Excavator autonomous operation method based on laser radar assistance

Abstract

The invention provides an excavator autonomous operation method based on laser radar assistance, and the method comprises the steps: turning on a laser radar, connecting the laser radar to a Raspberry Pi 4B embedded system, and connecting the Raspberry Pi 4B embedded system to an excavator processor; the excavation working face is scanned through a laser radar, and scanned three-dimensional point cloud data is transmitted to the Raspberry Pi 4B embedded system; filtering the received point cloud data through a gradient filtering algorithm imported into the Raspberry Pi 4B embedded system, and outputting an excavation point coordinate to an excavator processor; and after the excavator processor receives the coordinates of the excavation point, the excavator bucket can act to the excavation point to complete the excavation action through track planning of the excavator bucket. Only the laser radar sensor is used for assisting the operation process of the excavator, the data size is small, the processing speed is high, and the laser radar scanning precision is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of excavators, and in particular to an autonomous operation method of an excavator based on the assistance of a laser radar. Background Art

[0002] Excavators are one of the most widely used engineering machinery equipment and play an important role in the field of infrastructure construction. Improving the degree of autonomy of excavators can not only reduce the workload of drivers and reduce the harm to personnel in dangerous environments, but also reduce the dependence of construction on skilled drivers and reduce labor costs. As a new type of sensor, laser radar measures the distance information between the radar and the target by detecting light pulses or modulated signals, and obtains azimuth information through scanning or multi-point corresponding measurement. It is an active detector that can obtain three-dimensional spatial information of the target. It can be used as a good auxiliary sensor in the automatic excavation work of crawler excavators.

[0003] At present, the most commonly used combination for sensor-assisted crawler excavator operations is the combination of camera sensors and lidar. For example, a combination of ordinary cameras, lidars and stereo cameras is used. Ordinary cameras are used to provide monitoring signals to wireless remote control operators and provide captured environmental information to operators under remote control conditions; lidars are used for digging plane detection, fitting and obstacle detection during automatic digging; stereo cameras are used for three-dimensional modeling of digging results during automatic digging for effect evaluation. In addition, there is also a combination of visual sensors, pressure sensors, and inclination sensors, which use visual, pressure and angle information to convert digging actions and autonomously handle different working environments encountered during digging.

[0004] However, whether using a combination of camera sensors and lidar sensors, or a combination of multiple sensors, data processing will be difficult. The more sensors used, the more data information is received, and the more difficult it is to process this information in parallel, which will increase the error. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention proposes an autonomous operation method of an excavator based on the assistance of laser radar. The present invention only uses a laser radar sensor to assist the operation process of the excavator. The data volume is small, the processing speed is fast, and the accuracy of the laser radar scanning is effectively improved.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A laser radar-assisted excavator autonomous operation method comprises the following steps:

[0008] Turn on the LiDAR, connect the LiDAR to the Raspberry Pi 4B embedded system, and connect the Raspberry Pi 4B embedded system to the excavator processor;

[0009] Scan the excavation work surface with LiDAR and transmit the scanned 3D point cloud data to the Raspberry Pi 4B embedded system;

[0010] The received point cloud data is filtered by the slope filtering algorithm imported into the Raspberry Pi 4B embedded system, and an excavation point coordinate is output to the excavator processor;

[0011] After the excavator processor receives the coordinates of the excavation point, it plans the trajectory of the excavator bucket so that it can move to the excavation point to complete the excavation action.

[0012] Preferably, in the steps of turning on the laser radar, connecting the laser radar to the Raspberry Pi 4B embedded system, and connecting the Raspberry Pi 4B embedded system to the excavator processor, the Raspberry Pi 4B embedded system and the excavator processor are installed in the cab of the excavator, and the scanning image of the laser radar is displayed through a display screen.

[0013] Preferably, the step of filtering the received point cloud data by a slope filtering algorithm imported into the Raspberry Pi 4B embedded system and outputting an excavation point coordinate to the excavator processor specifically includes: filtering the received point cloud data by a slope filtering algorithm imported into the Raspberry Pi 4B embedded system, filtering out non-ground points, retaining ground points, and outputting an excavation point coordinate according to the shape of the excavation target and transmitting it to the excavator processor. In the slope filtering algorithm, the point cloud is divided into grids of a certain size, each grid has a certain slope threshold, and the point elevation value of each grid is subtracted from the lowest elevation value in the grid, and then divided by its distance from the lowest point to obtain a slope value. When the slope value exceeds this threshold, it is identified as a non-ground point; if it is lower than the threshold, it is identified as a ground point.

[0014] Preferably, the step of filtering the received point cloud data by using a slope filtering algorithm imported into the Raspberry Pi 4B embedded system and outputting a digging point coordinate to the excavator processor specifically includes:

[0015] Set the grid size, set the slope threshold, and calculate the number of grids;

[0016] Cycle through the grids sequentially, find the lowest point of each grid, and then calculate the slope values ​​of other points in the grid according to the formula. Let the lowest point be min_p, and the calculation formula is:

[0017]

[0018] Each grid loops through each point, comparing the slope value with the slope threshold to distinguish between ground points and non-ground points;

[0019] The loop ends and the filtering result is output.

[0020] Preferably, after the excavator processor receives the coordinates of the excavation point, the excavator bucket is planned to move to the excavation point to complete the excavation action. The steps specifically include: after the excavator processor receives the coordinates of the excavation point, the excavator processor reversely solves the kinematic equations, reversely calculates the rotation angles of each joint and the extension and retraction of the hydraulic cylinder, so as to move the bucket to the excavation point and complete the single excavation operation process.

[0021] Preferably, the excavator bucket trajectory planning specifically includes the following steps:

[0022] Establish the DH coordinate system of the excavator working device;

[0023] Perform forward solution of kinematic equations;

[0024] Perform inverse solution of kinematic equations;

[0025] Convert joint rotation angle and hydraulic cylinder extension and retraction amount;

[0026] The rotation sequence of the joints changing over time is obtained by quintic polynomial interpolation.

[0027] Preferably, the step of establishing the DH coordinate system of the excavator working device specifically includes: establishing five coordinate systems with the rotation center, the hinge point of the boom and the base, the hinge point of the arm and the boom, the hinge point of the bucket and the arm, and the tip of the bucket tooth as coordinate origins.

[0028] Preferably, the step of forward solving the kinematic equations specifically includes: based on the established DH coordinates of the excavator working device, using the transformation matrix of the DH coordinates to analyze and solve the forward kinematics of the excavator working device, and analyzing the inverse kinematics of the working device through a geometric method to complete the conversion between the tooth tip coordinates of the excavator working device, the joint angle and the cylinder extension and retraction amount.

[0029] Preferably, the step of inversely solving the kinematic equations specifically includes: solving the rotation angles of each joint and the extension and retraction amount of the hydraulic cylinder through the posture coordinates of the bucket tooth tip, and completing the forward and inverse conversion of the bucket tooth tip coordinates and each joint rotation angle.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The present invention uses a laser radar sensor to assist the excavator operation process. The point cloud data scanned by the laser radar is processed by the Raspberry Pi 4B embedded system and transmitted to the excavator processor. Only the point cloud data of the laser radar is accepted throughout the process, with a small amount of data and a fast processing speed.

[0032] 2. The present invention writes the slope filtering algorithm into the Raspberry Pi 4B embedded system, which can filter the scanned point cloud data, effectively remove noise caused by environmental reasons, remove ground points, and retain non-ground points, effectively improving the accuracy of lidar scanning and providing more accurate scanning data for subsequent excavation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0034] Figure 1 It is a flowchart of the autonomous operation method of an excavator based on laser radar assistance of the present invention;

[0035] Figure 2 A side view of an excavator assisted by laser radar according to the present invention;

[0036] Figure 3 It is the geometric relationship diagram of the excavator;

[0037] Figure 4 This is the connection diagram of the boom structure;

[0038] Figure 5 This is the connection diagram of the arm structure;

[0039] Figure 6 This is the bucket structure connection diagram. DETAILED DESCRIPTION

[0040] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0041] Specifically, the present invention provides an autonomous operation method of an excavator based on laser radar assistance, such as Figure 1 As shown, the method comprises the following steps:

[0042] S1: Turn on the LiDAR, connect the LiDAR to the Raspberry Pi 4B embedded system, and connect the Raspberry Pi 4B embedded system to the excavator processor;

[0043] Specifically, Figure 2 As shown, the excavator is a crawler excavator, including a crawler base 1, a counterweight 2, a cab 3, a laser radar 4, a boom 5, a dipper cylinder 6, a dipper 7, a bucket cylinder 8, a connecting rod 9 and a bucket 10. The Raspberry Pi 4B embedded system and the excavator processor are installed in the cab 3 of the excavator, and the scanning picture of the laser radar is displayed through a display screen.

[0044] S2: Scan the excavation working surface through the laser radar and transmit the scanned 3D point cloud data to the Raspberry Pi 4B embedded system;

[0045] The Raspberry Pi 4B embedded system receives data transmitted by the laser radar, including data transmission and data processing, and the data transmission includes the following steps:

[0046] Step 1: Connect the Raspberry Pi 4B, switch the working directory to the specified folder through the "cd car_ws" command, and execute the program or perform other operations in the folder indicated by the command;

[0047] Step 2: Enter the command "source devel / setup.bash" to set some environment variables or execute some initialization scripts in the working environment. This file contains the environment variables and other configurations required to set up the ROS working environment;

[0048] Step 3: Enter the command "roslaunch lslidar_driver lslidar_c16.launch" to start the lidar software to acquire and process lidar data;

[0049] Step 4: Enter the command "rviz" to start the ROS visualization tool, which will display and interact with robot-related data in a three-dimensional environment.

[0050] The data processing comprises the following steps:

[0051] Step 1: Set up the Raspberry Pi environment;

[0052] Step 2: Import the algorithm code into the Raspberry Pi 4B embedded system;

[0053] Step 3: Compile the code;

[0054] Step 4: Run the compiled executable file to output the coordinate points.

[0055] S3: filtering the received point cloud data by using the slope filtering algorithm imported into the Raspberry Pi 4B embedded system, and outputting an excavation point coordinate to the excavator processor;

[0056] Specifically, the received point cloud data is filtered by the slope filtering algorithm imported into the Raspberry Pi 4B embedded system to filter out non-ground points and retain ground points. The coordinates of an excavation point are output according to the shape of the excavation target and transmitted to the excavator processor.

[0057] In the slope filtering algorithm, the point cloud is divided into grids of a certain size. Each grid has a certain slope threshold. The point elevation value of each grid is subtracted from the lowest elevation value in the grid, and then divided by the distance between it and the lowest point to get the slope value. When the slope value exceeds this threshold, it is identified as a non-ground point; if it is lower than the threshold, it is identified as a ground point. The specific steps are as follows:

[0058] Step 1: Set the grid size, set the slope threshold, and calculate the number of grids;

[0059] Step 2: Loop through the grids sequentially to find the lowest point in each grid, and then calculate the slope values ​​of other points in the grid according to formula (1). Let the lowest point be min_p, then formula (1) is as follows:

[0060]

[0061] Step 3: loop through each point in each grid, compare the slope value with the slope threshold, and distinguish between ground points and non-ground points;

[0062] Step 4: The loop ends and the filtering result is output.

[0063] S4: After receiving the coordinates of the excavation point, the excavator processor plans the trajectory of the excavator bucket so that it can move to the excavation point to complete the excavation action.

[0064] Specifically, after the excavator processor receives the coordinates of the digging point, it reversely solves the kinematic equations and calculates the rotation angles of each joint and the extension and retraction of the hydraulic cylinder to move the bucket to the digging point and complete the single excavation operation process.

[0065] Using Cartesian space trajectory planning, the bucket tooth tip coordinates are converted into joint angles through inverse kinematics transformation, and the Cartesian space planning is converted into joint space planning. Through the above analysis and solution of the forward and inverse kinematics of the working device, the relationship between the excavator bucket tooth tip coordinates and joint angles can be obtained, that is, the joint angle and telescopic amount can be obtained if the spatial posture coordinates are known. The joint angles of the boom, dipper and bucket are known, and the bucket tooth tip coordinates can be obtained through the conversion relationship, thereby realizing the path planning of the excavator bucket.

[0066] According to the different description variables selected, the working space of the excavator is usually divided into four spaces: posture, joint, drive and detection. The definitions of each space are as follows:

[0067] Posture space: refers to the information of the spatial coordinates [x, y, z] and the attitude angle ζ of the excavator bucket in the Cartesian coordinate system. Since the starting point, end point or way point of a single work task of the excavator is usually determined in the Cartesian coordinate system during trajectory planning, the posture space is often also expressed as the working space of the excavator working device.

[0068] Joint space: The joint space is usually composed of the angle variables of each joint of the excavator working device, represented by the vector [θ1, θ2, θ3, θ4] T ,θ1 is the rotation angle of the rotary platform, θ2 is the boom joint angle, θ3 is the arm joint angle, and θ4 is the bucket joint angle.

[0069] Drive space: The drive space represents the motion parameters of each drive actuator of the excavator. The space vector is expressed as [λ1,λ2,λ3,λ4] T , λ1 is the rotation angle of the slewing platform, λ2 is the displacement of the boom hydraulic cylinder, λ3 is the displacement of the arm hydraulic cylinder, and λ4 is the displacement of the bucket hydraulic cylinder.

[0070] Detection space: Detection space refers to the available area used to monitor and evaluate the target object or system in a specific operation or task. It usually includes spatial range, dynamic and static obstacles, safety distance, and real-time monitoring.

[0071] The excavator bucket trajectory planning specifically includes the following steps:

[0072] Step 1: Establish the DH coordinate system of the excavator working device;

[0073] In order to obtain the forward and inverse kinematic solutions of the excavator working device and the relationship between the joint angle and the extension of the hydraulic cylinder, the DH coordinates of the excavator working device are established according to the principles of robotics and homogeneous coordinate transformation. Five coordinate systems are established with the rotation center, the hinge point between the boom and the base, the hinge point between the arm and the boom, the hinge point between the bucket and the arm, and the tip of the bucket tooth as the coordinate origin.

[0074] like Figure 3 As shown, in order to complete the mutual conversion between coordinate systems, four parameters a are defined i ,d i ,α i ,θ i ,in

[0075] θ i Represents the Z i-1 Axis from X i-1 Axis rotates to match X i Angle when the axes are parallel;

[0076] a i Indicates that along X i The direction of the axis, from Z i-1 Axis to Zi The distance between the axes;

[0077] d i Indicates that along Z i Axis direction, from X i-1 Axis to X i The distance between the axes;

[0078] α i Indicates around X i Axis from Z i-1 Axis rotation to Z i The angle through which the axis is rotated.

[0079] Step 2: Perform forward solution of the kinematic equations;

[0080] Based on the DH coordinates of the excavator working device established above, the transformation matrix of the DH coordinates is used to analyze and solve the forward kinematics of the excavator working device, and the inverse kinematics of the working device is analyzed by the geometric method to complete the conversion between the tooth tip coordinates, joint angles and cylinder extension and contraction amounts of the excavator working device.

[0081] The position of the bucket tooth tip can be expressed as:

[0082]

[0083] Where: s i represents sinθ i ;s ij represents sin(θ i +θ j );s ijk represents sin(θ i +θ j +θ k ), similarly, c i represents cosθ i .

[0084] The direction angle of the bucket relative to the base coordinate system can be expressed as The transformation equation from the joint angle to the bucket tooth tip coordinates is:

[0085]

[0086] Step 3: Inversely solve the kinematic equations;

[0087] The inverse solution of the kinematic equations of the excavator working device is to know the position coordinates of the bucket tooth tip Solve the rotation angle of each joint and the extension and contraction of the hydraulic cylinder.

[0088] 1) Bucket tooth tip position coordinates and joint angle transformation

[0089] The bucket tooth tip coordinates are expressed as right Figure 2 Geometric relationship analysis

[0090]

[0091] The two-dimensional coordinates of the bucket tooth tip, i.e. point L, are (x, z). From the geometric relationship analysis, the coordinates of point J are:

[0092]

[0093] Depend on Figure 3 The geometric relationship analysis shows that the tooth tip coordinates are The angles of each joint are:

[0094]

[0095] in:

[0096]

[0097] Through the above analysis, we can get the conversion equation of bucket tooth tip coordinates to each joint angle. Then, according to the above forward kinematics analysis, we can complete the forward and inverse conversion of bucket tooth tip coordinates and each joint angle.

[0098] Step 4: Convert the joint angle and the extension and contraction amount of the hydraulic cylinder;

[0099] The excavator mainly achieves the excavation task by controlling the movement of the boom, dipper arm and bucket, that is, by controlling the extension and contraction of the hydraulic cylinder to change the change of its joint angle, so that the working device can dig according to the target trajectory. Therefore, in order to effectively control the movement of the excavator working device, it is necessary to find the relationship between the joint angle and the corresponding hydraulic cylinder extension and contraction.

[0100] Figure 4 It is the connection diagram of the boom structure, such as Figure 4 As shown in the figure, the relationship between the boom joint angle and the extension and contraction of the hydraulic cylinder is:

[0101] BC is the telescopic amount of the boom hydraulic cylinder, represented by λ2, ∠FAX1 is the boom joint rotation angle, represented by θ2, and the cosine theorem is used for ΔABC to obtain the relationship between the telescopic amount of the boom hydraulic cylinder and its joint rotation angle:

[0102]

[0103] Figure 5 This is the connection diagram of the arm structure, such as Figure 5 As shown in the figure, the relationship between the arm joint angle and the extension and contraction of the hydraulic cylinder is:

[0104] DE is the expansion and contraction amount of the boom hydraulic cylinder, λ3, ∠JFX2 is the boom joint angle, θ3, and the cosine theorem is used for ΔDFE to obtain the relationship between the expansion and contraction amount of the boom hydraulic cylinder and its joint angle:

[0105]

[0106] Figure 6 This is the bucket structure connection diagram, such as Figure 6 As shown in the figure, the relationship between the bucket joint angle and the extension and contraction of the hydraulic cylinder is:

[0107]

[0108] Then the bucket joint angle θ4 is derived

[0109] θ4=π-∠IJF-∠LJK-∠HJI-∠HJK (11)

[0110] Step 5: Use quintic polynomial interpolation to obtain the rotation angle sequence of the joint changing over time.

[0111] By analyzing and solving the forward and inverse kinematics of the working device, the mutual conversion relationship between the excavator bucket tooth tip coordinates, joint angles, and hydraulic cylinder extension and contraction amount is obtained. That is, the joint angles and extension amount can be obtained when the spatial posture coordinates are known. Similarly, when the joint angles of the boom, dipper and bucket are known, the bucket tooth tip coordinates can be obtained through the conversion relationship. Trajectory planning for the excavator also requires determining the angle sequence of each joint that changes over time.

[0112] The motion process of the excavator working device can be represented by a time interpolation function describing the joint angle, and the starting time and the end time of the trajectory motion are represented as t0 and t f , the starting joint angle and the end point joint angle are represented as θ0 and θ f .

[0113] The expression of the quintic polynomial trajectory interpolation function is defined as follows:

[0114] θ(t)=a0+a1t+a2t 2 +a3t 3 +a4t 4 +a5t 5 (12)

[0115] Substituting the coefficients into the above formula, we can get the function expression of the quintic polynomial interpolation as follows:

[0116]

[0117] The angular velocity and angular acceleration of the corresponding joint can be derived by differentiation:

[0118]

[0119] The joint angle, angular velocity and angular acceleration obtained by quintic polynomial interpolation fitting have good stability and meet the kinematic requirements.

[0120] In summary, the present invention uses a laser radar sensor to assist the excavator operation process, and transmits the laser radar scanning point cloud data to the excavator processor through the Raspberry Pi 4B embedded system. The laser radar point cloud data is only accepted throughout the process, and the data volume is small and the processing speed is fast. In addition, the present invention writes the slope filtering algorithm into the Raspberry Pi 4B embedded system, and the scanned point cloud data can be filtered and processed, effectively removing the noise caused by environmental reasons, effectively improving the accuracy of the laser radar scanning, and providing more accurate scanning data for subsequent excavation work.

[0121] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A laser radar-assisted excavator autonomous operation method, characterized in that: The method comprises the following steps: Turn on the LiDAR, connect the LiDAR to the Raspberry Pi 4B embedded system, and connect the Raspberry Pi 4B embedded system to the excavator processor; Scan the excavation work surface with LiDAR and transmit the scanned 3D point cloud data to the Raspberry Pi 4B embedded system; The received point cloud data is filtered by the slope filtering algorithm imported into the Raspberry Pi 4B embedded system, and an excavation point coordinate is output to the excavator processor; After the excavator processor receives the coordinates of the excavation point, it plans the trajectory of the excavator bucket so that it can move to the excavation point to complete the excavation action.

2. The laser radar-assisted excavator autonomous operation method according to claim 1 is characterized in that: In the steps of turning on the laser radar, connecting the laser radar to the Raspberry Pi 4B embedded system, and connecting the Raspberry Pi 4B embedded system to the excavator processor, the Raspberry Pi 4B embedded system and the excavator processor are installed in the cab of the excavator, and the scanning image of the laser radar is displayed through a display screen.

3. The laser radar-assisted excavator autonomous operation method according to claim 1 is characterized in that: The step of filtering the received point cloud data by the slope filtering algorithm imported into the Raspberry Pi 4B embedded system and outputting an excavation point coordinate to the excavator processor specifically includes: filtering the received point cloud data by the slope filtering algorithm imported into the Raspberry Pi 4B embedded system, filtering out non-ground points, retaining ground points, and outputting an excavation point coordinate according to the shape of the excavation target and transmitting it to the excavator processor. In the slope filtering algorithm, the point cloud is divided into grids of a certain size, each grid has a certain slope threshold, and the point elevation value of each grid is subtracted from the lowest elevation value in the grid, and then divided by the distance between it and the lowest point to obtain the slope value. When the slope value exceeds this threshold, it is identified as a non-ground point; if it is lower than the threshold, it is identified as a ground point.

4. The laser radar-assisted excavator autonomous operation method according to claim 3 is characterized in that: The step of filtering the received point cloud data by using the slope filtering algorithm imported into the Raspberry Pi 4B embedded system and outputting an excavation point coordinate to the excavator processor specifically includes: Set the grid size, set the slope threshold, and calculate the number of grids; Cycle through the grids sequentially, find the lowest point of each grid, and then calculate the slope values ​​of other points in the grid according to the formula. Let the lowest point be min_p, and the calculation formula is: Each grid loops through each point, comparing the slope value with the slope threshold to distinguish between ground points and non-ground points; The loop ends and the filtering result is output.

5. The laser radar-assisted excavator autonomous operation method according to claim 1, characterized in that: After the excavator processor receives the coordinates of the excavation point, it plans the trajectory of the excavator bucket to enable it to move to the excavation point to complete the excavation action. The steps specifically include: after the excavator processor receives the coordinates of the excavation point, it reversely solves the kinematic equations, reversely calculates the rotation angles of each joint and the extension and contraction amount of the hydraulic cylinder, so as to move the bucket to the excavation point and complete the single excavation operation process.

6. The laser radar-assisted excavator autonomous operation method according to claim 1, characterized in that: The excavator bucket trajectory planning specifically includes the following steps: Establish the DH coordinate system of the excavator working device; Perform forward solution of kinematic equations; Perform inverse solution of kinematic equations; Convert joint rotation angle and hydraulic cylinder extension and retraction amount; The rotation sequence of the joints changing over time is obtained by quintic polynomial interpolation.

7. The laser radar-assisted excavator autonomous operation method according to claim 6 is characterized in that: The steps of establishing the DH coordinate system of the excavator working device specifically include: establishing five coordinate systems with the rotation center, the hinge point between the boom and the base, the hinge point between the arm and the boom, the hinge point between the bucket and the arm, and the tip of the bucket tooth as the coordinate origins.

8. The laser radar-assisted excavator autonomous operation method according to claim 6 is characterized in that: The steps of forward solving the kinematic equations specifically include: based on the established DH coordinates of the excavator working device, using the transformation matrix of the DH coordinates to analyze and solve the forward kinematics of the excavator working device, and analyzing the inverse kinematics of the working device through a geometric method to complete the conversion between the tooth tip coordinates of the excavator working device, the joint angle and the cylinder extension and retraction amount.

9. The laser radar-assisted excavator autonomous operation method according to claim 6, characterized in that: The step of inversely solving the kinematic equations specifically includes: solving the rotation angles of each joint and the extension and contraction amount of the hydraulic cylinder through the position coordinates of the bucket tooth tip, and completing the forward and inverse conversion of the bucket tooth tip coordinates and the rotation angles of each joint.

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