Control method of robot and robot

Abstract

The application discloses a robot control method and robot, the robot includes laser radar and RTK positioning module, the control method includes: after receiving the target path, the point cloud data is obtained through the laser radar, and the RTK signal is received through the RTK positioning module;When the signal strength of the RTK signal is in the preset threshold range, and the number of feature points of the point cloud data is greater than or equal to the preset value, the first pose information is obtained according to the point cloud data, and the second pose information is obtained according to the RTK signal;The first pose information and the second pose information are fused to obtain target pose information;The robot is controlled to walk along the target path according to the target pose information.The application can improve the accuracy of robot positioning, avoid the situation that the robot may deviate from the target path, and make the robot walk and work along the target path with high precision.

Description

Technical Field

[0001] This application relates to the field of robot control technology, specifically to a robot control method and a robot. Background Technology

[0002] Currently, in existing robots, RTK positioning is generally used to control the robot to walk along the target path. That is, the real-time pose is determined by receiving RTK signals from the RTK base station, so as to control the robot to walk to the target path and to walk and work along the target path.

[0003] However, in some less open areas, such as when there are large trees or tall buildings blocking the view, the RTK signal strength is relatively low. The robot cannot accurately locate itself based on the RTK signal, which may cause the robot to deviate from the target path. Ultimately, this will prevent the robot from walking and working with high precision along the target path. Summary of the Invention

[0004] Therefore, this application provides a robot control method and a robot to improve the accuracy of robot positioning, prevent the robot from deviating from the target path, and enable the robot to perform high-precision walking and operations along the target path. The technical solution of this application is as follows: The first aspect of this application provides a robot control method, the robot including a lidar and an RTK positioning module, the control method comprising: after receiving a target path, acquiring point cloud data through the lidar and receiving an RTK signal through the RTK positioning module; determining that the signal strength of the RTK signal is within a preset threshold range and the number of feature points in the point cloud data is greater than or equal to a preset value, acquiring first pose information based on the point cloud data and acquiring second pose information based on the RTK signal; fusing the first pose information and the second pose information to obtain target pose information; and controlling the robot to walk along the target path based on the target pose information.

[0005] In one embodiment of this application, the control method further includes: determining that when the signal strength of the RTK signal is less than the preset threshold range and the number of feature points of the point cloud data is greater than or equal to a preset value, obtaining the first pose information based on the lidar; and using the first pose information as the target pose information.

[0006] In one embodiment of this application, the control method further includes: when the signal strength of the RTK signal is greater than the preset threshold range, obtaining the second pose information based on the RTK signal; and using the second pose information as the target pose information.

[0007] In one embodiment of this application, the control method further includes: when the signal strength of the RTK signal is less than the preset threshold range and the number of feature points of the point cloud data is less than a preset value, controlling the robot to stop walking and reporting positioning failure information to the remote control terminal.

[0008] In one embodiment of this application, fusing the first pose information and the second pose information to obtain target pose information includes: performing RTK coordinate axis transformation on the first pose information to obtain reference pose information; and correcting the second pose information according to the reference pose information to obtain the target pose information.

[0009] In one embodiment of this application, the control method further includes: detecting obstacles in front of the robot in real time based on the point cloud data; generating an obstacle avoidance path based on the point cloud data of the obstacle when the obstacle is detected; and updating the target path based on the obstacle avoidance path.

[0010] A second aspect of this application provides a robot, including a controller, a lidar, and an RTK positioning module. The controller is connected to the lidar and the RTK positioning module. The lidar is used to acquire point cloud data. The RTK positioning module is used to receive RTK signals. The controller is used to: acquire point cloud data through the lidar and receive RTK signals through the RTK positioning module after receiving a target path; determine that the signal strength of the RTK signal is within a preset threshold range and the number of feature points in the point cloud data is greater than or equal to a preset value; acquire second pose information based on the RTK signal and acquire second pose information based on the point cloud data; fuse the first pose information and the second pose information to obtain target pose information; and control the robot to walk along the target path based on the target pose information.

[0011] In one embodiment of this application, the controller is further configured to: determine that when the signal strength of the RTK signal is less than the preset threshold range and the number of feature points of the point cloud data is greater than or equal to a preset value, obtain the first pose information based on the lidar; and use the first pose information as the target pose information.

[0012] In one embodiment of this application, the controller is further configured to: when the signal strength of the RTK signal is greater than the preset threshold range, obtain the second pose information based on the RTK signal; and use the second pose information as the target pose information.

[0013] In one embodiment of this application, the robot further includes a work module, which is detachably mounted on the robot and connected to the controller when mounted on the robot; the controller is also used to control the work module to work when controlling the robot to walk along the target path; the work module includes a line marking module, a weeding module, a manure pushing module, a spraying module, and a roller brush module.

[0014] After receiving the target path, the robot of this application determines that the RTK signal is within a preset threshold range and the number of feature points in the point cloud data is greater than or equal to a preset value. It then uses the point cloud data from the LiDAR to obtain the first pose information and the RTK signal received by the RTK point module to obtain the second pose information. By fusing the first pose information and the second pose information, the target pose information is obtained. The robot is then controlled to walk and work along the target path using the target pose information. This can improve the accuracy of robot positioning, prevent the robot from deviating from the target path, and enable the robot to walk and work with high precision along the target path. Attached Figure Description

[0015] Figure 1 This is a schematic block diagram of a robot provided in an embodiment of this application.

[0016] Figure 2 This is a flowchart illustrating a robot control method provided in an embodiment of this application.

[0017] Figure 3 This is a flowchart illustrating the second robot control method provided in the embodiments of this application.

[0018] Figure 4 This is a flowchart illustrating the third robot control method provided in the embodiments of this application.

[0019] Figure 5 This is a flowchart illustrating the fourth robot control method provided in the embodiments of this application.

[0020] Figure 6 This is a flowchart illustrating a pose information fusion method provided in an embodiment of this application.

[0021] Figure 7 This is a flowchart illustrating a robot control method provided in an embodiment of this application.

[0022] Figure 8 This is a schematic block diagram of the second type of robot provided in the embodiments of this application.

[0023] Figure 9 This is a schematic block diagram of the third type of robot provided in the embodiments of this application. Detailed Implementation

[0024] It should be noted that in the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone, where A and B can be singular or plural. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and drawings of this application are used to distinguish similar objects, not to describe a specific order or sequence.

[0025] It should also be noted that the methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of the claims, the execution order of multiple steps can be interchanged, and some steps can also be deleted.

[0026] Currently, in existing robots, RTK positioning is generally used to control the robot to walk along the target path. That is, the real-time pose is determined by receiving RTK signals from the RTK base station, so as to control the robot to walk to the target path and to walk and work along the target path.

[0027] However, in some less open areas, such as when there are large trees or tall buildings blocking the view, the RTK signal strength is relatively low. The robot cannot accurately locate itself based on the RTK signal, which may cause the robot to deviate from the target path. Ultimately, this will prevent the robot from walking and working with high precision along the target path.

[0028] This application provides a robot control method and a robot to improve the accuracy of robot positioning, prevent the robot from deviating from the target path, and enable the robot to walk and work with high precision along the target path.

[0029] Please refer to Figure 1 , Figure 1 This is a schematic block diagram of a robot provided in an embodiment of this application, wherein the robot 100 includes a lidar 110 and an RTK positioning module 120.

[0030] In this embodiment, the lidar 110 is used to acquire point cloud data, and the RTK positioning module 120 is used to receive RTK signals. The robot 100 also includes a controller connected to the lidar 110 and the RTK positioning module 120. The controller controls the opening and closing of the lidar 110 and the RTK positioning module 120, and receives the point cloud data from the lidar 110 and the pose information from the RTK positioning module 120. The controller can also draw a real-time point cloud map based on the point cloud data to analyze the terrain along the robot 100's walking path. The pose information includes the robot 100's geographical location and posture information.

[0031] In some embodiments, before operation, the user can manually remotely control the robot 100 to have its onboard LiDAR 110 scan the working environment and acquire continuous point cloud frames. The controller uses a laser SLAM algorithm (such as LIO-SAM, FAST-LIO, etc.) to process the continuous LiDAR 110 point cloud frames, construct a high-precision three-dimensional point cloud map, and save it as a map file.

[0032] Next, combined Figure 1 This application introduces a robot control method based on an embodiment. Please refer to... Figure 2 , Figure 2 This application provides a flowchart illustrating a robot control method, which specifically includes the following steps: Step S21: After receiving the target path, acquire point cloud data through lidar and receive RTK signals through the RTK positioning module.

[0033] In this embodiment of the application, the target path is the path that the robot needs to track and move along. After receiving the target path, the robot's controller can activate the lidar and RTK positioning module, and control the lidar to scan the surrounding environment to obtain point cloud data of the surrounding environment, and control the RTK positioning module to receive the RTK signal sent by the RTK base station.

[0034] Step S22: When the signal strength of the RTK signal is within a preset threshold range and the number of feature points in the point cloud data is greater than or equal to a preset value, obtain the first pose information based on the point cloud data and the second pose information based on the RTK signal.

[0035] It is understandable that the positioning accuracy of the RTK positioning module depends on the strength of the RTK signal. Higher RTK signal strength allows for more precise robot positioning. The aforementioned preset threshold range is the range within which RTK positioning can be performed; that is, RTK positioning can be achieved using RTK signal strength within the preset threshold range. Furthermore, point cloud data from the LiDAR can be used to further improve the positioning accuracy. This preset threshold range can be set by the robot user. For example, a setting port can be provided on the robot's remote terminal device, allowing the user to set the upper and lower limits of the preset threshold range.

[0036] In this embodiment, the controller determines that the strength of the RTK signal is within a preset threshold range and the number of feature points in the point cloud data is greater than or equal to a preset value. Then, it obtains the first pose information based on the point cloud data and the second pose information based on the RTK signal. The aforementioned feature points can be points in the point cloud data with a height greater than a preset height value. This ensures that the robot's surrounding environment is non-planar. When the number of feature points is greater than or equal to the preset value, it avoids the accumulation of errors and drift in the point cloud data, ensuring high accuracy of the first pose information.

[0037] Step S23: Fuse the first pose information and the second pose information to obtain the target pose information.

[0038] In this embodiment, after obtaining the first pose information and the second pose information, the controller can fuse the first pose information and the second pose information to obtain the target pose information. For example, the controller can use the first pose information to correct the second pose information to obtain the target pose information, or the controller can use the second pose information to correct the first pose information to obtain the target pose information. In some embodiments, when the number of feature points is greater than or equal to a second preset value, the controller uses the first pose information as the primary pose information and uses the second pose information to correct the first pose information. When the number of feature points is between the above-mentioned preset value and the second preset value, the controller uses the second pose information as the primary pose information and uses the first pose information to correct the second pose information. The second preset value is greater than the above-mentioned preset value.

[0039] It is understandable that the above fusion process includes aligning the positioning coordinates of both the RTK positioning module and the LiDAR when both can perform positioning normally, in order to obtain more accurate positioning coordinates.

[0040] Step S24: Control the robot to walk along the target path according to the target pose information.

[0041] In this embodiment, the target pose information includes the robot's current geographical location information and posture information. The controller controls the robot to walk along the target path based on the current geographical location information, posture information, and the received target path, so as to control the robot to perform operations on the target path.

[0042] It is understood that after receiving the target path, the robot of this application determines that the RTK signal is within a preset threshold range and the number of feature points in the point cloud data is greater than or equal to a preset value. It then uses the point cloud data of the lidar to obtain the first pose information and the RTK signal received by the RTK point module to obtain the second pose information. The first pose information and the second pose information are fused to obtain the target pose information. The robot is then controlled to walk and work along the target path using the target pose information. This can improve the accuracy of robot positioning, avoid the robot from deviating from the target path, and enable the robot to walk and work with high precision along the target path.

[0043] In some embodiments, such as Figure 3 As shown, the control method for the robot described above also includes; Step S25: When the signal strength of the RTK signal is less than a preset threshold range and the number of feature points in the point cloud data is greater than or equal to a preset value, the first pose information is obtained based on the lidar.

[0044] Step S26: Use the first pose information as the target pose information.

[0045] In this embodiment of the application, when the controller determines that the signal strength of the RTK signal is less than a preset threshold range, that is, when the current RTK positioning accuracy is low and cannot meet the normal RTK positioning requirements, and when the controller determines that the number of feature points of the power data is greater than or equal to a preset value, it can use only point cloud data for positioning.

[0046] In some embodiments, when the robot travels to an area with high RTK signal strength, that is, when the RTK signal strength recovers to a preset threshold range, the controller can switch to a mode that uses first attitude information and second attitude information for fusion positioning, and uses second attitude information for positioning correction, to eliminate the error generated when only the first attitude information is used for target path tracking.

[0047] In some embodiments, such as Figure 4 As shown, the control method for the robot also includes: Step S27: When the signal strength of the RTK signal is determined to be greater than the preset threshold range, the second pose information is obtained based on the RTK signal.

[0048] Step S28: Use the second pose information as the target pose information.

[0049] In this embodiment, when the controller determines that the signal strength of the RTK signal is greater than a preset threshold range, it determines that the current RTK positioning accuracy is high and positioning can be performed using only the RTK signal. During the period when only the second pose information is used as the target pose information, the controller can also control the LiDAR to be turned off to save the robot's power, extend the robot's working time, and increase the working range.

[0050] In some embodiments, when the robot travels to an area with low RTK signal strength, i.e. when the RTK signal strength recovers to a preset threshold range, the controller can switch to a mode that uses first attitude information and second attitude information for fusion positioning in order to maintain positioning accuracy.

[0051] In some embodiments, such as Figure 5 As shown, the control method for the robot also includes: Step S29: When the signal strength of the RTK signal is less than the preset threshold range and the number of feature points in the point cloud data is less than the preset value, control the robot to stop walking and report the positioning failure information to the remote control terminal.

[0052] In this embodiment, when the controller determines that the signal strength of the RTK signal is less than a preset threshold range and the number of feature points in the point cloud data is less than a preset value, it determines that the robot is currently in an open area with a very weak RTK signal, making it impossible to achieve accurate positioning and accurate tracking of the target path. The controller can then control the robot to stop walking and report the positioning failure information to the remote control terminal, so that the user can adjust the position of the RTK base station in a timely manner and restore the signal strength of the RTK signal to the preset threshold range.

[0053] Please refer to Figure 6 , Figure 6 A flowchart illustrating a pose information fusion method provided in this application embodiment specifically includes the following steps: Step S61: Perform RTK coordinate axis transformation on the first pose information to obtain the reference pose information.

[0054] In this embodiment of the application, in the mode of fusing the first pose information and the second pose information for positioning, after the controller obtains the first pose information of the point cloud data, it can first perform RTK coordinate axis transformation on the first pose information to obtain the reference pose information.

[0055] The controller can be configured with a conversion algorithm for transforming the LiDAR coordinate axis to the RTK coordinate axis, as well as calibration information between the LiDAR and the RTK positioning module. The controller performs RTK coordinate axis transformation on the first pose information based on the conversion algorithm and calibration information.

[0056] Step S62: Correct the second pose information based on the reference pose information to obtain the target pose information.

[0057] In this embodiment of the application, after obtaining the reference pose information, the controller uses the reference pose information to correct the second pose information in order to obtain high-precision target pose information.

[0058] Please refer to Figure 7 , Figure 7 This is a flowchart illustrating a robot control method provided in an embodiment of this application, wherein... Figure 6 The control method shown can be performed in parallel with the control method of any of the above embodiments, that is, while the robot is executing the steps of any of the above embodiments, it can also execute the specific steps in the embodiments of this application: Step S71: Detect obstacles in front of the robot in real time based on point cloud data.

[0059] In this embodiment, while acquiring the first pose information using LiDAR, the controller also uses the LiDAR's point cloud data for real-time obstacle detection. For example, the controller can filter target feature points with heights greater than a third preset value from the point cloud data, and determine that the target feature points are on the target path, thus confirming the presence of an obstacle in front of the robot.

[0060] Further filtering is used to select target feature points on the target path as point cloud data of obstacles.

[0061] Step S72: When an obstacle is detected, an obstacle avoidance path is generated based on the point cloud data of the obstacle.

[0062] Step S73: Update the target path based on the obstacle avoidance path.

[0063] In this embodiment, the controller can further filter out target feature points on the target path as point cloud data of obstacles, so as to generate an obstacle avoidance path based on the point cloud data of obstacles.

[0064] In some embodiments, the controller can generate the obstacle avoidance path described above based on the point cloud data of the obstacles using local path planning algorithms such as dynamic windowing or model predictive control. If an obstacle avoidance path cannot be generated, the controller can also control the robot to stop moving, pause the operation, and report relevant information to the remote control terminal.

[0065] Please refer to Figure 8 , Figure 8 This is a schematic block diagram of a second type of robot provided in an embodiment of this application. The robot 100 includes a lidar 110, an RTK positioning module 120, and a controller 130.

[0066] In this embodiment, the controller 130 is connected to the lidar 110 and the RTK positioning module 120. The lidar 110 is used to acquire point cloud data. The RTK positioning module 120 is used to receive RTK signals. The controller 130 is used to: after receiving a target path, acquire point cloud data through the lidar 110 and receive RTK signals through the RTK positioning module 120; determine that the signal strength of the RTK signal is within a preset threshold range and the number of feature points in the point cloud data is greater than or equal to a preset value; acquire second pose information based on the RTK signal and the point cloud data; fuse the first pose information and the second pose information to obtain target pose information; and control the robot 100 to walk along the target path based on the target pose information.

[0067] In some embodiments, the controller 130 is further configured to: determine that when the signal strength of the RTK signal is less than a preset threshold range and the number of feature points of the point cloud data is greater than or equal to a preset value, obtain the first pose information according to the lidar 110 and use the first pose information as the target pose information.

[0068] In some embodiments, the controller 130 is further configured to: when it is determined that the signal strength of the RTK signal is greater than a preset threshold range, obtain second pose information based on the RTK signal; and use the second pose information as target pose information.

[0069] In some embodiments, the controller 130 is further configured to: calculate the control quantities such as speed and rotation angle that need to be executed by the wheels of the robot 100 based on the target pose information and the target path, and send them to the underlying driver so as to accurately drive the robot 100 to walk along the target path.

[0070] In some embodiments, such as Figure 9 As shown, the robot 100 also includes a work module 140, which is detachably mounted on the robot 100 and connected to the controller 130 when mounted on the robot 100.

[0071] In this embodiment, the controller 130 is also used to control the operation module 140 to work when the robot 100 walks along the target path. The operation module 140 includes a line marking module, a weeding module, a manure pushing module, a spraying module, and a roller brush module.

[0072] This application also provides a computer storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the control method described above.

[0073] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer storage medium or transmitted through the computer storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital versatile discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0074] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and its implementation can be combined arbitrarily.

[0075] The embodiments described above are merely preferred embodiments of this application and are not intended to limit the scope of this application. Any modifications and improvements made by those skilled in the art to the technical solutions of this application without departing from the spirit of this application should fall within the protection scope defined by the claims of this application.

Claims

1. A method for controlling a robot, characterized in that, The robot includes a lidar and an RTK positioning module, and the control method includes: After receiving the target path, point cloud data is acquired through the lidar and RTK signals are received through the RTK positioning module; When the signal strength of the RTK signal is determined to be within a preset threshold range and the number of feature points of the point cloud data is greater than or equal to a preset value, the first pose information is obtained based on the point cloud data, and the second pose information is obtained based on the RTK signal. By fusing the first pose information and the second pose information, the target pose information is obtained; The robot is controlled to walk along the target path based on the target pose information.

2. The control method as described in claim 1, characterized in that, Also includes: When the signal strength of the RTK signal is determined to be less than the preset threshold range, and the number of feature points of the point cloud data is greater than or equal to a preset value, the first pose information is obtained based on the lidar. The first pose information is used as the target pose information.

3. The control method as described in claim 1, characterized in that, Also includes: When the signal strength of the RTK signal is determined to be greater than the preset threshold range, the second pose information is obtained based on the RTK signal; The second pose information is used as the target pose information.

4. The control method as described in claim 1, characterized in that, Also includes: When the signal strength of the RTK signal is determined to be less than the preset threshold range and the number of feature points in the point cloud data is less than the preset value, the robot is controlled to stop walking and a positioning failure message is reported to the remote control terminal.

5. The control method as described in claim 1, characterized in that, The process of fusing the first pose information and the second pose information to obtain the target pose information includes: RTK coordinate transformation is performed on the first pose information to obtain the reference pose information; The second pose information is corrected based on the reference pose information to obtain the target pose information.

6. The control method according to any one of claims 1 to 5, characterized in that, Also includes: The robot detects obstacles in front of it in real time based on the point cloud data. When the obstacle is detected, an obstacle avoidance path is generated based on the point cloud data of the obstacle; Update the target path based on the obstacle avoidance path.

7. A robot, characterized in that, It includes a controller, a lidar, and an RTK positioning module, wherein the controller is connected to the lidar and the RTK positioning module; The lidar is used to acquire point cloud data; The RTK positioning module is used to receive RTK signals; The controller is used for: After receiving the target path, point cloud data is acquired through the lidar and RTK signals are received through the RTK positioning module; When the signal strength of the RTK signal is determined to be within a preset threshold range and the number of feature points of the point cloud data is greater than or equal to a preset value, the first pose information is obtained based on the point cloud data, and the second pose information is obtained based on the RTK signal. By fusing the first pose information and the second pose information, the target pose information is obtained; The robot is controlled to walk along the target path based on the target pose information.

8. The robot as described in claim 7, characterized in that, The controller is also used for: When the signal strength of the RTK signal is determined to be less than the preset threshold range, and the number of feature points of the point cloud data is greater than or equal to a preset value, the first pose information is obtained based on the lidar. The first pose information is used as the target pose information.

9. The robot as described in claim 7, characterized in that, The controller is also used for: When the signal strength of the RTK signal is determined to be greater than the preset threshold range, the second pose information is obtained based on the RTK signal; The second pose information is used as the target pose information.

10. The robot as claimed in any one of claims 7 to 9, characterized in that, It also includes a work module, which is detachably mounted to the robot and connected to the controller when mounted to the robot; The controller is also used to control the operation module to work when controlling the robot to walk along the target path; The operation modules include a line marking module, a weeding module, a manure pushing module, a spraying module, and a roller brush module.

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