Method and apparatus for generating robot capability ontology information, and device and medium

Abstract

A method and apparatus (600) for generating robot capability ontology information, comprising: acquiring capability information (S210) and basic component information (S220) of a target robot; establishing a mapping relationship between capability components and the basic component information on the basis of the capability information (S230); and generating capability parameter values on the basis of a basic component parameter set and the mapping relationship (S240), and then generating capability ontology information of the target robot (S250). According to the method, a mapping relationship is established between abstract capability information of a robot and actual basic components of the robot on the basis of capability dimensions, so as to enable capability-based evaluation of the robot and generate capability ontology information of the robot. By means of the method, complex information of basic components provided by robots themselves can be ignored, and the capability difference of the robots can be described from the perspective of robot capabilities, thereby providing a basis for better evaluation of the capability difference of different robots and more precise control of robots.

Description

Methods, apparatus, equipment and media for generating robot capability ontology information

[0001] Cross-reference to related applications

[0002] This disclosure claims priority to Chinese Patent Application No. 202410910165.6, filed on July 8, 2024, entitled “Method, Apparatus, Device and Medium for Generating Robot Capability Ontology Information”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of robotics, and more specifically, to a method, apparatus, electronic device, and storage medium for generating robot capability ontology information. Background Technology

[0004] In smart spaces, in order to better establish the interaction between robots and the environment, objects and users, it is necessary to accurately obtain real-time capability information of service robots. For example, robots with bionic hands need to have the ability to grasp, press, pinch and lift.

[0005] However, it's worth noting that due to differences in basic structure, material selection, and technological implementation, different robots can perform very differently even when faced with similar commands. For example, in terms of grasping ability, robots have different load limits; some can easily lift objects weighing several kilograms, while others can only handle lightweight items. Furthermore, the material and shape of the objects being grasped are also influencing factors; some robots may be adept at handling smooth surfaces but struggle with irregular or fragile objects.

[0006] Therefore, it is necessary to establish a mapping relationship between the robot's basic components and its execution capabilities, so as to provide the upper-level system with the robot's capability status information in real time. This will enable the upper-level system to allocate tasks based on the robot's capabilities, making the robot's application and control more precise and intelligent.

[0007] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] This disclosure provides a method, apparatus, electronic device, and storage medium for generating robot capability ontology information.

[0009] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0010] According to one aspect of this disclosure, a method for generating robot capability ontology information is provided. The method includes: acquiring at least one capability information of a target robot; the capability information corresponding to at least one capability dimension; acquiring multiple basic component information of the target robot; the basic component information including at least: a basic component identifier, a basic component type, and a basic component parameter set; the target robot being composed of multiple basic components; establishing capability components based on the capability information; a mapping relationship between the capability components and the basic component information; generating capability parameter values ​​corresponding to the capability dimensions based on the basic component parameter set and the mapping relationship; and generating capability ontology information of the target robot based on the capability parameter values ​​corresponding to each capability dimension.

[0011] In an exemplary embodiment, the basic component is one or more of a mechanical unit, a sensor unit, a control unit, and a software driving unit.

[0012] In an exemplary embodiment, the set of basic component parameters includes: index parameters, operating parameters, accuracy parameters, and / or status parameters.

[0013] In an exemplary embodiment, a mapping relationship exists between the capability component and the basic component information, including: establishing a mapping relationship between the capability component and the basic component information based on a pre-defined mapping relationship between the capability component and the basic component type.

[0014] In an exemplary embodiment, the capability component corresponds to at least one of the basic component information; each of the basic component information corresponds to a capability percentage.

[0015] In an exemplary embodiment, generating capability parameter values ​​corresponding to the capability dimension based on the basic component parameter set and the mapping relationship includes: obtaining capability assessment criteria corresponding to the capability dimension; generating a corresponding capability assessment model based on the capability assessment criteria; and inputting the basic component parameter set corresponding to the capability dimension into the capability assessment model based on the mapping relationship to obtain the capability parameter values.

[0016] In an exemplary embodiment, the capability assessment criteria include capability grading standards; capability levels are determined based on the capability grading standards and capability parameter values.

[0017] In an exemplary embodiment, the step of inputting the set of basic component parameters corresponding to the capability dimension into the capability assessment model according to the mapping relationship to obtain the capability parameter value includes: the capability component corresponds to at least one set of basic component information; each set of basic component information corresponds to a capability percentage; and the set of basic component parameters corresponding to the capability dimension and the capability percentage are input into the capability assessment model according to the mapping relationship to obtain the capability parameter value.

[0018] In an exemplary embodiment, generating the capability ontology information of the target robot based on the capability parameter values ​​corresponding to each capability dimension includes: the capability ontology information includes: robot capability description information and execution object description information; and determining the robot capability description information and execution object description information based on the capability parameter values ​​corresponding to each capability dimension.

[0019] According to another aspect of this disclosure, an apparatus for generating robot capability ontology information is provided, comprising: a capability set module configured to acquire at least one capability information of a target robot; the capability information corresponding to at least one capability dimension; a basic component module configured to acquire multiple basic component information of the target robot; the basic component information at least includes: a basic component identifier, a basic component type, and a basic component parameter set; the target robot is composed of multiple basic components; a capability component module configured to establish capability components based on the capability information; the capability components and the basic component information have a mapping relationship; a capability evaluation module configured to generate capability parameter values ​​corresponding to the capability dimensions based on the basic component parameter set and the mapping relationship; and a capability information output module configured to generate capability ontology information of the target robot based on the capability parameter values ​​corresponding to each capability dimension.

[0020] According to another aspect of this disclosure, an electronic device is provided, comprising: one or more processors; and a storage device configured to store one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the method for generating robot capability ontology information as described in the above embodiments.

[0021] According to another aspect of this disclosure, a computer-readable storage medium is provided that stores a computer program, which, when executed by a processor, implements the method for generating robot capability ontology information as described in the above embodiments.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0024] Figure 1 illustrates a schematic diagram of an exemplary system architecture to which the methods of embodiments of the present disclosure can be applied.

[0025] Figure 2 shows a flowchart of a method for generating robot capability ontology information according to an embodiment of this disclosure;

[0026] Figure 3 shows a flowchart of the capability parameter value generation method according to an embodiment of the present disclosure;

[0027] Figure 4 shows a framework diagram of a robot capability ontology information generation system according to an embodiment of the present disclosure;

[0028] Figure 5A shows a schematic diagram of capability information according to an embodiment of the present disclosure;

[0029] Figure 5B shows a schematic diagram of the capability components according to an embodiment of the present disclosure;

[0030] Figure 5C illustrates a schematic diagram of the capability assessment criteria according to an embodiment of this disclosure;

[0031] Figure 5D shows a schematic diagram of capability ontology information according to an embodiment of this disclosure;

[0032] Figure 6 shows a schematic diagram of the structure of a device for generating robot capability ontology information according to an embodiment of the present disclosure;

[0033] Figure 7 shows a schematic diagram of the structure of an electronic device suitable for implementing exemplary embodiments of the present disclosure. Detailed Implementation

[0034] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0035] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0036] It should be noted that the ordinal numbers such as "first" and "second" mentioned in the embodiments of this disclosure are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects. Furthermore, the descriptions of "first" and "second" do not limit the objects to necessarily being different.

[0037] Figure 1 illustrates a schematic diagram of an exemplary system architecture to which the methods of embodiments of the present disclosure can be applied.

[0038] As shown in Figure 1, the system architecture may include server 101, network 102, terminal device 103, terminal device 104, and terminal device 105. Network 102 serves as the medium for providing a communication link between terminal device 103, terminal device 104, or terminal device 105 and server 101. Network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0039] Server 101 can be a server that provides various services, such as a back-end management server that supports the devices operated by users using terminal devices 103, 104, or 105. The back-end management server can analyze and process received requests and other data, and feed back the processing results to terminal devices 103, 104, or 105.

[0040] Terminal devices 103, 104, and 105 can be smartphones, tablets, laptops, desktop computers, smart speakers, wearable smart devices, virtual reality devices, augmented reality devices, etc., but are not limited to these.

[0041] It should be understood that the number of terminal devices 103, 104, 105, network 102 and server 101 in Figure 1 is merely illustrative. Server 101 can be a single physical server, a server cluster consisting of multiple servers, or a cloud server. Depending on actual needs, it can have any number of terminal devices, networks and servers.

[0042] The steps of the method in the exemplary embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings and examples.

[0043] Figure 2 shows a flowchart of a method for generating robot capability ontology information according to an embodiment of this disclosure. The method provided in the embodiment of Figure 2 can be executed by any electronic device, such as the terminal device in Figure 1, or the server in Figure 1, or the terminal device and server in Figure 1 executed jointly, but this disclosure does not limit it. As shown in Figure 2, the method for generating robot capability ontology information provided in the embodiment of this disclosure may include the following steps.

[0044] In step S210, at least one capability information of the target robot is obtained; the capability information corresponds to at least one capability dimension.

[0045] As mentioned earlier, the differences in the basic components of robots lead to variations in the degree to which they perform these actions and the objects of those actions. To address these technical issues, this disclosure proposes a method for generating robot capability ontology information. This method abstracts the robot's differentiated basic components into ontological capability descriptions based on the functions to be achieved. Capability ontology information refers to the information used in applying basic ontological methods, through concept analysis and modeling, to abstract real-world entities into descriptive information about the capabilities provided by the robot.

[0046] In this embodiment, robots are pre-classified based on the functions they are to perform, and corresponding capability description templates are pre-configured for each robot type. These capability description templates describe the capabilities of the robot through multiple capability information pieces. The capability information characterizes the capabilities the robot needs to achieve. Furthermore, each capability information piece corresponds to multiple capability dimensions. These capability dimensions characterize the various dimensions used to evaluate the capability information.

[0047] In an exemplary embodiment, for a bionic hand robot, based on the functions to be achieved by this type of robot, it must at least possess robot mobility, object position recognition, and object grasping capabilities. Therefore, the capability description template for this type of robot includes: mobility, recognition, and grasping capabilities. To evaluate each capability, each capability information is further divided into multiple capability dimensions. For example, mobility capability corresponds to capability dimensions such as planar movement range, planar movement speed, vertical movement range, and vertical movement speed. Recognition capability corresponds to capability dimensions such as planar recognition, depth recognition, color recognition, object identification, and recognition accuracy. Grasping capability corresponds to capability dimensions such as range of motion, grasping ability, pressing ability, load-bearing weight, and force control accuracy. It can be seen that through the above multiple capability information and their corresponding capability dimensions, the capabilities that the robot can provide can be abstractly described from a functional implementation perspective.

[0048] Figure 5A illustrates a schematic diagram of capability information according to an embodiment of this disclosure. As shown in Figure 5A, capability information can be classified and managed hierarchically based on capability categories. The data structure for each piece of capability information may include: capability ID, capability name, capability description, and capability dimension. The capability ID is the unique identifier of the capability information. The capability name is the name of the capability information. The capability description is descriptive information about the capability information. The capability dimension may include several capability dimensions corresponding to the capability information. It should be noted that this example diagram only provides an exemplary implementation of the capability information data structure and is not intended to limit the scope of protection of the capability information data structure.

[0049] In an exemplary embodiment, since the robot may need to perform different functions depending on different business scenarios, and thus correspond to different capability information, this capability information can further correspond to application scenario information to achieve accurate description and management of the capability information.

[0050] In an exemplary embodiment, the capability description template can be pre-stored in the capability set module, and the corresponding capability description template can be called according to the robot type corresponding to the target robot.

[0051] In step S220, information on multiple basic components of the target robot is obtained; the basic component information includes at least: basic component identifier, basic component type and basic component parameter set; the target robot is composed of multiple basic components.

[0052] In this embodiment, the target robot is composed of multiple basic components. Each basic component is a physical component of the target robot and is the smallest unit constituting the target robot. Each basic component has corresponding basic component information. This basic component information is a performance description of the basic component. The basic component information includes at least: a basic component identifier, a basic component type, and a basic component parameter set. The basic component identifier is the identification information of the basic component, such as its number, name, or model. The basic component type is the type information of the basic component, such as a vision sensor, drive arm, moving wheel, or moving leg. The basic component parameter set is a set of performance parameters for the basic component. The performance of each basic component of the target robot can be determined through this basic component information.

[0053] In an exemplary embodiment, the basic component may be one or more of a mechanical unit, a sensor unit, a control unit, and a software-driven unit. A mechanical unit refers to a mechanical drive unit, such as a drive arm or wheels. A sensor unit is a unit capable of sensing the information being measured, such as a vision sensor or a gyroscope. A control unit is a unit that processes control commands, such as a CPU, ECU, or microprocessor. A software-driven unit is the software program that drives the robot, such as an object recognition algorithm or obstacle avoidance algorithm.

[0054] In an exemplary embodiment, the set of parameters for the basic component may include: performance parameters, operational parameters, accuracy parameters, and / or status parameters. The performance parameters refer to the static performance parameters of the basic component. The operational parameters refer to the dynamic operational parameters of the basic component. The accuracy parameters refer to the operational accuracy parameters of the basic component. The status parameters refer to the current status parameters of the basic component.

[0055] In an exemplary embodiment, the basic component information may be pre-stored in the basic component module, or the basic component module may retrieve the relevant basic component information from the robot knowledge base.

[0056] In step S230, a capability component is established based on the capability information; the capability component has a mapping relationship with the basic component information.

[0057] In this embodiment of the disclosure, capability components corresponding to each capability dimension are established based on the capability information obtained in step S210. A capability component refers to the basic component required to realize the function of that capability dimension. For example, realizing grasping capability requires at least a pair of mechanical fingers, a drive motor, and a drive control unit. Each robot uses different basic components to realize grasping capability; it may use a pair of mechanical fingers or multiple mechanical fingers, and the drive motors used are also different. A capability component is a logical concept; it does not specify the exact solution adopted by each robot, but it clarifies which types of basic components are needed to realize grasping capability.

[0058] Based on the type of basic component required by the basic component, and using the multiple basic component information obtained in step S220 above, a mapping relationship is established between the capability component and the basic component information in the target robot. It should be noted that the capability component may correspond to one or more basic component information. Similarly, one piece of basic component information may simultaneously correspond to one or more capability components.

[0059] Figure 5B illustrates a schematic diagram of capability components according to an embodiment of this disclosure. As shown in Figure 5B, multiple capability components can correspond to a certain capability information (based on capability ID), each corresponding to a different capability dimension in that capability information. In the figure, one row in the capability component column represents one capability component, corresponding to a capability dimension in the same row. This capability component can be mapped to multiple basic component information. This basic component information includes basic components and basic component parameter sets. For example, in the figure, capability dimension 1 is mapped to basic component 1 and basic component 2; capability dimension 2 is mapped to basic component 2; and capability dimension 3 is mapped to basic component 2 and basic component 3.

[0060] In an exemplary embodiment, a mapping relationship between capability components and basic component types is pre-defined based on prior knowledge. For example, a grasping capability component is mapped to basic components such as mechanical fingers, drive motors, and drive control units. Based on this pre-defined mapping relationship, corresponding basic component information is selected from the basic component information provided by the target robot, establishing a mapping relationship between the capability component and the basic component information of the target robot. In this way, an abstract capability concept can be mapped to the specific basic components provided by each robot, and based on the basic component parameter set of the specific basic components, the capability differences of the target robot used to achieve the function can be obtained.

[0061] In an exemplary embodiment, each capability component corresponds to at least one basic component information. Furthermore, there is a capability percentage among the various basic capability information corresponding to the same capability component. This capability percentage reflects the different proportions of roles played by different basic components in achieving the capability function. This provides a data basis for evaluating the differences in the level of various robots in achieving relevant capability functions.

[0062] In an exemplary embodiment, step S230 is performed by the capability component module. This capability component module obtains capability information and basic component information from the capability set module and the basic component module, respectively, and establishes a mapping relationship between the capability components and the basic component information based on the obtained information.

[0063] In step S240, capability parameter values ​​corresponding to the capability dimension are generated based on the basic component parameter set and the mapping relationship.

[0064] In this embodiment of the disclosure, capability parameter values ​​corresponding to the capability dimension are generated based on the basic component parameter set and mapping relationship obtained in the preceding steps. As mentioned earlier, this mapping relationship can establish a correspondence between the abstract capability dimension and the basic components actually provided by each robot, and based on the obtained basic component parameter set of each basic component, the capability parameter values ​​of interest for that capability dimension can be calculated and converted. These capability parameter values ​​can be a specific parameter value or a range of parameter values.

[0065] For example, consider evaluating a robot's mobility. Robot 1's mobility is provided by a set of wheels. Based on the basic component parameters of these wheels, it can move in a plane with a maximum speed of 1 m / s, but it lacks vertical mobility. Robot 2's mobility is provided by a pair of mechanical legs. Based on the basic component parameters of these legs, it can move in a plane with a maximum speed of 0.5 m / s, and it also possesses vertical mobility with a maximum vertical stride of 20 cm. Therefore, by using the capability parameter values ​​corresponding to the capability dimension, we can describe the robot's capabilities based on the desired function, regardless of the robot's specific basic component information, facilitating comparison and evaluation of the capabilities of different robots.

[0066] In an exemplary embodiment, step S240 is performed by the capability assessment module. This module obtains the mapping relationship between capability components and basic component information established by the capability component module, and generates capability parameter values ​​corresponding to the capability dimension.

[0067] In step S250, the capability ontology information of the target robot is generated based on the capability parameter values ​​corresponding to each capability dimension.

[0068] In this embodiment of the disclosure, capability ontology information of the target robot is generated based on the capability parameter values ​​corresponding to each capability dimension generated in the aforementioned steps. As mentioned earlier, this capability ontology information is descriptive information that abstracts entities in the real world into capabilities provided by the robot. This capability ontology information may include: robot capability description information and execution object description information. The robot capability description information and execution object description information are determined based on the capability parameter values ​​corresponding to each capability dimension. The robot capability description information describes the capabilities possessed by the robot itself, such as the spatial range in which the robot's arm can move. The execution object description information describes the target object that the robot performs actions on, such as the size of objects that the robotic arm can grasp, or the height of obstacles that the simulated foot can traverse.

[0069] By combining robot capability description information and execution object description information, the robot's capability ontology can be described from both the robot's own capabilities and the execution object. This capability ontology information allows us to disregard the complex information provided by the basic components of each robot, describing the capability differences between robots from a capability perspective, and providing a foundation for better evaluating the capability differences between different robots.

[0070] Figure 5D illustrates a schematic diagram of capability ontology information according to an embodiment of this disclosure. As shown in Figure 5D, the capability ontology information may include robot capability description information and execution object description information. The robot capability description information and execution object description information may each include one or more pieces of information. This robot capability description information and execution object description information may correspond to the aforementioned capability dimensions, or they may not correspond to capability dimensions, but rather be capability ontology information obtained through capability parameter values ​​corresponding to multiple capability dimensions.

[0071] In an exemplary embodiment, step S250 is performed by the capability information output module. This capability information output module stores the generated capability ontology information of the target robot in a robot knowledge base.

[0072] The method for generating robot capability ontology information provided in this disclosure establishes a mapping relationship between the abstract capability information of the robot and the actual basic components of the robot based on the capability dimension, thereby evaluating the robot based on its capabilities and forming the robot's capability ontology information. This method can ignore the complex information of the basic components provided by each robot itself, describing the capability differences of each robot from the perspective of robot capabilities, providing a foundation for better evaluation of the capability differences between different robots and more precise robot control.

[0073] Figure 3 shows a flowchart of a capability parameter value generation method according to an embodiment of this disclosure. As shown in Figure 3, in this embodiment of the disclosure, generating capability parameter values ​​corresponding to the capability dimension based on the basic component parameter set and the mapping relationship may include the following steps.

[0074] In step S310, the capability assessment criteria corresponding to the capability dimension are obtained.

[0075] In this embodiment of the disclosure, capability evaluation criteria corresponding to each of the capability dimensions are pre-set. These criteria standardize the evaluation criteria for each capability dimension. Based on these criteria, the capabilities of the target robot can be evaluated using basic component information to obtain corresponding capability parameter values.

[0076] Figure 5C illustrates a schematic diagram of the capability assessment criteria according to an embodiment of the present disclosure. As shown in Figure 5C, in the capability assessment criterion data structure, each capability assessment criterion corresponds to a capability dimension.

[0077] In an exemplary embodiment, the capability assessment criteria are stored together with the capability information in the capability set module. The capability assessment module retrieves the corresponding capability assessment criteria from the capability set module.

[0078] In step S320, a corresponding capability assessment model is generated according to the capability assessment criteria.

[0079] In this embodiment of the disclosure, a corresponding capability assessment model is generated based on capability assessment criteria. This capability assessment model is used to assess the corresponding capability dimension and generate capability parameter values.

[0080] In an exemplary embodiment, the capability assessment model is a pre-configured capability assessment module. The corresponding capability assessment module is invoked according to the capability assessment criteria to assess the capability dimensions. This capability assessment module includes a preset capability assessment algorithm.

[0081] In an exemplary embodiment, the capability assessment model is a pre-trained artificial intelligence model. Using the pre-trained corresponding artificial intelligence model, capability dimensions can be assessed based on the information of the corresponding basic components.

[0082] In step S330, according to the mapping relationship, the set of basic component parameters corresponding to the capability dimension is input into the capability evaluation model to obtain the capability parameter values.

[0083] In this embodiment of the disclosure, based on the mapping relationship determined in the aforementioned step S230, the basic component parameters in the basic component information corresponding to the capability dimension are input into the corresponding capability assessment model to obtain the capability parameter value.

[0084] In an exemplary embodiment, as described above, each capability component corresponds to at least one of the aforementioned basic component information; each of the basic component information pieces corresponds to a capability percentage. This capability percentage reflects the different proportions of contribution made by different basic components in realizing the capability function. In step S330, when calculating the capability parameter value, the capability parameter value is also calculated based on the capability percentage corresponding to each basic component information piece. This method allows for a more accurate evaluation of the capability parameter value.

[0085] In an exemplary embodiment, the capability assessment criteria also include a capability grading standard. This grading standard is used to classify the robot's corresponding capability dimensions into levels. Based on the capability grading standard and the capability parameter values ​​obtained in step S330, the capability level corresponding to that capability dimension can be determined. For some application scenarios, people may not care about the specific capability parameter values; it is sufficient to divide the capability into several levels, such as levels A, B, and C. Each level corresponds to a range of capability parameter values. The capability level of the target robot is determined based on these capability parameter values.

[0086] The method for generating robot capability ontology information provided in this disclosure evaluates each capability dimension of the robot by pre-setting capability evaluation criteria corresponding to the capability dimensions, thereby obtaining corresponding capability parameter values. These capability evaluation criteria can unify and standardize the capability evaluation standards among various robots, making the obtained capability parameter values ​​more objective and comparable. Furthermore, by setting corresponding capability proportions for each basic component information, the capability parameter values ​​can be evaluated more accurately.

[0087] Figure 4 shows a framework diagram of the robot capability ontology information generation system according to an embodiment of this disclosure. As shown in Figure 4, in this embodiment of the disclosure, the system includes: a robot knowledge base, a basic component module, a capability set module, a capability component module, a capability evaluation module, and a capability information output module.

[0088] The robot knowledge base stores knowledge information related to each robot, including data attribute information and interaction information. It also stores the capability ontology information of each robot.

[0089] The basic component module contains pre-set standardized basic component information for each robot. A basic component is a physical building block of the robot and the smallest unit available for evaluation. This basic component can be one or more of a mechanical unit, sensor unit, control unit, or software-driven unit. The basic component information includes at least: a basic component identifier, a basic component type, and a basic component parameter set. The parameter set includes: performance parameters, operational parameters, accuracy parameters, and / or status parameters. The robot knowledge base provides the basic component information for each robot within the basic component module.

[0090] The capability set module is a collection of capabilities that the robot possesses, defined for user and application scenarios. It stores capability information in a data structure as shown in Figure 5A. This capability information corresponds to at least one capability dimension. The capability set module outputs this capability information to the capability component module and, simultaneously, outputs the capability evaluation criteria corresponding to each capability dimension to the capability evaluation module.

[0091] The capability component module generates capability components corresponding to the capability dimensions based on the capability information provided by the capability set module, and decomposes the capability components into several basic components based on prior knowledge. The capability component module requests relevant basic component information from the basic component module and establishes a dynamic mapping relationship between capability components and basic component information. Referring to Figure 5B, the corresponding basic component parameter set is filled into the corresponding capability component.

[0092] The capability assessment module generates a capability assessment model based on the capability assessment criteria provided by the capability set module. Based on the mapping relationship established by the capability component module and the information of each basic component, the module uses this capability assessment model to assess the capability of each component and generate capability parameter values ​​corresponding to the capability dimensions.

[0093] The capability information output module generates capability ontology information of the target robot based on the capability parameter values ​​corresponding to each capability dimension, and synchronizes the generated capability ontology information to the robot knowledge base.

[0094] Based on the same inventive concept, this disclosure provides an apparatus for generating robot capability ontology information, as described in the following embodiments. Since the principle by which this apparatus solves the problem is similar to that of the method embodiments described above, the implementation of this apparatus can be directly referenced to the methods described above, and repeated details will not be repeated.

[0095] Figure 6 shows a schematic diagram of the structure of the apparatus for generating robot capability ontology information according to an embodiment of the present disclosure. As shown in Figure 6, the apparatus 600 for generating robot capability ontology information may include: a capability set module 610, a basic component module 620, a capability component module 630, a capability evaluation module 640, and a capability information output module 650.

[0096] The capability set module 610 is configured to acquire at least one capability information of the target robot; the capability information corresponds to at least one capability dimension.

[0097] The basic component module 620 is configured to acquire information on multiple basic components of the target robot; the basic component information includes at least: basic component identifier, basic component type, and basic component parameter set; the target robot is composed of multiple basic components.

[0098] Capability component module 630 is configured to establish capability components based on the capability information; there is a mapping relationship between the capability components and the basic component information.

[0099] The capability assessment module 640 is configured to generate capability parameter values ​​corresponding to the capability dimension based on the basic component parameter set and the mapping relationship.

[0100] The capability information output module 650 is configured to generate capability ontology information of the target robot based on the capability parameter values ​​corresponding to each capability dimension.

[0101] In an exemplary embodiment, the basic component is one or more of a mechanical unit, a sensor unit, a control unit, and a software driving unit.

[0102] In an exemplary embodiment, the set of basic component parameters includes: index parameters, operating parameters, accuracy parameters, and / or status parameters.

[0103] In an exemplary embodiment, the capability component module 630 is further configured to establish a mapping relationship between the capability component and the basic component information based on a pre-defined mapping relationship between the capability component and the basic component type.

[0104] In an exemplary embodiment, the capability component corresponds to at least one of the basic component information; each of the basic component information corresponds to a capability percentage.

[0105] In an exemplary embodiment, the capability assessment module 640 is further configured to: acquire capability assessment criteria corresponding to the capability dimension; generate a corresponding capability assessment model based on the capability assessment criteria; and input the set of basic component parameters corresponding to the capability dimension into the capability assessment model according to the mapping relationship to obtain the capability parameter values.

[0106] In an exemplary embodiment, the capability assessment module 640 is further configured to include capability grading standards in the capability assessment criteria; and to determine the capability level based on the capability grading standards and capability parameter values.

[0107] In an exemplary embodiment, the capability assessment module 640 is further configured such that the capability component corresponds to at least one of the basic component information; each of the basic component information corresponds to a capability percentage; and according to the mapping relationship, the basic component parameter set corresponding to the capability dimension and the capability percentage are input into the capability assessment model to obtain the capability parameter value.

[0108] In an exemplary embodiment, the capability information output module 650 is further configured such that the capability ontology information includes: robot capability description information and execution object description information; and the robot capability description information and execution object description information are determined based on the capability parameter values ​​corresponding to each capability dimension.

[0109] Figure 7 shows a schematic diagram of the structure of an electronic device suitable for implementing exemplary embodiments of the present disclosure. An electronic device 700 according to this embodiment of the present disclosure will now be described with reference to Figure 7. The electronic device 700 shown in Figure 7 is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0110] As shown in Figure 7, the electronic device 700 is presented in the form of a general-purpose computing device. The components of the electronic device 700 may include, but are not limited to: at least one processing unit 710, at least one storage unit 720, a bus 730 connecting different system components (including storage unit 720 and processing unit 710), and a display unit 740.

[0111] The storage unit stores program code that can be executed by the processing unit 710, causing the processing unit 710 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention.

[0112] Storage unit 720 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 7201 and / or cache memory 7202, and may further include a read-only memory (ROM) 7203.

[0113] The storage unit 720 may also include a program / utility 7204 having a set (at least one) program module 7205, such program module 7205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0114] Bus 730 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0115] Electronic device 700 can also communicate with one or more external devices 770 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 700, and / or with any device that enables electronic device 700 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 750. Furthermore, electronic device 700 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 760. As shown, network adapter 760 communicates with other modules of electronic device 700 via bus 730. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0116] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0117] According to embodiments of the present invention, a program product for implementing the above-described method may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0118] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0119] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0120] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0121] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0122] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0123] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0124] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure. Industrial applicability

[0125] This disclosure applies to the field of robotics technology and aims to address the problem of the lack of mapping relationship between basic robot components and execution capabilities in related technologies. It aims to describe the capability differences of various robots from the perspective of robot capabilities, providing a foundation for better evaluation of the capability differences of different robots and more precise robot control.

[0126] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0127] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for generating robot capability ontology information, the method comprising: Obtain at least one capability information of the target robot; The capability information corresponds to at least one capability dimension; Obtain information on multiple basic components of the target robot; The basic component information includes at least: basic component identifier, basic component type, and basic component parameter set; the target robot is composed of multiple basic components. Capability components are established based on the capability information; the capability components and the basic component information have a mapping relationship; Based on the basic component parameter set and the mapping relationship, generate capability parameter values ​​corresponding to the capability dimension; Based on the capability parameter values ​​corresponding to each capability dimension, the capability ontology information of the target robot is generated.

2. The method according to claim 1, wherein, The basic components are one or more of the following: mechanical unit, sensor unit, control unit, and software driving unit.

3. The method according to claim 1, characterized in that, The set of basic component parameters includes: index parameters, operating parameters, accuracy parameters and / or status parameters.

4. The method according to claim 1, wherein, There is a mapping relationship between the capability components and the basic component information, including: Based on the pre-defined mapping relationship between the capability component and the basic component type, a mapping relationship between the capability component and the basic component information is established.

5. The method according to claim 1, wherein, Also includes: The capability component corresponds to at least one of the basic component information; The information of each basic component corresponds to a certain percentage of capability.

6. The method according to claim 1, wherein, The step of generating capability parameter values ​​corresponding to the capability dimension based on the basic component parameter set and the mapping relationship includes: Obtain the capability assessment criteria corresponding to the aforementioned capability dimension; Generate a corresponding capability assessment model based on the aforementioned capability assessment criteria; Based on the mapping relationship, the set of basic component parameters corresponding to the capability dimension is input into the capability assessment model to obtain the capability parameter values.

7. The method according to claim 6, wherein, Also includes: The capability assessment criteria include capability grading standards; The capability level is determined based on the capability grading criteria and capability parameter values.

8. The method according to claim 6, wherein, The step of inputting the set of basic component parameters corresponding to the capability dimension into the capability assessment model according to the mapping relationship to obtain the capability parameter values ​​includes: Each capability component corresponds to at least one of the basic component information; each of the basic component information corresponds to a capability percentage. Based on the mapping relationship, the set of basic component parameters corresponding to the capability dimension and the capability proportion are input into the capability assessment model to obtain the capability parameter value.

9. The method according to claim 1, wherein, The step of generating the capability ontology information of the target robot based on the capability parameter values ​​corresponding to each capability dimension includes: The capability ontology information includes: robot capability description information and execution object description information; Based on the capability parameter values ​​corresponding to each capability dimension, the robot capability description information and execution object description information are determined.

10. An apparatus for generating robot capability ontology information, comprising: The capability set module is configured to acquire at least one capability information of the target robot; The capability information corresponds to at least one capability dimension; The basic component module is configured to acquire information on multiple basic components of the target robot; The basic component information includes at least: basic component identifier, basic component type, and basic component parameter set; the target robot is composed of multiple basic components. The capability component module is configured to create capability components based on the capability information; there is a mapping relationship between the capability components and the basic component information. The capability assessment module is configured to generate capability parameter values ​​corresponding to the capability dimension based on the basic component parameter set and the mapping relationship; The capability information output module is configured to generate capability ontology information of the target robot based on the capability parameter values ​​corresponding to each capability dimension.

11. An electronic device, comprising: One or more processors; A storage device configured to store one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 9.

12. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 9.

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