estimated value of the k step; />
Is the posterior state estimated value of the step k-1; />
Is the measured value of the inertial positioning unit of the step k-1.
And solving the posterior estimation to obtain the actual position of the inspection equipment. And substituting the updated error covariance into the estimation process, executing the next estimation and correction process, and continuously updating the position coordinates of the inspection equipment until the inspection equipment stops working.
The visual navigation camera 107 is connected with the microprocessor 302, the visual navigation camera 107 captures picture information of the grain surface and the walkway plate, the picture information is transmitted to the microprocessor 302 for image processing (distortion correction, feature extraction, image noise reduction and guidance line fitting), and finally a motion instruction for realizing autonomous navigation is obtained, the microprocessor 302 transmits the received upper computer control signal to each electric element driver in the form of a level signal, and controls the electric elements to execute expected actions, so that the autonomous navigation function is realized. The RFID card control board 301 is disposed inside the inspection equipment box 108, and is connected to the microprocessor 302 and the RFID reader/writer 105. The RFID reader-writer 105 is arranged on the inspection equipment box 108, intermittently reads the ground label information, obtains the current coordinate position, verifies the current coordinate position with the actual position of the inspection equipment, corrects the position error of the actual position of the inspection equipment, and realizes the autonomous positioning function.
The information acquisition system comprises a pest monitoring camera 101, a camera bracket 102 and a light supplement lamp 103, and the pest monitoring camera is enlarged as shown in fig. 6. The pest monitoring camera 101 can rotate in all directions through the camera bracket 102, and the rotation in all directions can be realized through a structure similar to a rotating platform. The full-automatic unmanned grain surface inspection equipment is in the autonomous inspection process, the inspection equipment stops at a specified position according to position information obtained through autonomous positioning, the pest monitoring camera 101 automatically focuses and shoots clear photos according to the grain surface size, the shot photos are numbered, and the photo position information is given by combining the positioning information of the inspection equipment. The image information shot by the pest monitoring camera can also be uploaded to an appointed IP address through an image transmission interface of the communication system.
Further, the system comprises an ultrasonic ranging sensor 106 and a temperature, humidity and gas concentration monitoring system, wherein the ultrasonic ranging sensor 106 is arranged on the patrol equipment box 108 and connected with the microprocessor 302, the ultrasonic ranging sensor 106 emits and detects ultrasonic waves, the time difference between the ultrasonic ranging sensor 106 and the patrol equipment box is output to the microprocessor 302 as pulse width data, then the distance to an obstacle is calculated according to the sound velocity and the time difference, and the autonomous obstacle avoidance function is achieved. The temperature, humidity and gas concentration monitoring system includes an integrated sensor 201. The integrated sensor 201 is arranged on the inspection equipment box body 108 and is used for monitoring the concentration, temperature and humidity of oxygen, carbon dioxide and phosphine gas. The data of the integrated sensor 201 is transmitted to the microprocessor 302 through the RS485 communication port, and the microprocessor 302 adds coordinate information to the data of the integrated sensor.
As shown in fig. 4, the method for positioning the unmanned grain inspection device based on the RFID comprises the following steps:
step 2, acquiring an actual position based on an inertial positioning unit and an encoder;
step 3, identifying information of the RFID tag in the polling process by the grain surface unmanned polling device based on the RFID to obtain the current coordinate position;
and 4, checking the actual position by adopting the current coordinate position, and correcting the error of the actual position.
The RFID label is internally pre-stored with the position information of the inspection equipment under the world coordinate, when the RFID reader-writer identifies the RFID label, the current coordinate position is obtained, the current coordinate position is checked with the actual position obtained by the inertial positioning unit and the encoder, the position error of the actual position is corrected, and the pose information of the inspection equipment is more accurate.
As shown in fig. 5, the RFID tag is laid at a corner, and the inspection device identifies information of the RFID tag in the inspection process, so as to obtain a current coordinate position; and related pose transformation instructions can be obtained to finish operations such as steering and the like.
The step 2 specifically comprises the following steps: the encoder can confirm the position data of equipment of patrolling and examining according to the number of turns of the motor and the diameter of patrolling and examining equipment wheel. The inertial positioning unit carries out integral operation on angular velocity to obtain a corner, carries out continuous integral calculation on acceleration to obtain displacement, obtains current position information of the inspection equipment, and then converts an inertial positioning coordinate system into a world coordinate system through a coordinate conversion matrix to obtain the attitude and coordinate information of the inspection equipment. And (3) fusing the coordinates obtained by the inertial positioning unit and the encoder through the prediction and correction process of the Kalman filtering algorithm to obtain the actual position of the inspection equipment.
Further, the patrol inspection equipment stops moving at fixed time or fixed distance, the grain surface is photographed through the pest monitoring camera, and position information is added to the photographed picture based on the position of the patrol inspection equipment. The specific position of the pest can be known by carrying out image analysis on the acquired picture, and the specific image analysis method is not repeated herein.
Claims (10)
1. Unmanned inspection device of grain face based on RFID, including the treater, respectively with treater signal connection's actuating system, navigation positioning system and information acquisition system, be provided with the motor among the actuating system, its characterized in that, navigation positioning system includes: the device comprises an inertial positioning unit, a visual navigation camera, an RFID read-write unit and an encoder, wherein the encoder is installed on a motor, and the inertial positioning unit, the visual navigation camera, the RFID read-write unit and the encoder are in signal connection with a processor.
2. The RFID-based unmanned aerial vehicle inspection device of claim 1, wherein the inertial positioning unit comprises: an accelerometer and a gyroscope.
3. The unmanned inspection device of grain surface based on RFID of claim 1, wherein the information acquisition system includes a pest monitoring camera, a camera bracket and a light supplement lamp, the pest monitoring camera is disposed on the camera bracket.
4. The unmanned inspection device of grain surface based on RFID of claim 1, further comprising an ultrasonic ranging sensor.
5. The unmanned grain surface inspection device based on RFID according to any one of claims 1-4, further comprising a temperature, humidity and gas concentration monitoring system.
6. The RFID-based grain surface unmanned inspection device positioning method is applied to any one of claims 1 to 5, and is characterized by comprising the following steps:
step 1, setting an inspection route based on distribution conditions of granary walkways, and laying an RFID tag on the inspection route;
step 2, acquiring an actual position based on an inertial positioning unit and an encoder;
step 3, identifying information of the RFID tag in the polling process by the grain surface unmanned polling device based on the RFID to obtain the current coordinate position;
and 4, checking the actual position by adopting the current coordinate position, and correcting the error of the actual position.
7. The RFID-based grain surface unmanned inspection device positioning method according to claim 6, wherein the step 2 specifically comprises:
step 21, obtaining the position X based on the inertial positioning unit k ；
Step 22, obtaining the position Z based on the encoder k ；
Step 23, fusing X through the estimation and correction process of Kalman filtering algorithm k And Z k And obtaining the actual position.
8. The RFID-based grain surface unmanned inspection device positioning method according to claim 6, further comprising step 5 of periodically shooting grain surfaces and giving position information during shooting.
9. The RFID-based grain surface unmanned inspection device positioning method according to claim 8, wherein the grain surface is photographed at regular intervals in the step 5 when the grain surface is photographed specifically at a specified time or distance.
10. The RFID-based grain surface unmanned inspection device positioning method according to claim 8 or 9, further comprising a step 6 of performing pest identification based on the image shot in the step 5 to determine pest occurrence positions.
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