Autonomous unloading and mine truck fullness rate detection device for unmanned shovel loading robot

Abstract

The invention belongs to the field of shovel loading robot autonomous unloading, mine truck full-fill rate detection and application thereof, and particularly relates to an unmanned shovel loading robot autonomous unloading and mine truck full-fill rate detection device. Comprising a host which is arranged in a far-end operation room and comprises a processor, a display screen, a wireless data transmission module, a control keyboard and a direct-current stabilized power supply; the device A is mounted in the center of a box body of the shovel loading robot and is integrated with a Beidou positioning module A and a posture detection module A; the device B is mounted at the center of the lower part of the mine truck compartment bucket and is integrated with a Beidou positioning module B and an attitude detection module B; the device C is mounted on a big arm saddle of the shoveling robot, and an included angle between a bucket rod and a big arm is measured through the integrated tilt angle sensor C; the device D is mounted on a bucket rod driving mechanism, and the telescopic length of the bucket rod is detected through an integrated encoder D; the device E is mounted on two sides of a big arm head sheave and is integrated with a Beidou positioning module E and a laser radar E; and the base station is deployed at a high position of a construction environment.

Description

Technical Field

[0001] The invention belongs to the fields of autonomous discharging of a loading robot and detection of the full-bucket rate of a dump truck and its applications, and specifically relates to a device for autonomous discharging of an unmanned loading robot and detection of the full-bucket rate of a dump truck. Background Art

[0002] In the application scenarios of unmanned loading robots, the autonomous discharging of the loading robot and the detection of the full-bucket rate of the dump truck are still key problems that need to be urgently solved. Taking the discharging link as an example, when the loading robot is in an unmanned operation state, it is quite challenging to accurately move the bucket to the predetermined discharging position. Since it is difficult to achieve it in one step, there are often large initial position deviations of the bucket. At this time, the bucket needs to be lifted again and adjusted repeatedly until it accurately reaches the appropriate position, which will result in a large amount of time consumption and affect the overall operation efficiency. Moreover, during the discharging process, the external perception of the bucket is extremely vulnerable to various complex external factors. For example, the dust in the open-pit coal mine site may obscure the sensor's line of sight and affect its judgment of the bucket's position and attitude; strong light changes, whether it is direct strong light or shadow occlusion, may cause misjudgment of the visual monitoring system; in addition, factors such as mechanical vibration and electromagnetic interference will also cause fluctuations in the data collected by the sensor, thereby reducing the accuracy of the bucket attitude monitoring and bringing great uncertainty to the unmanned loading operation. In addition, how to accurately detect the full-bucket rate of the materials in the dump truck when the loading robot is in an unmanned operation state is a major problem that needs to be urgently solved. At present, the scheme of installing sensing devices such as sensors on the top of the dump truck is not feasible because the material splashing is very likely to damage the equipment. Subsequently, not only a large amount of manpower and material resources will be consumed for maintenance, but also the overall operation efficiency will be seriously affected.

[0003] At present, the bucket discharging of the loading robot still adopts traditional methods, and the operator needs to continuously adjust the bucket's position and attitude to match the predetermined position of the dump truck below. However, for the dump truck below, if it is not accurate, even a slight deviation will cause casualties to the dump truck driver. In addition, the current method is still to identify the loading situation of the dump truck materials by the operator in the cab, and the efficiency is relatively low. The invention can effectively solve the problem of accurate discharging of the unmanned loading robot, timely and effectively adjust the bucket position, and ensure accurate cooperation with the dump truck. At the same time, accurately identify the loading situation of the dump truck materials and ensure that the full-bucket rate of the dump truck meets the required standards. Summary of the Invention

[0004] In order to solve the above problems, the invention provides a device for autonomous discharging of an unmanned loading robot and detection of the full-bucket rate of a dump truck.

[0005] The invention adopts the following technical solutions: A device for autonomous discharging of an unmanned loading robot and detection of the full-bucket rate of a dump truck, comprising: The host computer, which is located in the remote operation room, includes a processor, a display screen, a wireless data transmission module, a control keyboard, and a DC regulated power supply; Device A is installed at the center of the loading robot box body, integrating Beidou positioning module A and attitude detection module A, and is used for calculating the absolute position and attitude of the loading robot; Device B is installed at the center of the lower part of the mining truck box, integrating Beidou positioning module B and attitude detection module B, and is used for calculating the absolute position and attitude of the mining truck; Device C is installed on the saddle of the loading robot's boom, and measures the angle between the dipper stick and the boom through the integrated inclination sensor C; Device D is installed on the dipper stick drive mechanism, and detects the telescopic length of the dipper stick through the integrated encoder D; Device E is installed on both sides of the boom sheave, integrating Beidou positioning module E and lidar E, and is used for positioning the unloading range and three-dimensional modeling of the full bucket rate of the mining truck; The base station is deployed at a high place in the construction environment; Among them, the host computer communicates with devices A - E through the wireless data transmission module, and receives and displays the position, attitude, dipper stick angle, dipper stick telescopic length, and full bucket rate data of the loading robot in real time; Devices A, B, and E perform differential positioning correction through the base station, and device E scans the mining truck box through the lidar to establish a three-dimensional point cloud model to calculate the full bucket rate.

[0006] In some embodiments, the host computer includes a processor, a display screen, a wireless data transmission module, a control keyboard, and a DC regulated power supply, and the display screen displays the longitude and latitude coordinates, rotation angle, dipper stick angle, telescopic length, and full bucket rate of the loading robot in real time.

[0007] In some embodiments, devices A, B, C, D, and E are connected to each component through mounting brackets. The mounting brackets are of a ring structure, and the center is connected to the outer ring and the central geometry through evenly distributed strips. The strip length is ≥500 mm and includes an outward-expanded diamond-shaped groove, and the included angle between adjacent strips is 120° or 180°; each module is fixed at the groove.

[0008] In some embodiments, the mounting brackets are fixed by two-component acrylate glue or neodymium iron boron permanent magnet arrays, and the magnet combinations are adapted to the curvature of the mechanical surface.

[0009] In some embodiments, Device A includes Microprocessor A, Wireless Data Transmission Module A, Beidou Positioning Module A, Attitude Detection Module A, Power Conversion Module A, and Lithium Battery A. Among them, Wireless Data Transmission Module A, Beidou Positioning Module A, and Attitude Detection Module A are respectively connected to Microprocessor A. The output end of Power Conversion Module A is connected to the power supply ends of Beidou Positioning Module A and Microprocessor A, and the input end is connected to Lithium Battery A. Device A is fixed at the central position of the loading robot box through the Device A mounting bracket. Device B includes Microprocessor B, Wireless Data Transmission Module B, Beidou Positioning Module B, Attitude Detection Module B, Power Conversion Module B, and Lithium Battery B. Among them, Wireless Data Transmission Module B, Beidou Positioning Module B, and Attitude Detection Module B are respectively connected to Microprocessor B. The output end of Power Conversion Module B is connected to the power supply ends of Beidou Positioning Module B and Microprocessor B, and the input end is connected to Lithium Battery B. Device B is fixed at the central position of the lower part of the mining truck box through the Device B mounting bracket. Device C includes Microprocessor C, Wireless Data Transmission Module C, Inclinometer C, Power Conversion Module C, and Lithium Battery C. Among them, Wireless Data Transmission Module C and Inclinometer C are respectively connected to Microprocessor C. The output end of Power Conversion Module C is connected to the power supply ends of Inclinometer C and Microprocessor C, and the input end is connected to Lithium Battery C. Device C is fixed at the saddle position of the boom of the loading robot through the Device C mounting bracket. Device D includes Microprocessor D, Wireless Data Transmission Module D, Encoder D, Power Conversion Module D, and Lithium Battery D. Among them, Wireless Data Transmission Module D and Encoder D are respectively connected to Microprocessor D. The output end of Power Conversion Module D is connected to the power supply ends of Encoder D and Microprocessor D, and the input end is connected to Lithium Battery D. Device E includes Microprocessor E, Wireless Data Transmission Module E, Beidou Positioning Module E, LiDAR E, Power Conversion Module E, and Lithium Battery E. Among them, Wireless Data Transmission Module E and LiDAR E are respectively connected to Microprocessor E. The output end of Power Conversion Module B is connected to the power supply ends of LiDAR E and Microprocessor E, and the input end is connected to Lithium Battery E. The base station includes Microprocessor F, Wireless Data Transmission Module F, Beidou Positioning Module F, Power Conversion Module F, and Power Supply F. Among them, Wireless Data Transmission Module F and Beidou Positioning Module F are respectively connected to Microprocessor F. The output end of Power Conversion Module F is connected to the power supply ends of Beidou Positioning Module F and Microprocessor F, and the input end is connected to the power supply.

[0010] In some embodiments, the Beidou positioning modules of Device A, Device B, and Device E adopt a differential positioning algorithm, including: The base station sends its carrier phase observations and position information to Device A, Device B, and Device E; Device A, Device B, and Device E receive the Beidou satellite carrier phase and the base station carrier phase, form the phase differential observation values, and calculate the corrected coordinates in real time.

[0011] In some embodiments, an accelerometer and a gyroscope are integrated into Attitude Detection Module A and Attitude Detection Module B, and the gyroscope angular velocity and accelerometer gravity component data are fused by the following method, including: In the working environment of the loading robot, the angle change output by the gyroscope of Device A is , and the angle measured by the accelerometer is . According to the requirements of the working environment, the complementary filtering weighting coefficient is set, , and the angles measured by the gyroscope and the accelerometer are fused to obtain the fused angle , , is the angle of the previous fusion on Device A; The angle change output by the gyroscope of Device B is , and the angle measured by the accelerometer is . According to the requirements of the working environment, the complementary filtering weighting coefficient is set, , and the angles measured by the gyroscope and the accelerometer are fused to obtain the fused angle , , is the angle of the previous fusion on Device B.

[0012] In some embodiments, based on the data of LiDAR E, a material distribution model inside the mining truck is constructed and precise positioning is achieved, and the recognition of the full bucket rate of the mining truck material is realized in the working environment of the loading robot, including the following steps: 1) LiDAR data processing: Receive the LiDAR scan data, preprocess the LiDAR data, and remove noise and outliers; 2) Scan matching: Match the LiDAR scan data with the constructed material distribution model inside the mining truck, find the best pose estimate of the current scan in the material distribution model inside the mining truck, and determine the position and pose of the LiDAR by optimizing the objective function to minimize the distance error between the current scan points and the points in the material distribution model inside the mining truck during the scan matching process; 3) Sub-model construction: Divide the material distribution model inside the mining truck into multiple sub-models, fuse the LiDAR scan data into the sub-models according to the pose information obtained by the LiDAR scan matching. The sub-models are represented by occupancy grid models, and each grid cell represents a small area inside the mining truck, and its value represents the probability that the area is occupied by an object; 4) Construct a globally consistent material distribution model; 5) Calculation of the full-load rate of the mining truck: According to the design specifications of the mining truck, determine the spatial shape and size of the mining truck when it is theoretically fully loaded, and construct an ideal full-load volume model with a volume of ; Use the sub-model represented by the constructed occupancy grid model to calculate the volume of each sub-model; Since each grid cell represents a small area inside the mining truck, assuming the volume of each grid cell is v, by traversing all grid cells in the sub-model and counting the number of grid cells n with the probability of being occupied by an object greater than the set threshold, the volume of the material in each sub-model ; Add up the volumes of the materials in all sub-models to obtain the total volume of the materials inside the current mining truck , and the calculation formula for the full-load rate of the mining truck is ; By substituting the current material volume and the full-load volume into the formula, the full-load rate of the mining truck can be obtained.

[0013] In some embodiments, step 2) includes: First, perform initialization. Let the data point set scanned by the lidar be , the data point set of the material distribution model inside the mining truck be , and the pose of the lidar be matrix, and the pose includes the rotation matrix quantity and the translation vector ; Then, perform iterative optimization on the above data using the ICP algorithm to find the data point point in the lidar scan that is closest to the material distribution model point point inside the mining truck, measure the distance between the two, and calculate the objective function according to . Use singular value decomposition to update the pose of the lidar according to the objective function , and the obtained is the optimal pose estimation of the lidar in the material distribution model inside the mining truck.

[0014] In some embodiments, step 4) includes: Local point cloud acquisition. The loading robot is equipped with a lidar and continuously acquires the point cloud data of the materials inside the mining truck during the movement process to obtain the local point cloud set ; Perform local model creation, and use adjacent local point clouds and , through the point cloud stitching algorithm, a local material distribution model is initially constructed, and at the same time, the pose is estimated using the pose sensor of the loading robot. ; Perform loop detection. For each local model, extract simple features. When the loading robot moves to a new position and obtains a new local model after that, compare its features with the features of all previously saved local models to further check the geometric similarity of the point cloud; Perform global model optimization, construct an optimized graph, and use the pose of the robot as nodes and the transformation relationship between poses as edges to construct a graph; For the pose transformation estimated based on the motion sensor, the error function is defined by the difference between the estimated pose and the pose matching the actual scanned point cloud; for the pose relationship obtained from loop detection, the error function is defined based on the matching error of the point cloud at the loop; the total error function is , and using the gradient descent method, iteratively adjust the pose nodes to minimize the total error function E, thereby updating the pose, eliminating or reducing the cumulative error, and thus constructing a globally consistent material distribution model.

[0015] Compared with the prior art, the present invention has the following beneficial effects: In terms of operation efficiency, it can significantly shorten the time consumed in the unloading link of the loading operation process, promote the acceleration of the overall operation process, and greatly improve the work output per unit time. From the dimension of operation accuracy, the device can provide strong precise positioning support for the loading operation, effectively help the unmanned loading robot to accurately control the operation accuracy of the bucket within the centimeter level, and ensure the accuracy and reliability of each loading action. In terms of material detection, the device can detect the materials in the mining truck without affecting the normal operation of the loading robot, reducing the time consumed in the material detection link of the mining truck. At the same time, during the actual loading operation process, there is no need for ground staff to perform cumbersome auxiliary command operations, which not only greatly reduces the heavy labor intensity of ground staff, but also more powerfully guarantees the life and property safety of ground personnel. Description of the Drawings

[0016] Figure 1 is the system structure schematic diagram of the present invention; Figure 2 is the top view of the present invention; Figure 3 is the side view of the present invention; Figure 4 is the front view of the present invention; Figure 5 It is a schematic diagram of the installation of the sensing device of the present invention; In the figure: 1 - host; 2 - device A; 3 - device B; 4 - device C; 5 - device D; 6 - device E; 7 - base station; 8 - loading robot; 9 - mining truck; 101 - processor; 102 - display screen; 103 - wireless data transmission module; 104 - host control keyboard; 105 - DC regulated power supply; 201 - microprocessor A; 202 - wireless data transmission module A; 203 - Beidou positioning module A; 204 - attitude detection module A; 205 - power conversion module A; 206 - lithium battery A; 207 - device A mounting bracket; 301 - microprocessor B; 302 - wireless data transmission module B; 303 - Beidou positioning module B; 304 - attitude detection module B; 305 - power conversion module B; 306 - lithium battery B; 307 - device B mounting bracket; 401 - microprocessor C; 402 - wireless data transmission module C; 403 - inclinometer; 404 - power conversion module C; 405 - lithium battery C; 406 - device C mounting bracket; 501 - microprocessor D; 502 - wireless data transmission module D; 503 - encoder; 504 - power conversion module D; 505 - lithium battery D; 506 - device D mounting bracket; 601 - microprocessor E; 602 - wireless data transmission module E; 603 - Beidou positioning module E; 604 - lidar; 605 - power conversion module E; 606 - lithium battery E; 607 - device E mounting bracket; 701 - microprocessor F; 702 - wireless data transmission module F; 703 - Beidou positioning module F; 704 - power conversion module F; 705 - power supply. Detailed implementation manners

[0017] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] An unmanned loading robot automatic unloading and mining truck full - bucket rate detection device, comprising: A host 1, arranged in a remote operation room, including a processor 101, a display screen 102, a wireless data transmission module 103, a control keyboard 104 and a DC regulated power supply 105; The device A2 is installed at the center of the box body of the loading robot, integrating the Beidou positioning module A203 and the attitude detection module A204, and is used for calculating the absolute position and attitude of the loading robot; The device B3 is installed at the center of the lower part of the mining truck box, integrating the Beidou positioning module B303 and the attitude detection module B304, and is used for calculating the absolute position and attitude of the mining truck; The device C4 is installed on the saddle of the boom of the loading robot, and measures the included angle between the stick and the boom through the integrated inclination sensor C403; The device D5 is installed on the stick driving mechanism, and detects the telescopic length of the stick through the integrated encoder D503; The device E6 is installed on both sides of the boom sheave, integrating the Beidou positioning module E603 and the lidar E604, and is used for unloading range positioning and three-dimensional modeling of the full bucket rate of the mining truck; The base station 7 is deployed at a high place in the construction environment; Among them, the host communicates with the devices A-E through the wireless data transmission module, and receives and displays the position, attitude, stick angle, stick telescopic length and full bucket rate data of the loading robot in real time; the devices A, B, and E perform differential positioning correction through the base station, and the device E scans the mining truck box through the lidar to establish a three-dimensional point cloud model to calculate the full bucket rate.

[0019] In some embodiments, the host 1 includes a processor 101, a display screen 102, a wireless data transmission module 103, a control keyboard 104 and a DC regulated power supply 105, and the display screen 102 displays the longitude and latitude coordinates, rotation angle, stick angle, telescopic length and full bucket rate of the loading robot in real time.

[0020] In some embodiments, the devices A2, B3, C4, D5, and E6 are connected to each component through a mounting bracket. The mounting bracket is a ring structure, and the center is connected to the outer ring and the central geometry through evenly distributed strips. The strip length is ≥500mm and includes an outward-expanded diamond-shaped groove, and the included angle between adjacent strips is 120° or 180°; each module is fixed at the groove.

[0021] In some embodiments, the mounting bracket is fixed by two-component acrylate glue or a neodymium iron boron permanent magnet array, and the magnet combination adapts to the curvature of the mechanical surface.

[0022] In some embodiments, device A2 includes a microprocessor A201, a wireless data transmission module A202, a Beidou positioning module A203, an attitude detection module A204, a power conversion module A205, and a lithium battery A206. Among them, the wireless data transmission module A202, the Beidou positioning module A203, and the attitude detection module A204 are respectively connected to the microprocessor A201. The output end of the power conversion module A205 is connected to the power supply end of the Beidou positioning module A203 and the power supply end of the microprocessor A201, and the input end is connected to the lithium battery A206. Device A is fixed at the central position of the loading robot box body through the device A mounting bracket. The device B3 includes a microprocessor B301, a wireless data transmission module B302, a Beidou positioning module B303, an attitude detection module B304, a power conversion module B305, and a lithium battery B306. Among them, the wireless data transmission module B302, the Beidou positioning module B303, and the attitude detection module B304 are respectively connected to the microprocessor B301. The output end of the power conversion module B305 is connected to the power supply end of the Beidou positioning module B303 and the power supply end of the microprocessor B301, and the input end is connected to the lithium battery B306. Device B is fixed at the central position of the lower part of the mining truck box through the device B mounting bracket. The device C4 includes a microprocessor C401, a wireless data transmission module C402, an inclination sensor C403, a power conversion module C404, and a lithium battery C405. Among them, the wireless data transmission module C402 and the inclination sensor C403 are respectively connected to the microprocessor C401. The output end of the power conversion module C404 is connected to the power supply end of the inclination sensor C403 and the power supply end of the microprocessor C401, and the input end is connected to the lithium battery C405. Device C is fixed at the saddle position of the boom of the loading robot through the device C mounting bracket. The device D5 includes a microprocessor D501, a wireless data transmission module D502, an encoder D503, a power conversion module D504, and a lithium battery D505. Among them, the wireless data transmission module D502 and the encoder D503 are respectively connected to the microprocessor D501. The output end of the power conversion module D504 is connected to the power supply end of the encoder D503 and the power supply end of the microprocessor D501, and the input end is connected to the lithium battery D505. The device E6 includes a microprocessor E601, a wireless data transmission module E602, a Beidou positioning module E603, a lidar E604, a power conversion module E605, and a lithium battery E606. Among them, the wireless data transmission module E602 and the lidar E604 are respectively connected to the microprocessor E601. The output end of the power conversion module B605 is connected to the power supply end of the lidar E604 and the power supply end of the microprocessor E601, and the input end is connected to the lithium battery E606. The base station 7 includes a microprocessor F701, a wireless data transmission module F702, a Beidou positioning module F703, a power conversion module F704, and a power supply F705. Among them, the wireless data transmission module F702 and the Beidou positioning module F703 are respectively connected to the microprocessor F701. The output end of the power conversion module F704 is connected to the power supply end of the Beidou positioning module F703 and the power supply end of the microprocessor F701, and the input end is connected to the power supply 705.

[0023] Specifically, the display screen is connected to the SPI interface of the processor through the SPI bus data line port. The wireless data transmission module is connected to the host processor through the UART serial line. The host control keyboard is connected to the GPIO of the processor. The DC regulated power supply is connected to the power pin of the processor through a transformer. The host is located in the remote operation room outside the loading robot. The microprocessor A of device A is connected to the Beidou positioning module A through the UART serial line. The output end of the power conversion module A is respectively connected to the power supply ends of the Beidou positioning module A and the microprocessor A, and the input end is connected to the lithium battery A of device A. The attitude detection module A is connected to the microprocessor A through the IIC bus and is fixed to the upper panel through bolts by means of the device fixing through holes. The wireless data transmission module A is connected to the microprocessor A through the UART serial line. The Beidou positioning module of device A is fixed to the upper panel through the fixing through holes, and device A is fixed at the central position of the loading robot box body. The microprocessor B of device B is connected to the Beidou positioning module B through the UART serial line. The output end of the power conversion module B is respectively connected to the power supply ends of the Beidou positioning module B and the microprocessor B, and the input end is connected to the lithium battery B of device B. The attitude detection module B is connected to the microprocessor B through the IIC bus and is fixed to the upper panel through bolts by means of the device fixing through holes. The wireless data transmission module B is connected to the microprocessor B through the UART serial line. The Beidou positioning module of device B is fixed to the upper panel through the fixing through holes, and device B is fixed at the central position below the mining truck box. The microprocessor C of device C is connected to the inclination sensor through the UART serial line. The output end of the power conversion module C is respectively connected to the power supply ends of the inclination sensor and the microprocessor C, and the input end is connected to the lithium battery C of device C. Device C is fixed to the upper panel through bolts by means of the device fixing through holes. The wireless data transmission module C is connected to the microprocessor C through the UART serial line. Device C is fixed at the saddle position of the boom of the loading robot. The microprocessor D of device D is connected to the inclination sensor through the UART serial line. The output end of the power conversion module D is respectively connected to the encoder and the power supply end of the microprocessor D, and the input end is connected to the lithium battery D of device D. Device D is fixed to the upper panel through bolts by means of the device fixing through holes. The wireless data transmission module D is connected to the microprocessor D through the UART serial line. Device D is fixed at the drive mechanism position of the bucket rod of the loading robot. The microprocessor E of device E is connected to the Beidou positioning module E through the UART serial line. The output end of the power conversion module E is respectively connected to the power supply ends of the Beidou positioning module E and the microprocessor E, and the input end is connected to the lithium battery E of device E. The lidar E is connected to the microprocessor E through the IIC bus and is fixed to the upper panel through bolts by means of the device fixing through holes. The wireless data transmission module E is connected to the microprocessor E through the UART serial line. The Beidou positioning module of device E is fixed to the upper panel through the fixing through holes, and device E is fixed on both sides of the skywheel at the top of the boom of the loading robot.The host, Device A, Device B, Device C, Device D, and Device E share data through a wireless data transmission module. This unique layout can significantly improve the accuracy of the autonomous unloading of the loading robot and the detection of the full bucket rate of the mining truck, effectively enhancing the operation efficiency of the loading robot.

[0024] The wireless data transmission module uses a half-duplex 2.4GHz - 2.5GHz nRF24L01+PA+LNA wireless communication module, which is connected and communicates with the embedded processor through SPI interface pins (including the CSN chip select pin, SCK clock signal pin, MOSI communication output pin, and MISO communication input pin).

[0025] The Beidou positioning module used in the present invention adopts the Huixin Xingtong UM982 chip, and the Beidou positioning module uses an active ceramic antenna. This antenna integrates a surface acoustic wave filter (SAW), an ultra-low noise amplifier, and a high-performance ultra-wideband low-noise amplifier for preprocessing the received Beidou positioning signal.

[0026] The attitude detection module integrates a 3-axis magnetometer AK09911C chip and a 6-axis inertial measurement unit (a 3-axis accelerometer LIS3DHTR chip and a 3-axis gyroscope MPU6050 chip). The module is connected to the pins of the microprocessor through an I2C interface (consisting of a data line SDA and a clock line SCL).

[0027] The ground inclination sensor uses the Posital Titlx series, which has added a new triaxial MEMS accelerometer, and the measurement accuracy can be improved to ±0.1 degrees. It has multiple interfaces, facilitating the measurement of the tilt angle of the loading robot's dipper stick.

[0028] The ground encoder uses the Posital OCE series, which has multiple interfaces and supports multiple communication protocols, facilitating connection and communication with various automation control systems.

[0029] The ground lidar uses the CUMT Guj30 mine-intrinsic safety lidar, which has high horizontal resolution, high vertical resolution, and high ranging accuracy, and integrates fiber optic communication. It can operate under various complex working conditions to detect the full bucket rate of the mining truck's bucket in real time.

[0030] The microprocessor uses a minimum system board with the STM32F407 chip of the STM series as the core.

[0031] The adopted TPS7A4501 voltage regulator chip stabilizes the voltage of the lithium battery at 5V, providing a stable working voltage for the microprocessor and the Beidou positioning module.

[0032] Microprocessors 2 and 3 use a 64-core 1.8GHz Orange Pi 3B control board that uses the Orange Pi OS (Arch) operating system.

[0033] The display module is an ILI9488 3.5-inch color screen that uses SPI4-wire mode. It is connected to the MCU through the SPI interface to realize color graphic information display.

[0034] The application framework of VxWorks real-time operating system is transplanted on the system with STM32F429IGT6 chip as the core to realize different task scheduling. The graphics support system in Qt forEmbedded Linux embedded applications is transplanted on the basis of hardware driver and VxWorks real-time operating system. Qt for Embedded Linux can provide efficient independent graphical user interface for any application using LCD graphics display, and is suitable for real display or virtual display of any size under any LCD controller and CPU. It is used to display the bucket posture and positioning information of unmanned shovel loading robot and interactive interface.

[0035] The Beidou positioning module of device A2, device B3, and device E6 adopts a differential positioning algorithm, including: Base station 7 sends its carrier observation and location information to device A2, device B3, and device E6; Device A2, device B3, and device E6 receive the Beidou satellite carrier phase and the base station carrier phase, form phase difference observation values, and calculate the corrected coordinates in real time.

[0036] In some embodiments, the posture detection module A204 and the posture detection module B304 integrate an accelerometer and a gyroscope, and fuse the gyroscope angular velocity and the accelerometer gravity component data by the following method, including: In the shovel robot operation environment, the angle change of the gyroscope output of device A is , the angle measured by the accelerometer is , according to the requirements of the operating environment, set the complementary filter weighting coefficient , , the angles measured by the gyroscope and accelerometer are integrated to obtain the integrated angle , , is the angle of the last fusion of device A; The angle change of the gyroscope output of device B is , the angle measured by the accelerometer is , according to the requirements of the operating environment, set the complementary filter weighting coefficient , , the measured angles of the gyroscope and accelerometer are fused to obtain the fused angle. , , is the angle of the previous fusion of device B.

[0037] In some embodiments, a material distribution model inside the mining truck is constructed based on the lidar E604 data and precise positioning is achieved, and the recognition of the full bucket rate of the materials in the mining truck is realized in the working environment of the loading robot, including the following steps: 1 Lidar data processing: Receive lidar scan data, preprocess the lidar data to remove noise and outliers; 2 Scan matching: Match the lidar scan data with the constructed material distribution model inside the mining truck to find the best pose estimate of the current scan in the material distribution model inside the mining truck. The scan matching process determines the position and attitude of the lidar by optimizing the objective function and minimizing the distance error between the current scan points and the points in the material distribution model inside the mining truck; 3 Sub-model construction: Divide the material distribution model inside the mining truck into multiple sub-models, fuse the lidar scan data into the sub-models according to the pose information obtained by lidar scan matching. The sub-models are represented by occupancy grid models, and each grid cell represents a small area inside the mining truck, and its value represents the probability that the area is occupied by an object; 4 Construct a globally consistent material distribution model; 5 Calculation of the full bucket rate of the mining truck: According to the design specifications of the mining truck, determine the spatial shape and size of the mining truck when it is theoretically fully loaded, and construct an ideal full-load volume model with a volume of ; Use the sub-models represented by the constructed occupancy grid models to calculate the volume of each sub-model; Since each grid cell represents a small area inside the mining truck, assuming the volume of each grid cell is v, by traversing all the grid cells in the sub-model and counting the number n of grid cells with the probability of being occupied by an object greater than the set threshold, the volume of the material in each sub-model is ; Add up the volumes of the materials in all sub-models to obtain the total volume of the materials inside the current mining truck , and the calculation formula for the full bucket rate of the mining truck is ; By substituting the current material volume and the full-load volume into the formula, the full bucket rate of the mining truck can be obtained.

[0038] In some embodiments, step 2 includes: First, perform initialization. Let the set of data points scanned by the lidar be , the set of points in the material distribution model inside the mining truck be , and the pose of the lidar be Matrix, pose Including a rotation matrix quantity And a translation vector ; Then, perform iterative optimization on the above data using the ICP algorithm to find the data points scanned by the lidar The point corresponding to the point with the closest distance in the material distribution model inside the mining truck Point, measure the distance between the two , according to Calculate the objective function , use singular value decomposition according to the objective function Update the pose of the lidar , the obtained Is the optimal pose estimation of the lidar in the material distribution model inside the mining truck

[0039] In some embodiments, step 4 includes: Local point cloud acquisition. The loading robot is equipped with a lidar and continuously acquires the point cloud data of the materials inside the mining truck during movement to obtain a local point cloud set ; Perform local model creation. Use adjacent local point clouds And , through the point cloud stitching algorithm, initially construct a local material distribution model, and at the same time use the attitude sensor of the loading robot to calculate the pose ; Perform loop detection. For each local model, extract simple features. When the loading robot moves to a new position and obtains a new local model After that, compare its features with the features of all previously saved local models To further check the geometric similarity of the point cloud; Perform global model optimization. Construct an optimization graph, use the pose of the robot As nodes and the transformation relationship between poses as edges to construct a graph; For the pose transformation estimated based on the motion sensor, the error function Is defined by the difference between the estimated pose and the actual scanned point cloud matching pose; for the pose relationship obtained from loop detection, the error function Is defined based on the matching error of the point cloud at the loop; the total error function is , use the gradient descent method to iteratively adjust the pose nodes , minimize the total error function E, so as to update the pose, eliminate or reduce the cumulative error, and thus construct a globally consistent material distribution model

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An unmanned shovel loading robot autonomous unloading and mining truck full bucket rate detection device, characterized in that: include: A host (1) is arranged in a remote operation room, comprising a processor (101), a display screen (102), a wireless data transmission module (103), a control keyboard (104) and a DC regulated power supply (105); The device A (2) is installed at the center of the shovel loading robot box, and integrates a Beidou positioning module A (203) and a posture detection module A (204) for calculating the absolute position and posture of the shovel loading robot; The device B (3) is installed at the center of the lower part of the mining truck compartment, and integrates a Beidou positioning module B (303) and a posture detection module B (304) for calculating the absolute position and posture of the mining truck; The device C (4) is installed on the saddle of the upper arm of the shovel loading robot, and measures the angle between the bucket arm and the upper arm through the integrated inclination sensor C (403); The device D (5) is installed on the boom drive mechanism and detects the telescopic length of the boom through an integrated encoder D (503); The device E (6) is installed on both sides of the boom sheave, and integrates a Beidou positioning module E (603) and a laser radar E (604) for positioning the unloading range and three-dimensional modeling of the full bucket rate of the mining truck; The base station (7) is deployed at a high location in the construction environment; Among them, the host communicates with device AE through the wireless data transmission module, and receives and displays the position, posture, dipper angle, dipper extension length and full bucket rate data of the shovel robot in real time; device A, device B, and device E perform differential positioning correction through the base station, and device E scans the dipper bucket of the dipper truck through the laser radar to establish a three-dimensional point cloud model to calculate the full bucket rate.

2. The unmanned shovel loading robot autonomous unloading and mining truck full bucket rate detection device according to claim 1 is characterized in that: The host (1) comprises a processor (101), a display screen (102), a wireless data transmission module (103), a control keyboard (104) and a DC regulated power supply (105); the display screen (102) displays the latitude and longitude coordinates, rotation angle, arm angle, telescopic length and full bucket rate of the shovel robot in real time.

3. The unmanned shovel loading robot autonomous unloading and mining truck full bucket rate detection device according to claim 1 is characterized in that: The device A (2), the device B (3), the device C (4), the device D (5), and the device E (6) are connected to each component via a mounting bracket. The mounting bracket is a ring structure, and the center is connected to the outer ring and the central geometric body via evenly distributed strips. The strip length is ≥500 mm and contains outwardly expanding diamond grooves. The angle between adjacent strips is 120° or 180°. Each module is fixed at the groove.

4. The unmanned shovel loading robot autonomous unloading and mining truck full bucket rate detection device according to claim 3 is characterized in that: The mounting bracket is fixed by two-component acrylic adhesive or a NdFeB permanent magnet array, and the magnet assembly is adapted to the curvature of the mechanical surface.

5. The unmanned shovel loading robot autonomous unloading and mining truck full bucket rate detection device according to claim 1 is characterized in that: The device A (2) comprises a microprocessor A (201), a wireless data transmission module A (202), a Beidou positioning module A (203), a posture detection module A (204), a power conversion module A (205), and a lithium battery A (206); wherein the wireless data transmission module A (202), the Beidou positioning module A (203), and the posture detection module A (204) are respectively connected to the microprocessor A (201); the output end of the power conversion module A (205) is connected to the power end of the Beidou positioning module A (203) and the power end of the microprocessor A (201); and the input end is connected to the lithium battery A (206); the device A is fixed to the center of the box of the shovel loading robot through a device A mounting bracket; The device B (3) comprises a microprocessor B (301), a wireless data transmission module B (302), a Beidou positioning module B (303), a posture detection module B (304), a power conversion module B (305), and a lithium battery B (306); wherein the wireless data transmission module B (302), the Beidou positioning module B (303), and the posture detection module B (304) are respectively connected to the microprocessor B (301); the output end of the power conversion module B (305) is connected to the power end of the Beidou positioning module B (303) and the power end of the microprocessor B (301); and the input end is connected to the lithium battery B (306); the device B is fixed to the central position of the lower part of the mining truck compartment through a device B mounting bracket; The device C (4) comprises a microprocessor C (401), a wireless data transmission module C (402), a tilt sensor C (403), a power conversion module C (404), and a lithium battery C (405); wherein the wireless data transmission module C (402) and the tilt sensor C (403) are respectively connected to the microprocessor C (401); the output end of the power conversion module C (404) is connected to the power end of the tilt sensor C (403) and the power end of the microprocessor C (401); and the input end is connected to the lithium battery C (405); the device C is fixed to the saddle position of the shovel loading robot arm through a device C mounting bracket; The device D (5) comprises a microprocessor D (501), a wireless data transmission module D (502), an encoder D (503), a power conversion module D (504), and a lithium battery D (505); wherein the wireless data transmission module D (502) and the encoder D (503) are respectively connected to the microprocessor D (501); the output end of the power conversion module D (504) is connected to the power end of the encoder D (503) and the power end of the microprocessor D (501); and the input end is connected to the lithium battery D (505); The device E (6) comprises a microprocessor E (601), a wireless data transmission module E (602), a Beidou positioning module E (603), a laser radar E (604), a power conversion module E (605), and a lithium battery E (606); wherein the wireless data transmission module E (602) and the laser radar E (604) are respectively connected to the microprocessor E (601); the output end of the power conversion module B (605) is connected to the power end of the laser radar E (604) and the power end of the microprocessor E (601); and the input end is connected to the lithium battery E (606); The base station (7) comprises a microprocessor F (701), a wireless data transmission module F (702), a Beidou positioning module F (703), a power conversion module F (704), and a power supply F (705); wherein the wireless data transmission module F (702) and the Beidou positioning module F (703) are respectively connected to the microprocessor F (701); the output end of the power conversion module F (704) is connected to the power supply end of the Beidou positioning module F (703) and the power supply end of the microprocessor F (701); and the input end is connected to the power supply (705).

6. The unmanned shovel loading robot autonomous unloading and mining truck full bucket rate detection device according to claim 1 is characterized in that: The Beidou positioning modules of the device A (2), the device B (3), and the device E (6) adopt a differential positioning algorithm, including: The base station (7) sends its carrier observation and location information to device A (2), device B (3), and device E (6); Device A (2), device B (3), and device E (6) receive the Beidou satellite carrier phase and the base station carrier phase, form phase difference observation values, and calculate the corrected coordinates in real time.

7. The unmanned shovel loading robot autonomous unloading and mining truck full bucket rate detection device according to claim 1 is characterized in that: The posture detection module A (204) and the posture detection module B (304) integrate an accelerometer and a gyroscope, and fuse the gyroscope angular velocity and the accelerometer gravity component data through the following method, including: In the shovel robot operation environment, the angle change of the gyroscope output of device A is , the angle measured by the accelerometer is , according to the requirements of the operating environment, set the complementary filter weighting coefficient , , the angles measured by the gyroscope and accelerometer are integrated to obtain the integrated angle , , is the angle of the last fusion of device A; The angle change of the gyroscope output of device B is , the angle measured by the accelerometer is , according to the requirements of the operating environment, set the complementary filter weighting coefficient , , the angles measured by the gyroscope and accelerometer are integrated to obtain the integrated angle , , is the angle of the last fusion of device B.

8. The unmanned shovel loading robot autonomous unloading and mining truck full bucket rate detection device according to claim 1 is characterized in that: Based on the laser radar E (604) data, a material distribution model in the mining truck is constructed and accurate positioning is achieved, and the full bucket rate of the mining truck material is identified in the working environment of the shovel robot, including the following steps: 1) LiDAR data processing: receiving LiDAR scan data, preprocessing the LiDAR data, and removing noise and outliers; 2) Scan matching: Match the lidar scan data with the constructed material distribution model in the mine truck to find the best pose estimation of the current scan in the material distribution model in the mine truck. The scan matching process minimizes the distance error between the current scan point and the material distribution model point in the mine truck by optimizing the objective function, thereby determining the position and pose of the lidar; 3) Sub-model construction: The material distribution model in the mine truck is divided into multiple sub-models. The position information is obtained according to the LiDAR scanning matching, and the LiDAR scanning data is integrated into the sub-model. The sub-model is represented by an occupancy grid model. Each grid cell represents a small area in the mine truck, and its value represents the probability of the area being occupied by an object. 4) Build a globally consistent material distribution model; 5) Calculation of the full load rate of the mining truck: According to the design specifications of the mining truck, determine the spatial shape and size of the mining truck when it is theoretically fully loaded, and build an ideal full load volume model with a volume of ; Using the sub-models represented by the constructed occupancy grid model, the volume of each sub-model is calculated; Since each grid cell represents a small area in the mine card, assuming that the volume of each grid cell is v, by traversing all the grid cells in the sub-model and counting the number of grid cells n whose probability of being occupied by objects is greater than the set threshold, the volume of the material in each sub-model is ; Add up the volumes of materials in all sub-models to get the total volume of materials in the current mine card , the calculation formula for the mine card full bucket rate is: ; By substituting the current material volume and the full load volume into the formula, the full bucket rate of the mining truck can be obtained.

9. The unmanned shovel loading robot autonomous unloading and mining truck full bucket rate detection device according to claim 8 is characterized in that: The step 2) comprises: First, initialize and set the data point set scanned by the laser radar as , the material distribution model point set in the mine card is , the position of the laser radar is Matrix, pose Contains the rotation matrix and the translation vector ; Then perform ICP algorithm iterative optimization on the above data to find the data points scanned by the lidar The material distribution model point in the mine card corresponding to the point closest to it point, and measure the distance between them ,according to Calculate the objective function , using singular value decomposition according to the objective function Update the LiDAR pose , the income That is, the optimal pose estimation of the lidar in the material distribution model inside the mine truck.

10. The unmanned shovel loading robot autonomous unloading and mining truck full bucket rate detection device according to claim 8, characterized in that: The step 4) comprises: Local point cloud collection: The shovel loading robot is equipped with a laser radar, which continuously collects point cloud data of materials in the mining truck during movement to obtain a local point cloud collection ; Create local models using adjacent local point clouds and Through the point cloud stitching algorithm, a local material distribution model is initially constructed, and the posture sensor of the shovel robot is used to calculate the posture ; Perform loop detection and extract simple features for each local model. When the shovel robot moves to a new position, a new local model is obtained. After that, its features are combined with the features of all previously saved local models Compare and further check the geometric similarity of the point clouds; Perform global model optimization, build optimized graphics, and transform the robot’s position and posture As nodes, the transformation relationships between postures are used as edges to build a graph; For the pose transformation based on motion sensor estimation, the error function It is defined by the difference between the estimated pose and the actual scan point cloud matching pose; for the pose relationship obtained by loop closure detection, the error function It is defined based on the matching error of the point cloud at the loop; the total error function is , using the gradient descent method, iteratively adjust the pose nodes , minimize the total error function E, thereby updating the pose and eliminating or reducing the cumulative error, thereby constructing a globally consistent material distribution model.

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