Data processing method and device for robot walking control

Abstract

The invention discloses a data processing method and device for robot walking control. The method comprises the following steps: acquiring to-be-processed walking data; plane feature extraction processing based on point cloud clustering is carried out on the to-be-processed walking data, plane feature data are obtained, and the plane feature data comprise plane slope feature data and plane obstacle feature data in the robot walking environment; running planning processing based on a walking control model is carried out on the plane slope feature data and the plane obstacle feature data, target walking control data are obtained, and the target walking control data comprise walking path data and walking posture control data. By recognizing the plane gradient and the obstacle data in the walking environment of the robot, path planning and robot operation attitude control are carried out according to the gradient and the obstacle data, obstacle detection is carried out in the operation environment with the gradient, and the effect of improving the slope operation control efficiency of the robot is achieved.

Description

Technical Field

[0001] The present application relates to the field of robots, and in particular, to a data processing method and device for robot walking control. Background Art

[0002] With the continuous development of robot technology, the application of robots in various scenarios has become increasingly widespread. When the robot is running, it detects the ground, identifies obstacles on the ground during operation, and plans the robot's operation path based on the obstacles.

[0003] In the prior art, obstacle zone identification on the ground mainly detects whether there are obstacles on the ground. For the detection of slopes with inclined angles, it is difficult for the robot to effectively detect the slopes and obstacles, making it difficult for the robot to effectively plan its operation on the slopes.

[0004] Therefore, this application is filed to address the above problems. Summary of the invention

[0005] The main purpose of this application is to provide a data processing method and device for robot walking control to solve the above-mentioned problems pointed out in the prior art.

[0006] In order to achieve the above-mentioned purpose, in a first aspect of the present application, a data processing method for robot walking control is proposed, which is characterized by comprising:

[0007] Acquire walking data to be processed, wherein the walking data to be processed is environmental data collected for representing the walking of the robot;

[0008] Performing plane feature extraction processing based on point cloud clustering on the walking data to be processed to obtain plane feature data, wherein the plane feature data includes plane slope feature data and plane obstacle feature data in the robot walking environment;

[0009] The plane slope characteristic data and the plane obstacle characteristic data are processed by operation planning based on a walking control model to obtain target walking control data, wherein the target walking control data includes walking path data and walking posture control data.

[0010] Furthermore, the walking data to be processed is subjected to plane feature extraction processing based on point cloud clustering, and the plane feature data obtained includes:

[0011] Performing point cloud data recognition processing on the walking data to be processed to obtain point cloud data to be processed, wherein the point cloud data to be processed is point cloud data used to represent the walking environment of the robot;

[0012] Performing point cloud clustering processing on the point cloud data to be processed to obtain point cloud clustering data;

[0013] The point cloud clustering data is subjected to point cloud plane fitting processing to obtain the plane feature data, wherein the plane feature data is feature data used to indicate that it is a point cloud plane obtained by fitting.

[0014] Furthermore, performing point cloud plane fitting processing on the point cloud clustering data to obtain the plane feature data includes:

[0015] Performing point cloud plane fitting processing on the point cloud clustering data to obtain fitting plane data;

[0016] Performing a first plane feature generation process based on point cloud filtering on the fitting plane data to obtain first plane feature data, wherein the first plane feature data is feature data for representing obstacles on the fitting plane;

[0017] Performing a first plane feature generation process based on plane slope calculation on the fitting plane data to obtain second plane feature data, wherein the second plane feature data is feature data for representing the slope of the fitting plane;

[0018] The plane feature data is obtained according to the first plane feature data and the second plane feature data.

[0019] Furthermore, the fitting plane data is subjected to a first plane feature generation process based on point cloud filtering, and the first plane feature data obtained includes:

[0020] Performing plane feature vector generation processing on the fitting plane data to obtain plane feature vector data, wherein the plane feature vector data is data used to represent a point cloud feature vector on the fitting plane;

[0021] Performing a first point cloud filtering process on the point cloud clustering data according to the plane feature vector data to obtain process obstacle point cloud data, wherein the process obstacle point cloud data is used to represent point cloud data in the point cloud clustering data that is not filtered out by the plane feature vector;

[0022] The process obstacle point cloud data is subjected to a second point cloud filtering process based on a preset slope threshold to obtain the first plane feature data.

[0023] Furthermore, the plane slope characteristic data and the plane obstacle characteristic data are processed by operation planning based on a walking control model to obtain target walking control data including:

[0024] Performing operation position recognition processing on the walking data to be processed to obtain operation position data, wherein the operation position data is data used to represent the operation position of the robot;

[0025] Acquire target position data, wherein the target position data is position data used to represent the robot's running target;

[0026] Performing path planning processing on the running position data and the target position data based on the planar obstacle feature data to obtain the walking path data;

[0027] The robot is posture controlled according to the plane slope characteristic data to obtain the walking posture control data.

[0028] Further, the running position data and the target position data are processed by path planning based on the planar obstacle feature data to obtain the walking path data, including:

[0029] Performing path planning model construction processing on the planar obstacle feature data and the preset global map data to obtain path planning model data;

[0030] The path planning model data is processed by a model solution based on the minimum running distance to obtain the walking path data, wherein the walking path data is used to represent the walking path corresponding to the target solution, and the target solution is the model solution corresponding to the path planning model data based on the minimum running distance.

[0031] According to a second aspect of the present application, a data processing device for robot walking control is proposed, comprising:

[0032] A data acquisition module, used to acquire walking data to be processed, wherein the walking data to be processed is environmental data collected for representing the walking of the robot;

[0033] A plane feature extraction module, used for performing plane feature extraction processing based on point cloud clustering on the walking data to be processed to obtain plane feature data, wherein the plane feature data includes plane slope feature data and plane obstacle feature data in the robot walking environment;

[0034] The walking control module is used to perform operation planning processing on the plane slope characteristic data and the plane obstacle characteristic data based on the walking control model to obtain target walking control data, wherein the target walking control data includes walking path data and walking posture control data.

[0035] Furthermore, the plane feature extraction module includes:

[0036] An identification module is used to perform point cloud data identification processing on the walking data to be processed to obtain point cloud data to be processed, wherein the point cloud data to be processed is point cloud data used to represent the walking environment of the robot;

[0037] A clustering module, used for performing point cloud clustering processing on the point cloud data to be processed to obtain point cloud clustering data;

[0038] The plane fitting module is used to perform point cloud plane fitting processing on the point cloud clustering data to obtain the plane feature data, wherein the plane feature data is used to indicate that it is feature data of the point cloud plane obtained by fitting.

[0039] According to a third aspect of the present application, a computer-readable storage medium is proposed, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned data processing method for robot walking control.

[0040] According to the fourth aspect of the present application, an electronic device is proposed, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the above-mentioned data processing method for robot walking control.

[0041] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0042] In the present application, the walking data to be processed is obtained, wherein the walking data to be processed is the environmental data collected for representing the walking of the robot; the walking data to be processed is subjected to plane feature extraction processing based on point cloud clustering to obtain plane feature data, wherein the plane feature data includes plane slope feature data and plane obstacle feature data in the robot walking environment; the plane slope feature data and the plane obstacle feature data are subjected to operation planning processing based on the walking control model to obtain target walking control data, wherein the target walking control data includes walking path data and walking posture control data. By identifying the plane slope and obstacle data in the robot walking environment, performing path planning and robot operation posture control according to the slope and obstacle data, and performing obstacle detection in an operating environment with a slope, the effect of improving the robot's slope operation control efficiency is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0044] Figure 1 A flow chart of a data processing method for robot walking control provided in this application;

[0045] Figure 2 A flow chart of a data processing method for robot walking control provided in this application;

[0046] Figure 3 A flow chart of a data processing method for robot walking control provided in this application;

[0047] Figure 4 A schematic diagram of a data processing device for robot walking control provided in the present application;

[0048] Figure 5 A schematic diagram of another data processing device for robot walking control provided in this application. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0050] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0051] In the present application, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings. These terms are mainly used to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0052] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to specific circumstances.

[0053] In addition, the terms "installed", "set", "provided with", "connected", "connected", and "socketed" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0054] In some optional embodiments of the present application, a data processing method for robot walking control is proposed. Figure 1 A flowchart of a data processing method for robot walking control provided in this application, such as Figure 1 As shown, the method comprises the following steps:

[0055] S101: Acquire walking data to be processed;

[0056] The walking data to be processed is used to represent the environmental data collected by the robot while walking;

[0057] S102: performing plane feature extraction processing based on point cloud clustering on the walking data to be processed to obtain plane feature data;

[0058] The plane feature data includes plane slope feature data and plane obstacle feature data in the robot walking environment;

[0059] In some optional embodiments of the present application, a data processing method for robot walking control is proposed. Figure 2 A flowchart of a data processing method for robot walking control provided in this application, such as Figure 2 As shown, the method comprises the following steps:

[0060] S201: performing point cloud data recognition processing on the walking data to be processed to obtain point cloud data to be processed;

[0061] The point cloud data to be processed is point cloud data used to represent the robot's walking environment;

[0062] S202: performing point cloud clustering processing on the point cloud data to be processed to obtain point cloud clustering data;

[0063] S203: performing point cloud plane fitting processing on the point cloud clustering data to obtain plane feature data;

[0064] The plane feature data is feature data used to represent the fitted point cloud plane.

[0065] In some optional embodiments of the present application, a data processing method for robot walking control is proposed, comprising:

[0066] The point cloud clustering data is subjected to point cloud plane fitting processing to obtain fitting plane data; the fitting plane data is subjected to first plane feature generation processing based on point cloud filtering to obtain first plane feature data, wherein the first plane feature data is feature data for representing obstacles on the fitting plane; the fitting plane data is subjected to first plane feature generation processing based on plane slope calculation to obtain second plane feature data, wherein the second plane feature data is feature data for representing the slope of the fitting plane; the plane feature data is obtained according to the first plane feature data and the second plane feature data.

[0067] In some optional embodiments of the present application, a data processing method for robot walking control is proposed to implement the above-mentioned first plane feature generation process, the method comprising:

[0068] The fitting plane data is subjected to a plane feature vector generation process to obtain plane feature vector data, wherein the plane feature vector data is data for representing a point cloud feature vector on the fitting plane; the point cloud clustering data is subjected to a first point cloud filtering process according to the plane feature vector data to obtain process obstacle point cloud data, wherein the process obstacle point cloud data is data for representing point cloud clustering data that is not filtered out by the plane feature vector; the process obstacle point cloud data is subjected to a second point cloud filtering process based on a preset slope threshold to obtain the first plane feature data.

[0069] The process obstacle point cloud data is processed based on point cloud feature extraction to obtain obstacle point cloud feature data; the process obstacle point cloud after filtering the first point cloud is subjected to point cloud feature extraction based on principal component analysis (PCA) algorithm; obstacle feature screening based on point spacing, maximum point threshold and minimum point threshold is performed on the process obstacle point cloud to achieve judgment of the first plane obstacle, including: judging the point spacing between any two points in the process obstacle point cloud; if the point spacing between the points to be judged is less than a preset point spacing threshold, the two points corresponding to the point spacing between the points to be judged are one type of obstacle point cloud; if the point spacing between the points to be judged is greater than or equal to the preset point spacing threshold, the two points corresponding to the point spacing between the points to be judged are different types of obstacle point clouds; and Based on the judgment of the maximum number of points, if the number of point clouds in the process obstacle point cloud is greater than or equal to the preset maximum point threshold, there are multiple types of obstacle point clouds in the process obstacle point cloud. In the above process, the process obstacle point cloud is not completely segmented, and the process obstacle point cloud is again segmented based on point cloud clustering until the number of point clouds in a single process type obstacle point cloud is less than the preset maximum point threshold; by performing screening processing based on the minimum points on the process obstacle point cloud, if the number of point clouds of any type of obstacle point cloud in the process obstacle point cloud is less than the preset minimum point threshold, this type of obstacle point cloud is eliminated.

[0070] In an embodiment of the present application, point cloud clustering and plane fitting are performed on the collected point cloud data, and point cloud filtering is performed on the clustered point cloud to achieve point cloud map construction of plane slopes and plane obstacles in the environment, so that path planning and robot operation posture control can be performed based on the slope and obstacle data, thereby achieving the effect of improving the robot's slope operation control efficiency.

[0071] S103: performing operation planning processing based on a walking control model on the plane slope characteristic data and the plane obstacle characteristic data to obtain target walking control data;

[0072] The target walking control data includes walking path data and walking posture control data.

[0073] In some optional embodiments of the present application, a data processing method for robot walking control is proposed. Figure 3 A flowchart of a data processing method for robot walking control provided in this application, such as Figure 3 As shown, the method comprises the following steps:

[0074] S301: Performing running position identification processing on the walking data to be processed to obtain running position data;

[0075] The operating position data is data used to represent the operating position of the robot;

[0076] S302: Acquire target location data;

[0077] The target position data is the position data used to represent the robot's running target;

[0078] S303: performing path planning processing on the running position data and the target position data based on the planar obstacle feature data to obtain walking path data;

[0079] S304: Controlling the posture of the robot according to the plane slope characteristic data to obtain walking posture control data.

[0080] In some optional embodiments of the present application, a data processing method for robot walking control is proposed, comprising:

[0081] The planar obstacle feature data and the preset global map data are processed by path planning model construction to obtain path planning model data; the path planning model data is processed by model solving based on the minimum running distance to obtain the walking path data, wherein the walking path data is used to represent the walking path corresponding to the target solution, and the target solution is the model solution corresponding to the path planning model data based on the minimum running distance.

[0082] In an optional embodiment of the present application, a robot grid map is constructed by rasterizing planar obstacle feature data and preset global map data; based on the above robot grid map, as well as the current operating position and target position of the robot, based on constructing a robot path planning model, several adjacent nodes of the current operating position node in the grid map are traversed, and if the several adjacent nodes have been traversed, the adjacent nodes are skipped; if the adjacent nodes have not been traversed, the estimated distance from the adjacent nodes to the target position node is calculated, and the adjacent nodes are marked as traversed; the above operation is repeated until the unmarked points in the grid map are empty or the target position node is found, and the path planning of the robot from the operating position to the target position is completed.

[0083] In some optional embodiments of the present application, a data processing method for robot walking control is proposed, which performs posture control on the robot according to plane slope characteristic data to obtain walking posture control data, including:

[0084] Acquire the robot's running posture data, construct and process the plane slope characteristic data according to the walking posture planning model, and obtain the running control equation; solve the above robot running posture data according to the running control equation to obtain the above walking posture control data, wherein the running control equation includes the walking control equation and the acquisition control equation; control the robot's walking parameters according to the walking control equation, for example, the robot's running speed, the robot's torque, etc., to achieve parameter control of the robot's walking structure; control the environmental information acquisition device according to the acquisition control equation, for example, the data transmission and receiving angles of the radar sensor, the angle of the depth camera, etc., to achieve control of the robot's information acquisition device parameters, and optimize the information acquisition during the robot's walking process.

[0085] In some optional embodiments of the present application, a data processing device for robot walking control is proposed. Figure 4 A schematic diagram of a data processing device for robot walking control is provided for this application, such as Figure 4 As shown, this includes:

[0086] A data acquisition module 41 is used to acquire walking data to be processed, wherein the walking data to be processed is environmental data collected for representing the walking of the robot;

[0087] A plane feature extraction module 42 is used to perform plane feature extraction processing based on point cloud clustering on the walking data to be processed to obtain plane feature data, wherein the plane feature data includes plane slope feature data and plane obstacle feature data in the robot walking environment;

[0088] The walking control module 43 is used to perform operation planning processing on the plane slope characteristic data and the plane obstacle characteristic data based on the walking control model to obtain target walking control data, wherein the target walking control data includes walking path data and walking posture control data.

[0089] In some optional embodiments of the present application, a data processing device for robot walking control is proposed. Figure 5 A schematic diagram of a data processing device for robot walking control is provided for this application, such as Figure 5 As shown, this includes:

[0090] The recognition module 51 is used to perform point cloud data recognition processing on the walking data to be processed to obtain point cloud data to be processed, wherein the point cloud data to be processed is point cloud data used to represent the walking environment of the robot;

[0091] A clustering module 52 is used to perform point cloud clustering processing on the point cloud data to be processed to obtain point cloud clustering data;

[0092] The plane fitting module 53 is used to perform point cloud plane fitting processing on the point cloud clustering data to obtain the plane feature data, wherein the plane feature data is used to indicate that it is feature data of the point cloud plane obtained by fitting.

[0093] The specific manner of executing the operation of each unit in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0094] In summary, in the present application, the walking data to be processed is obtained, wherein the walking data to be processed is the environmental data collected to represent the walking of the robot; the walking data to be processed is subjected to plane feature extraction processing based on point cloud clustering to obtain plane feature data, wherein the plane feature data includes plane slope feature data and plane obstacle feature data in the robot walking environment; the plane slope feature data and the plane obstacle feature data are subjected to operation planning processing based on the walking control model to obtain target walking control data, wherein the target walking control data includes walking path data and walking posture control data. By identifying the plane slope and obstacle data in the robot walking environment, performing path planning and robot operation posture control according to the slope and obstacle data, and performing obstacle detection in an operating environment with a slope, the effect of improving the robot's slope operation control efficiency is achieved.

[0095] The present invention also provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0096] Among them, the storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, referred to as ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also exist in a communication device as discrete components. The storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0097] The present invention also provides a program product, which includes an execution instruction, which is stored in a storage medium. At least one processor of a device can read the execution instruction from the storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0098] In the above-mentioned terminal or server embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0099] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0100] Obviously, those skilled in the art should understand that the above-mentioned units or steps of the present application can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0101] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method for robot walking control, characterized in that: include: Acquire walking data to be processed, wherein the walking data to be processed is environmental data collected for representing the walking of the robot; Performing plane feature extraction processing based on point cloud clustering on the walking data to be processed to obtain plane feature data, wherein the plane feature data includes plane slope feature data and plane obstacle feature data in the robot walking environment; The plane slope characteristic data and the plane obstacle characteristic data are processed by operation planning based on a walking control model to obtain target walking control data, wherein the target walking control data includes walking path data and walking posture control data.

2. The data processing method according to claim 1, characterized in that: The plane feature extraction processing based on point cloud clustering is performed on the walking data to be processed, and the plane feature data obtained includes: Performing point cloud data recognition processing on the walking data to be processed to obtain point cloud data to be processed, wherein the point cloud data to be processed is point cloud data used to represent the walking environment of the robot; Performing point cloud clustering processing on the point cloud data to be processed to obtain point cloud clustering data; The point cloud clustering data is subjected to point cloud plane fitting processing to obtain the plane feature data, wherein the plane feature data is feature data used to indicate that it is a point cloud plane obtained by fitting.

3. The data processing method according to claim 2, characterized in that: Performing point cloud plane fitting processing on the point cloud clustering data to obtain the plane feature data includes: Performing point cloud plane fitting processing on the point cloud clustering data to obtain fitting plane data; Performing a first plane feature generation process based on point cloud filtering on the fitting plane data to obtain first plane feature data, wherein the first plane feature data is feature data for representing obstacles on the fitting plane; Performing a first plane feature generation process based on plane slope calculation on the fitting plane data to obtain second plane feature data, wherein the second plane feature data is feature data for representing the slope of the fitting plane; The plane feature data is obtained according to the first plane feature data and the second plane feature data.

4. The data processing method according to claim 3, characterized in that: The fitting plane data is subjected to a first plane feature generation process based on point cloud filtering to obtain the first plane feature data, including: Performing plane feature vector generation processing on the fitting plane data to obtain plane feature vector data, wherein the plane feature vector data is data used to represent a point cloud feature vector on the fitting plane; Performing a first point cloud filtering process on the point cloud clustering data according to the plane feature vector data to obtain process obstacle point cloud data, wherein the process obstacle point cloud data is used to represent point cloud data in the point cloud clustering data that is not filtered out by the plane feature vector; The process obstacle point cloud data is subjected to a second point cloud filtering process based on a preset slope threshold to obtain the first plane feature data.

5. The data processing method according to claim 1, characterized in that: The plane slope characteristic data and the plane obstacle characteristic data are processed by operation planning based on a walking control model to obtain target walking control data including: Performing operation position recognition processing on the walking data to be processed to obtain operation position data, wherein the operation position data is data used to represent the operation position of the robot; Acquire target position data, wherein the target position data is position data used to represent the robot's running target; Performing path planning processing on the running position data and the target position data based on the planar obstacle feature data to obtain the walking path data; The robot is posture controlled according to the plane slope characteristic data to obtain the walking posture control data.

6. The data processing method according to claim 5, characterized in that: The running position data and the target position data are processed by path planning based on the planar obstacle feature data to obtain the walking path data, including: Performing path planning model construction processing on the planar obstacle feature data and the preset global map data to obtain path planning model data; The path planning model data is processed by a model solution based on the minimum running distance to obtain the walking path data, wherein the walking path data is used to represent the walking path corresponding to the target solution, and the target solution is the model solution corresponding to the path planning model data based on the minimum running distance.

7. A data processing device for robot walking control, characterized in that: include: A data acquisition module, used to acquire walking data to be processed, wherein the walking data to be processed is environmental data collected for representing the walking of the robot; A plane feature extraction module, used for performing plane feature extraction processing based on point cloud clustering on the walking data to be processed to obtain plane feature data, wherein the plane feature data includes plane slope feature data and plane obstacle feature data in the robot walking environment; The walking control module is used to perform operation planning processing on the plane slope characteristic data and the plane obstacle characteristic data based on the walking control model to obtain target walking control data, wherein the target walking control data includes walking path data and walking posture control data.

8. The data processing device according to claim 7, characterized in that: The plane feature extraction module includes: An identification module is used to perform point cloud data identification processing on the walking data to be processed to obtain point cloud data to be processed, wherein the point cloud data to be processed is point cloud data used to represent the walking environment of the robot; A clustering module, used for performing point cloud clustering processing on the point cloud data to be processed to obtain point cloud clustering data; The plane fitting module is used to perform point cloud plane fitting processing on the point cloud clustering data to obtain the plane feature data, wherein the plane feature data is used to indicate that it is feature data of the point cloud plane obtained by fitting.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the data processing method for robot walking control according to any one of claims 1 to 6.

10. An electronic device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the data processing method for robot walking control described in any one of claims 1-6.

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