Dynamic use of artificial intelligence models on AI-enabled autonomous robotic devices

Abstract

Using artificial intelligence (AI) to dynamically adjust sensors and models of an autonomous roving robot device includes receiving, at a computer of the roving robot device, data about an asset from sensors on the robot device. The robot device uses the sensors to identify the asset at a location, and the robot device has received instructions from a control system to inspect the location or an item at the location. The computer of the robot device analyzes the data, and the analysis includes using historical data of the asset. Based on the identification of the asset, the computer of the robot device loads an AI model. The computer of the robot device selects sensors to implement an inspection of the asset based on the analysis of the data and the AI model.

Description

Technical Field

[0001] This disclosure relates to the use of AI models on artificial intelligence (AI) enabled autonomous roaming robotic devices, and more specifically, to the use of AI for adaptive sensing and model selection of roaming edge devices. Background Technology

[0002] In one example, a human, such as an operator, can record or define the robot's duties, and the robot can perform those duties. In another example, roaming edge devices (REDs) (e.g., robotic devices) can be used to navigate a location and complete assigned tasks. In yet another example, the device can assist with inspection or maintenance in manufacturing. Specific duties can be assigned to the device to perform inspections; for example, it can be pre-programmed to perform the same type of inspection on the same type of asset at a specific location. For instance, the first duty might involve a first robot performing a visual inspection of multiple fire extinguishers, and the second duty might involve a second robot investigating the sound from a heating and air conditioning (HVAC) system on the ground. In this example, even though the number of fire extinguishers and the HVAC system are on the same floor, the robot still needs to make two trips. Therefore, the robot performs pre-programmed duties and typically does not change the duties or make decisions at the robotic device's location. Thus, one problem with current robotic device systems is the inefficiency associated with the robot's tasks or duties. Summary of the Invention

[0003] This disclosure recognizes the drawbacks and problems associated with current technologies for enabling adaptive navigation to enable artificial intelligence (AI) in order to enhance the task performance of autonomous roaming robotic devices, and more specifically, to optimize the sensing capabilities of mobile robotic devices (e.g., roaming edge devices (RED)).

[0004] In one example, current robotic devices do not provide the flexibility for robots equipped with multiple sensors to perform multiple AI workloads within the same duty or stroke. The method and system of the present invention allow the robot to dynamically switch to using different sensors and run different AI workloads as needed. According to the present invention, the robotic device can determine which sensors it uses and make inferences using the corresponding AI models, i.e., determine the sensors. Therefore, the present invention enables the robot to use AI to intelligently determine which sensors it uses, i.e., determine the sensors to use, activate the selected sensors, and receive data from the selected sensors at the robotic device.

[0005] In one aspect of the invention, a computer-implemented method is provided for an autonomous roaming robotic device to dynamically adjust actions and a model for those actions using artificial intelligence (AI). The method includes: in response to the identification of an asset at a location using one or more sensors from a plurality of sensors on the roaming robotic device, receiving data about the asset from the one or more sensors at a computer of the robotic device. The robotic device has instructions received from a control system for inspecting the location or an item at the location. The method includes using the computer of the robotic device to analyze the received data and historical data for the asset, referencing relevant inspection instructions and inspection history for the asset. The method includes using the computer of the robotic device to select sensors from the plurality of sensors based on the analysis of the data. The method includes using the computer of the robotic device to load an AI model based on the identification of the asset and the analysis of the data, and performing an asset inspection based on the AI ​​model using the selected sensors to generate asset inspection data.

[0006] In related aspects, the method also includes analyzing the applicability of multiple AI models to the asset; selecting an AI model from multiple models based on asset identification, data analysis, and analysis of the multiple AI models; loading the selected AI model using a computer on a robotic device; and performing asset inspection using the selected AI model and selected sensors.

[0007] In related aspects, AI models may include inspection protocols for assets.

[0008] In related aspects, the method also includes using a computer on the robotic device to select a sensor from a plurality of sensors for performing an asset inspection based on an AI model; and using the selected sensor to perform the asset inspection based on the AI ​​model for the asset to generate asset inspection data.

[0009] In related aspects, the method may include determining an action for an asset based on asset inspection data; and communicating that action to a control system.

[0010] In related aspects, the inspection protocol may include the inspection type and the inspection actions specific to that inspection type.

[0011] In related aspects, the method may include using a computer to analyze asset inspection data from an asset inspection using robotic equipment; and determining the next action based on the analysis of the asset inspection data.

[0012] In related aspects, the method may also include initiating a next action at the robotic device, the next action including a second sensor among a plurality of sensors on the robotic device.

[0013] In related aspects, the method also includes enabling a third sensor among multiple sensors based on the asset type, loading relevant AI model input for the asset type for the generation of the AI ​​model; capturing inspection data with the third sensor; analyzing the inspection data to determine another action based on the analysis of the asset inspection data; and initiating the next action at the robotic device, the next action including a second sensor among multiple sensors on the robotic device.

[0014] In related aspects, the method also includes identifying multiple assets for inspection by a robotic device; identifying an AI model corresponding to each of the multiple identified assets from multiple AI models; receiving data for each of the assets respectively; analyzing the received data for each of the assets respectively; selecting sensors for each of the assets respectively; loading the AI ​​model for each of the assets respectively; and performing asset inspection for each of the assets respectively.

[0015] In this regard, the selection of sensors is performed by the computer of the robotic device without any instructions from the control system.

[0016] In this regard, the computer of the robotic device initiates the implementation of asset inspection based on the inspection protocol derived from the AI ​​model, and the implementation of asset inspection by the robotic device is in response to the computer of the robotic device.

[0017] In this context, the computer of the robotic device responds to the AI ​​model and initiates the execution of asset checks.

[0018] In related aspects, the selection of sensors and the implementation of asset checks are not initiated in response to the control system.

[0019] In a related aspect, the method further includes using one or more sensors from a plurality of sensors on a roaming robotic device to identify a second asset at a location; in response to the identification of the second asset at the location, receiving data about the second asset from the one or more sensors at a computer on the robotic device; analyzing the second data using the computer, wherein the analysis includes using historical data of the second asset; using the analysis of the second data to generate another AI model based on the identification of the second asset; analyzing the other AI model to generate a second inspection protocol for the second asset; selecting a second sensor from a plurality of sensors based on the second inspection protocol to initiate an inspection of the second asset; performing the second asset inspection using the selected second sensor to generate second asset inspection data; determining a second action for the second asset based on the second asset inspection data; and transmitting the second action to a control system.

[0020] In related aspects, this action includes alerts regarding assets.

[0021] In related aspects, this action includes communication with a person's device.

[0022] In related aspects, this action includes asset maintenance activities.

[0023] In another aspect of the invention, a system that uses a computer of an autonomous roaming robot device to dynamically adjust sensors and models for performing actions using artificial intelligence (AI) may include a computer system. The computer system may include a computer processor, a computer-readable storage medium, and program instructions stored on the computer-readable storage medium, the program instructions being executable by the processor to cause the computer system to perform the following functions: in response to the identification of an asset at a location using one or more of a plurality of sensors on the roaming robot device, receiving data about the asset from one or more sensors at the computer of the robot device, the robot device having instructions received from a control system for inspecting the location or an item at the location; using the computer of the robot device to analyze the received data and historical data for the asset, referencing relevant inspection instructions and inspection history for the asset; using the computer of the robot device to select a sensor from a plurality of sensors based on the analysis of the data; using the computer of the robot device to load an AI model based on the identification of the asset and the analysis of the data; and using the selected sensors to perform an asset inspection based on the AI ​​model to generate asset inspection data.

[0024] According to another aspect of the invention, a computer program product for an autonomous roaming robot device dynamically adjusts sensors and models to perform actions using artificial intelligence (AI). The computer program product may include a computer-readable storage medium having program instructions embodied therein. These program instructions can be executed by a computer to perform functions, including: receiving data about the asset from one or more sensors at a location in response to the identification of an asset using one or more of a plurality of sensors on the roaming robot device; the robot device having instructions received from a control system for inspecting the location or an item at the location; analyzing the received data and historical data for the asset using the robot device's computer, referencing relevant inspection instructions and inspection history for the asset; selecting sensors from a plurality of sensors based on the data analysis using the robot device's computer; loading an AI model using the robot device's computer based on the asset identification and data analysis; and performing asset inspection using the selected sensors based on the AI ​​model to generate asset inspection data. Attached Figure Description

[0025] These and other objects, features, and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is taken in conjunction with the accompanying drawings. For clarity, the various features in the drawings are not illustrated to scale and are intended to assist those skilled in the art in understanding the invention in conjunction with the detailed description. The drawings will now be discussed.

[0026] Figure 1 This is a schematic block diagram illustrating an overview of a system, system features or components, and method for an autonomous roaming robot device dynamically adjusting sensors and models to perform actions using artificial intelligence (AI) according to embodiments of the present disclosure.

[0027] Figure 2 This illustrates the use according to embodiments of the present disclosure. Figure 1 The flowchart shown illustrates a method for dynamically adjusting sensors and models to perform actions using AI, including artificial intelligence (AI) models and data analysis, in order to enable autonomous roaming robotic devices.

[0028] Figure 3 Therefore, this explains and Figure 1 and Figure 2 The functional features of this disclosure associated with the embodiments shown are illustrated for illustrative purposes and can be at least partially related to... Figure 1 The diagram shown is a functional block diagram illustrating a series of operations and functional methods for the coordinated implementation of a system for autonomous roaming robot devices to dynamically adjust sensors and models to perform actions using AI, including artificial intelligence (AI) models and data analysis.

[0029] Figure 4A This is a flowchart illustrating another method for dynamically adjusting sensors and models for autonomous roaming robotic devices to achieve actions using AI, including artificial intelligence (AI) models and data analysis. This other method... Figure 2 The flowchart continues.

[0030] Figure 4B This is a flowchart illustrating another method for dynamically adjusting sensors and models for autonomous roaming robotic devices to achieve actions using AI, including artificial intelligence (AI) models and data analysis. This other method... Figure 2 The flowchart continues.

[0031] Figure 5 This is a flowchart illustrating another method for dynamically adjusting sensors and models for an autonomous roaming robot device according to an embodiment of the present invention to perform actions using AI including artificial intelligence (AI) models and data analysis.

[0032] Figure 6This is a block diagram illustrating another system for an autonomous roaming robot device to dynamically adjust sensors and models to perform actions using AI, including artificial intelligence (AI) models and data analysis, according to an embodiment of the present invention.

[0033] Figure 7 This is a block diagram illustrating another system for an autonomous roaming robot device to dynamically adjust sensors and models to perform actions using AI, including artificial intelligence (AI) models and data analysis, according to an embodiment of the present invention.

[0034] Figure 8 This illustrates that embodiments according to this disclosure can be incorporated, in whole or in part. Figure 1 A schematic block diagram of a computer system in one or more computers or devices and cooperating with the systems and methods at the facilities shown in the figure.

[0035] Figure 9 This is a schematic block diagram depicting system components interconnected using a bus. These components are used, in whole or in part, with embodiments of this disclosure according to one or more embodiments.

[0036] Figure 10 This is a block diagram depicting a cloud computing environment according to an embodiment of the present invention.

[0037] Figure 11 This is a block diagram depicting an abstract model layer according to an embodiment of the present invention. Detailed Implementation

[0038] The following description, provided with reference to the accompanying drawings, is intended to aid in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. This description includes various specific details to aid understanding; however, these details should be considered merely exemplary, and the description aims to provide clarity and conciseness. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Additionally, descriptions of well-known functions and structures may be omitted.

[0039] The terms and words used in the following description and claims are not limited to their bibliographical meaning, but are merely used to enable a clear and consistent understanding of the invention. Therefore, it will be apparent to those skilled in the art that the following description of exemplary embodiments of the invention is for illustrative purposes only and is not intended to limit the invention as defined by the appended claims and their equivalents.

[0040] It should be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly indicates otherwise. Thus, for example, unless the context clearly indicates otherwise, “component surface” includes a reference to one or more of such surfaces.

[0041] Examples and Implementations

[0042] The embodiments and drawings of this disclosure may have the same or similar components as other embodiments. These drawings and descriptions illustrate and explain further examples and embodiments according to this disclosure.

[0043] refer to Figure 1 , Figure 2 and Figure 3 According to embodiments of this disclosure, a computer-implemented method 200 for optimizing the sensing capabilities of a roaming robot device (or robot) using artificial intelligence (AI) includes the features described below. Embodiments of this disclosure include operational actions and / or processes. The computer-implemented method 200 includes a series of operation blocks for implementing embodiments of this disclosure, which may include... Figure 1 The system shown is described. The operational framework of the method and system according to this disclosure may include techniques, mechanisms, modules, etc., for implementing the functions according to this disclosure.

[0044] Method 200 includes using one or more sensors 144 of a plurality of sensors on a roaming robotic device 148 to identify an asset 150 at location 140, as shown in box 204. The robotic device may include a computer 131 having AI capabilities for generating computer models, and the robotic device has instructions 308 received from a control system 170 in communication with the robot for inspecting the location or an item at the location.

[0045] Method 200 includes receiving data 312 about asset 150 from one or more sensors 144 of a plurality of sensors on the roaming robot device 148 at a computer 131 of the roaming robot device 148 in response to identification 304 of asset 150 at a location using one or more sensors, as shown in box 208.

[0046] The method includes using a computer analysis 340 of the robotic device to analyze received data 312 and historical data 314 for the asset, referencing relevant inspection instructions and inspection history for the asset, as shown in box 212. Such historical data can be stored in a knowledge base 196.

[0047] The method includes using a computer on a robotic device to select sensor 144 from 344 sensors based on analysis of data, as shown in box 216.

[0048] The method includes loading an AI model 146 using a computer on a robotic device, as shown in box 220, based on asset identification and data analysis.

[0049] When the method determines that the model is unacceptable at box 224, the method returns to box 212. When the method determines that the model is acceptable at box 224, the method continues to box 228.

[0050] The method includes performing action 350, for example, as shown in box 228, performing an asset inspection based on an AI model using selected sensors to generate asset inspection data 352. In one example, the method can transmit the asset inspection data to a control system 354.

[0051] refer to Figure 4A In another embodiment according to this disclosure, method 400 from Figure 2 The method 200 continues at box 220, including analyzing the suitability of multiple AI models for the asset, as shown in box 404. The method includes selecting an AI model from among multiple models based on asset identification and analysis of data and the analysis of the multiple AI models, as shown in box 406. The method includes loading the selected AI model using a computer on a robotic device, as shown in box 408, and implementing an asset inspection using the selected AI model and selected sensors, as shown in box 410. The method continues to box 212 of method 200.

[0052] In another example, the AI ​​model includes an inspection protocol for the asset. In yet another example, the method may include using a computer on the robotic device to select sensors from a plurality of sensors for performing asset inspection based on the AI ​​model, and using the selected sensors to perform the asset inspection based on the AI ​​model for the asset to generate asset inspection data. In yet another example, the method may include determining an action for the asset based on the asset inspection data, and communicating that action to a control system. In yet another example, the inspection protocol includes an inspection type and inspection actions specific to that type. In yet another example, the method includes loading relevant AI model inputs for the identified asset to generate an AI model or simulation for use by the robot.

[0053] In another example, the method includes using a computer on a robotic device to analyze asset inspection data from an asset inspection; and determining a next action based on the analysis of the asset inspection data. The method may include initiating the next action at the robotic device, and the next action may include a second sensor among a plurality of sensors on the robotic device. In one example, asset inspection data is analyzed from the asset inspection, and a next action is determined based on the analysis of the asset inspection data, and the next action is communicated to a control system.

[0054] In another example, the method includes enabling a third sensor among multiple sensors based on the asset type. The method may include loading relevant AI model inputs for the asset type to generate an AI model, and utilizing the third sensor to capture inspection data. The method may include analyzing the inspection data to determine another action based on the analysis of the asset inspection data. The method may include initiating a next action at a robotic device, which includes a second sensor among multiple sensors on the robotic device.

[0055] refer to Figure 4B In another method 450 according to this disclosure, method 450 from Figure 2 The method 200 continues from box 204, and the method includes identifying multiple assets for inspection by a robotic device, as shown in box 454. Method 450 includes identifying an AI model from a plurality of AI models corresponding to each of the multiple identified assets, as shown in box 456. Method 450 includes receiving data for each of the assets, as shown in box 458, and analyzing the received data for each of the assets, as shown in box 460. The method includes selecting sensors for each of the assets, as shown in box 462. The method also includes loading AI models for each of the assets, as shown in box 464, and performing asset inspections for each of the assets, as shown in box 466.

[0056] This method may include having the selection of sensors performed by the computer of the robotic device without any instructions from the control system.

[0057] The method may include the robot device’s computer initiating the implementation of an asset inspection based on an inspection protocol derived from an AI model, and the robot device’s implementation of the asset inspection is in response to the robot device’s computer.

[0058] This method may include the execution of an asset inspection initiated by a computer on a robotic device in response to an AI model. The method may also include the selection of sensors and the execution of asset inspections not yet initiated in response to a control system.

[0059] The method may further include using one or more sensors from a plurality of sensors on the roaming robot device to identify a second asset at a location. In response to the identification of the second asset at the location, the method may receive data about the second asset from the one or more sensors at a computer on the robot device. The method may analyze the second data using the computer, wherein the analysis includes historical data using the second asset. The method may use the analysis of the second data to generate another AI model based on the identification of the second asset. The method may analyze the other AI model to generate a second inspection protocol for the second asset. The method may select a second sensor from a plurality of sensors based on the second inspection protocol to initiate an inspection of the second asset. The method may use the selected second sensor to perform the second asset inspection to generate second asset inspection data. The method may determine a second action for the second asset based on the second asset inspection data and communicate the second action to a control system.

[0060] In one example, the action includes an alert about the asset. In another example, the action includes communication to a person's device. In yet another example, the action includes maintenance activities for the asset.

[0061] Computer 131 can be integrated with or communicate with robot device 148 in device 130. Computer 190, located remotely from device 148, can communicate electronically, wholly or partially, with computer 172, which is part of control system 170. Control system 172 may include computer 172 having a computer-readable storage medium 173 capable of storing one or more programs 174, and a processor 175 for executing program instructions. Control system 172 may also include a storage medium that may include registration and / or account data 182 and profile 183 of a user or entity (such entity may include robot entities) as part of user account 181. User account 181 may be stored on storage medium 180, which is part of control system 170. User account 181 may include registration and account data 182 and user profile 183. Control system 172 may also include computer 172 having a computer-readable storage medium 173 that may store programs or code embedded on the storage medium. Program code may be executed by processor 175. Computer 172 may communicate with database 176. The control system 170 may also include a database 176 for storing all or part of the data described above, as well as other data.

[0062] The control system can also communicate with computer system 190, which may include a learning engine / module 192 and a knowledge base or database 196. Computer system 190 can also communicate with computer 131 of device 130 and can be located remotely from user device 130. In another example, computer system 190 may be all or part of the control system, or all or part of device 130. The depiction of computer system 190 and other components of system 100 is shown as an example according to this disclosure.

[0063] A new or different AI (artificial intelligence) ecosystem, or technology / communication or IT (information technology) ecosystem, may include a local communication network 152 that can communicate with communication network 160. System 100 may include a learning engine / module 192, which may be at least part of or communicate with the control system, for generating or learning models. In one example, the learning model may model the workflow of machines / devices in the new AI or IT ecosystem.

[0064] In another example, computer 131 may be part of device 130. The computer may include processor 132 and computer-readable storage medium 134 that can store application 135, which in one example may embody all or part of the methods of this disclosure. The application may include all or part of instructions for implementing the methods of this disclosure, embodied in code and stored on the computer-readable storage medium. Device 148 may include a display. Device 148 may operate in conjunction with a remote server, wholly or partially, via a communication network 160 (e.g., the Internet).

[0065] The method includes, in response to receiving data from a robotic device at a location, using a control system to analyze the received data 340 to determine when the received data meets a threshold 344 for determining the quality 346 of the data, as shown in box 208.

[0066] The method includes analysis that generates a model 346 based on received data. Model 346 includes vector representations of inputs detected at locations by sensor array 144, which are at least a portion of the received data, as shown in box 212. The model can also be generated by an AI system, such as the output of an AI system that at least partially utilizes machine learning for analysis.

[0067] The method includes, in response to data received at the control system not meeting a threshold for determining quality, the robotic device using cloud communication 150 to communicate with a cloud server, such as a remote server 152, to collaborate in selecting a navigation action 354 when updating the strategy 350, the cloud server communicating with the robot at that location, as shown in box 220.

[0068] In another example, compared to a control system, a cloud server that can communicate with or is communicating with the robot may have increased communication capabilities with the robot at that location. In yet another example, the method may include receiving data from the robot at the cloud system; and the data received at the cloud system meeting a threshold used to determine the quality of the data.

[0069] In one example, as part of the analysis of the received data, the received data includes data from a knowledge base and a historical database 196, which may be populated with historical data collected from sensors, mobile robots, and machines.

[0070] In one example, the robotic device may be a roaming edge device (RED) at the edge of communication with a roaming edge device located at a position. In another example, the robotic device may be at least semi-autonomous, and the robotic device may perform a task initiated by the robotic device without task-specific communication from a control system.

[0071] In another example, the task can be initiated by the robotic device and may involve the robotic device's local computer communicating and collaborating with a cloud server to generate an updated strategy. The robotic device's local computer can initiate the task based on the updated strategy, and the robotic device can execute the task in response to the robotic device's local computer.

[0072] In another example, the robot's local computer may initiate a task using AI analysis of data provided by a sensor array at the location, and the sensor array may include environmental data at the location. In another example, the task may not be initiated in response to a control system. In another example, the environmental data may include environmental conditions. In another example, the threshold used to determine quality includes a measured level of electronic noise acceptable for quality, and thus, received data is considered unacceptable relative to quality when it exceeds the threshold. In another example, the location where the robot is operating may have low-quality electronic wireless communication capabilities, such as suboptimal electronic wireless communication capabilities.

[0073] In another example, compared to a control system, a cloud server can have increased communication capabilities with the robot at a location.

[0074] In another example, the method includes receiving data from the robot at a cloud system, and the data received at the cloud system meets a threshold used to determine the quality of the data.

[0075] refer to Figure 4A In another embodiment according to this disclosure, method 400 can be derived from... Figure 2 Block 220 of method 200 continues, and method 400 includes a task initiated by a robotic device, including communication and collaboration between the robotic device's local computer and a cloud server to generate an updated strategy, as shown in block 404.

[0076] Method 400 includes initiating a task based on an updated policy using a computer local to the robot device, as shown in box 406. The method also includes the robot device performing the task in response to the computer local to the robot device, as shown in box 408.

[0077] refer to Figure 4B In another embodiment according to this disclosure, method 450 can be derived from... Figure 2 Continuing from box 220 of method 200, method 450 includes updating the model using a computer, the updated model including updated received data, as shown in box 454. Method 450 includes analysis of the updated data, as shown in box 456. Next, method 450 includes collaborating with a cloud server in response to an update of the strategy for selecting navigation actions in response to the updated received data not meeting a threshold used to determine quality, as shown in box 458. The method may also include iteratively updating the updated notification based on changes in parameters detected by an event.

[0078] Other embodiments and examples

[0079] refer to Figure 1 Device 130, also referred to as a user device or administrator device, includes a computer 131 having a processor 132 and a storage medium 134 capable of storing application 135. The features of the methods disclosed herein can be embodied in the specification. A user can use device 130 to connect to learning engine 150. Device 130 includes computer 131 and a display or monitor 138. Application 135 can embody the methods disclosed herein and can be stored on computer-readable storage medium 134. Device 130 may also include a processor 132 for executing application / software 135. Device 130 can communicate with a communication network 160 (e.g., the Internet).

[0080] It should be understood that user equipment 130 represents a similar device that can be used by other users, as a representative of such a device, which may include mobile devices, smart devices, laptops, etc.

[0081] In one example, the system of this disclosure may include a control system 170 that communicates with user equipment 130 via a communication network 160. The control system may incorporate all or part of an application or software for implementing the methods of this disclosure. The control system may include a computer-readable storage medium 180, in which account data and / or registration data 182 may be stored. A user profile 183 may be part of the account data and stored on the storage medium 180. The control system may include a computer 172 having a computer-readable storage medium 173 and software program 174 stored therein. A processor 175 may be used to execute or implement instructions of the software program. The control system may also include a database 176.

[0082] In another example and embodiment, profiles can be maintained for entities such as users, participants, operators, human operators, or robotic devices. Such profiles can provide data about users and delivery history for analysis. In one example, a user can register or create an account using control system 170, which may include one or more profiles 183 as part of registration and / or account data 182. Registration may include a profile for each user with personalized data. For example, a user can register using a website via their computer and a GUI (Graphical User Interface). Registration or account data 182 may include a profile 183 for each user's account 181. Such accounts may be stored on control system 170, which may also store data using database 176. Users and associated accounts may refer to, for example, people, entities, corporate entities, corporate departments, or other machines such as entities used for automation (such as systems that use artificial intelligence fully or partially).

[0083] Additionally, refer to Figure 3 The methods and systems discussed herein include a functional system 300 comprising components and operations according to embodiments of the present disclosure, and are referred to in describing the operational steps of the methods and systems of the present disclosure. Furthermore, according to embodiments of the present disclosure, the functional system 300 is depicted as indicative of functional operations of the embodiments discussed herein.

[0084] refer to Figure 3 In one embodiment according to this disclosure, system 300 can be used to identify objects related to an event for use regarding the event by using networked computer system resources. Figure 3 In this context, similar components can have the same characteristics as... Figure 1 The same reference numerals are shown for system 100, and system 300 may include, for example, Figure 1 and Figure 2 The computer-implemented method shown, or its coordinated operation.

[0085] More embodiments and examples

[0086] Overall reference Figure 5 In one embodiment of this disclosure, system and method 500 can provide flexibility for a robotic device (also referred to as a robot) equipped with multiple sensors to perform multiple AI workloads within the same travel period. This disclosure may include a method that allows the robot to dynamically switch to use different sensors and run different AI workloads as needed. The robot can determine for itself which sensors to use and the corresponding AI models to make inferences (e.g., processes, instructions). Thus, the methods and systems of this disclosure may include using AI-enabled robotic devices to perform duties using different sensors and associated AI models, providing an intelligent robot capable of determining which sensors it uses to dynamically adapt to a target-rich environment (e.g., assets or objects). Therefore, the robot can dynamically adapt to a target-rich environment and is not limited to pre-programmed duties or tasks with a limited range of data capture.

[0087] In embodiments of this disclosure, the robot can freely perform tasks autonomously without any predefined responsibilities, such as locally determining the process of actions and / or determining the robot's actions when detecting and collecting information, and initiating the process of actions. Therefore, the robot is autonomous. In one example, the robot has the ability to detect nearby objects and can identify whether an object is an asset / equipment requiring maintenance by querying historical data from the asset catalog, and determine what type of inspection was previously performed. The robot can perform appropriate follow-up actions based on the inspection results. Therefore, no human operator is required to tell the robot which responsibility to perform.

[0088] In one example, the robot has the ability to load and run multiple AI models on its own without any predefined duties from human operators.

[0089] In another example, embodiments of this disclosure may include a robotic device comprising multiple sensors mounted on the robot, such as a PTZ (pan, tilt, zoom) camera, an infrared camera, a LiDAR camera, a microphone, etc. A GPU (Graphics Processing Unit) payload server provides computing power. Multiple AI models, such as visual inspection models, acoustic models, thermal models, time series models, etc., may be loaded onto the payload server. Programs may be loaded onto the payload server to assist the robot in making decisions in real time, including which sensors to use and which AI models to run. The program may include components comprising: an inbound component for capturing data from the robot's sensors; a preprocessing component for processing any data if necessary before sending it to the AI ​​models; an AI payload component for containing various AI models and libraries required to run the models, such as visual inspection models, acoustic inspection models, thermal inspection models, or time series analysis; a post-processing component for processing the inference results from the AI ​​models in a desired format; and an outbound component for executing appropriate alerts or actions based on the inference results, such as sending an alert to a human. The coordination component provides basic operational logic to help the robot make decisions about which sensors and AI models to use.

[0090] For more specific reference Figure 5 In one example, the methods and systems according to this disclosure may include robots or robotic devices gantrying on a production floor. Method 500 includes the robot performing random walks or moving on the production floor, as shown in box 504.

[0091] Figure 5 The method 500 shown includes a robot scanning assets in an area surrounding the production site, as shown in box 508. For example, the robot may take photographs using a camera while on the production site, as shown in box 508. The robot may also walk or move by means of other methods, such as rolling.

[0092] Method 500 may include the robot identifying objects in a surrounding area, as shown in box 512. In one example, the robot may be equipped with an object recognition model. Through object recognition, the robot identifies assets in the surrounding area. For example, a production site may include a power transformer, a fire extinguisher and HVAC unit, two humans (e.g., a human operator or manufacturing worker), a table, and two chairs. In another embodiment, the robot may detect its own position relative to a site map. If the site map includes the locations of assets, the robot can easily identify nearby assets without using an object recognition model. The robot may exclude humans because they are not assets / equipment.

[0093] This method involves locating objects in Enterprise Asset Management (EAM) software, as shown in box 516. For example, the robot can look up maintenance lists from the Enterprise Asset Management system.

[0094] The method includes determining in box 520 whether the asset is in the maintenance list. If not, the method can proceed by adding the asset to the review list, as shown in box 532, and then proceeding to notify the operator in box 542. When the asset is in the maintenance list at box 520, the method can look up past AI tasks in box 524. The method can then proceed by enabling the selected sensors (as shown in box 526) and loading the corresponding AI model(s)(s) in box 528.

[0095] For example, the robot can identify objects as assets (power transformers, fire extinguishers, and HVAC systems) from a maintenance list. For each asset near the robot, it can query historical data from the asset management system to know the type of inspection performed on the nearby asset. For example, visual inspections may have been performed on fire extinguishers, thermal inspections on transformers, and acoustic inspections on HVAC systems in the past.

[0096] This method may include performing AI tasks, as shown in box 536. In one example, the method includes the robot performing an AI-enabled task, such as an inspection. For example, for each asset near the robot, the robot performs the correct inspection based on historical inspections. In one example, the robot may enable appropriate sensors; if the appropriate AI model differs from the AI ​​model loaded onto the robot, the correct AI model is loaded at runtime, data is captured with the sensors, inference is performed on the selected AI model, the inference results of the inspection are captured, and an alert is generated if necessary.

[0097] In one example, when an asset is not listed in the maintenance list of the asset catalog (at box 520), the robot can add the asset to a temporary list, as shown in box 532, and the robot can later notify the human operator of these new assets (as shown in box 542).

[0098] Method 500 includes generating an action 540, which may include activating a sensor such as a camera and capturing data such as an image. When no next action is available at box 540, the method may notify a human operator 542. When a next action is available at box 540, the method may record the inference result, as shown in box 544.

[0099] For example, this method could include the robot switching to different sensors and AI models as needed until a nearby asset is inspected. In cases where different types of inspections are performed on the same asset—for example, HVAC systems have undergone visual inspections over the past three years and acoustic inspections over the past year—the robot can compare the results of different AI models and see which has better performance / accuracy. The robot will then select the sensor and AI model that performs better for the asset. The robot can continue moving, such as walking, and continue performing inspections of nearby assets until its battery needs to be recharged.

[0100] The method may include continuing to determine whether the battery level is acceptable, as shown in box 548. When the battery level is acceptable at box 548, the method may return to box 504. When the battery level is unacceptable at box 548, the robot may return to the charging station at box 550 to charge its battery.

[0101] Overall reference Figure 6 and Figure 7 The system 600 according to this disclosure includes a robot control system 604, which includes a robot 606 and a computer-readable storage medium 610. The robot 606 can use sensors or devices 608 to deliver payloads or collect data. The computer-readable storage medium 610 has software 612 embedded therein for executing programming instructions for the robot. The system 604 includes a graphics processing unit (GPU) 614 and a central processing unit (CPU) 616. The device 608 includes a robot arm 620, a camera 622, a LiDAR (light detection and ranging) device 624, a graphics system processor (GSP) 626, and a camera and an infrared (IR) / thermal imaging system. Infrared light can be used to illuminate the area of ​​interest.

[0102] Users 630 of the robotic equipment may include operators 632, production planners 634, safety operators 636, operations managers 638, or original equipment managers 640. Cognitive solutions using cognitive analytics enable system 600 to provide solutions using the components described herein. Enterprise system 650 includes asset management components 652, maintenance management components 654, and enterprise resource planning (ERP) components 656. System 600 includes automation systems 660 and external systems 664. Automation systems may include building automation systems (BAS), building management systems (BMD), or building information modeling (BIM). External systems may include weather systems or mapping systems.

[0103] The components of System 600 may also include an asset catalog maintained in an asset management system. For each asset, the management system may include historical data on the AI ​​pipeline / task being inspected, as well as tracking actions (such as the creation of alerts or work orders), which defines which sensors(s) and AI models(s) were used. The robot may be equipped with multiple sensors, a payload server, multiple AI models, and the intelligent edge solution described above. The AI ​​model training environment may include a development environment for data scientists to create different AI models before deploying different AI models at the edge (i.e., on the robot). Wi-Fi connectivity may allow the robot to query the asset management system for asset information and historical AI tasks. In another embodiment, the robot may be placed in a new facility to perform inspections autonomously without any predefined duties of a human operator, as it has the ability to infer the same type of assets to be applied to by the same type(s) of machine learning models(s) and sensors(s).

[0104] In another example according to this disclosure, the management system 700 includes an application manager 704, which can be located in a cloud computing environment 702, and includes a UI (user interface) 706, an Agbot (agricultural robot) 708, a switch 710, an MMS (Multimedia Messaging Service) 712, and an SDO (Standardization Organization for Standards) 714. A field engineer 720 can access the UI 706 to register the robot. A developer 728 can define policies and services for the robot. The developer can use a push container to record data 724 in a registry in a database. For example, the location 730 where the robot operates may include an application 740 stored on the robot 765, and an application that receives input from the application manager 704. The application includes an agent 742 and a service 744 that provides input to multiple processes. The application 740 includes inbound data 750, preprocessing 752, commands and control 754, an analytics model 756, postprocessing 758, and outbound data 760.

[0105] More examples and embodiments

[0106] Therefore, according to the embodiments and examples described herein, the methods and systems of this disclosure may include switching AI models if two or more different types of asset inspections are performed. At a higher level, the robot may receive instructions to survey assets in an area, and the robot may identify a first asset and look up historical data on what inspections were performed. Based on the historical information, the robot may activate one of multiple sensors and select an AI model to perform the inspection using the selected sensor, and complete the inspection. The robot may continue to identify other assets or a second asset in the area. Asset identification can be accomplished by running an object detection AI model. The robot may look up historical data on the second asset. The robot may activate one or more sensors and select a second AI model if the type of inspection performed on the second asset is different from the type of inspection performed on the first asset. The robot may use the selected second AI model and the selected sensor to perform the inspection. The robot continues to scan assets in the area and perform inspections until all assets in the area have been inspected. In one example, the switching of AI models may be performed on the robot's computer, or it may be performed on an external server with which the robot communicates.

[0107] The operation blocks and system components shown in one or more of the accompanying drawings may be similar to those in other drawings. The diversity of operation blocks and system components depicts exemplary embodiments and aspects according to this disclosure. For example, the methods shown are intended as exemplary embodiments that may include aspects / operations previously shown and discussed in this disclosure, and in one example, continue from a previous method shown in another flowchart.

[0108] Additional Examples and Implementation Examples

[0109] exist Figure 1 and Figure 2 In the embodiments of this disclosure shown, the computer may be a remote computer or part of a remote server, such as remote server 1100. Figure 8 In another example, computer 131 may be part of control system 170 and provide the execution of the functions of this disclosure. In another embodiment, computer may be part of mobile device and provide the execution of the functions of this disclosure. In yet another embodiment, the portion of the execution of the functions of this disclosure may be shared between the control system computer and the mobile device computer; for example, the control system may serve as the backend of one or more programs embodying this disclosure, and the mobile device computer may serve as the frontend of one or more programs.

[0110] The computer may be part of a mobile device or a remote computer communicating with the mobile device. In another example, the mobile device and the remote computer may work together to perform features of one or more methods described herein using stored program code or instructions, thereby implementing the methods of this disclosure. In one example, device 130 may include a computer 131 having a processor 132 and a storage medium 134 storing an application 135, and the computer includes a display 138. The application may incorporate program instructions for performing features of this disclosure using the processor 132. In another example, the mobile device application or computer software may have program instructions executable by a front-end of the software application, incorporating features of the methods of this disclosure in the program instructions, while one or more back-end programs 174 of the software application stored on the computer 172 of the control system 170 communicate with the mobile device computer and perform other features of the method. The control system 170 and device (e.g., mobile device or computer) 130 may communicate using a communication network 160 (e.g., the Internet).

[0111] Therefore, the method 100 according to embodiments of this disclosure can be incorporated into one or more computer programs or applications 135 stored on electronic storage medium 134, and can be executed by processor 132 as part of a computer on a mobile device. For example, the mobile device can communicate with control system 170, and in another example, a device such as a video feed device can communicate directly with control system 170. Other users (not shown) may have similar mobile devices that similarly communicate with the control system. Applications can be stored wholly or partially on a computer or mobile device, and also on a control system that communicates with the mobile device, for example, using a communication network 160 such as the Internet. It is conceivable that applications can access all or part of the program instructions to implement the methods of this disclosure. Programs or applications can communicate with and access data to remote computer systems via communication network 160 (e.g., the Internet), and cooperate with one or more programs stored on the remote computer system. Such interactions and mechanisms are described in more detail herein and relate to components of a computer system, such as computer-readable storage media, which... Figure 8 One embodiment is shown and described in more detail with reference to one or more computer systems 1010.

[0112] Therefore, in one example, the control system 170 communicates with the computer 131 or device 130, and the computer may include an application or software 135. The computer in the computer 131 or device 130 communicates with the control system 170 using a communication network 160.

[0113] In another example, the control system 170 may have a front-end computer belonging to one or more users and a back-end computer embodied as a control system.

[0114] In addition, refer to Figure 1 Device 130 may include computer 131, computer-readable storage medium 134, operating system and / or programs, and / or software applications 135, which may include program instructions executable using processor 132. These features are described herein. Figure 1 As shown in [the document], and other similar components and features are also [present / exhibited]. Figure 8 In the embodiment of the computer system shown, Figure 8 It relates to a computer system 1010 that may include one or more computer components.

[0115] The method according to this disclosure may include a computer as part of a control system for implementing the features of the method according to this disclosure. In another example, the computer as part of the control system may work in conjunction with a mobile device computer communication system to implement the features of the method according to this disclosure. In yet another example, the computer for implementing the features of the method may be part of a mobile device and thus implement the method locally.

[0116] Specifically, regarding control system 170, one or more devices 130, or in one example, devices belonging to one or more users, can communicate with control system 170 via communication network 160. Figure 1 In the embodiment of the control system shown, the control system 170 includes a computer 172 communicating with a database 176 and one or more programs 174 stored on a computer-readable storage medium 173. Figure 1 In the embodiments of this disclosure shown, device 130 communicates with control system 170 and one or more programs 174 stored on computer-readable storage medium 173. The control system includes computer 172 having processor 175, which also has access to database 176.

[0117] Control system 170 may include storage medium 180 for maintaining user and device registration 182 for analyzing audio input. Such registration may include user profiles 183, which may include user data provided by the user for reference in registering and creating an account. In one embodiment, the methods and systems incorporated herein include a control system (generally referred to as a backend) combined with and cooperating with a frontend of the methods and systems, which may be application 135. In one example, application 135 is stored on a device, such as a computer or device at a location, and has access to data and additional programs at the application backend (e.g., control system 170).

[0118] The control system may also be part of a software application implementation, and / or represent a software application having a front-end user portion and a back-end portion that provide functionality. In one embodiment, the methods and systems incorporating this disclosure include a control system (which may generally be referred to as the back-end of a software application incorporating a portion of the methods and systems of this application), which is combined and cooperates at the device with the front-end of a software application incorporating another portion of the methods and systems of this application, such as in Figure 1 The example shown is of device 130 and computer 131 with application 135. Application 135 is stored on the device or computer and can access data and additional programs at the back end of the application, such as in program(s) 174 stored in control system 170.

[0119] One or more programs 174 may include, in whole or in part, a series of executable steps for implementing the methods of this disclosure. Programs incorporating this method may be stored, in whole or in part, on a computer-readable storage medium on the control system, or in whole or in part on a computer or device 130. It is contemplated that the control system 170 may not only store user profiles, but in one embodiment, may interact with a website to be viewed on a display of a device such as a mobile device, or in another example, on the Internet, and receive user input related to the methods and systems of this disclosure. It is understood that... Figure 1 One or more profiles 183 are described; however, the method may include multiple profiles, users, registration, etc. It is conceivable that multiple users or a group of users can use the control system used by the method and system according to this disclosure to register and provide profiles.

[0120] Further embodiments and examples

[0121] It should be understood that the features shown in some of the accompanying drawings, such as block diagrams, are functional representations of the features of this disclosure. For illustrative purposes, such features are shown in embodiments of the systems and methods of this disclosure to illustrate the function of the features of this disclosure.

[0122] The methods and systems disclosed herein may include a series of operation blocks for implementing one or more embodiments of the present disclosure. In some examples, the operation blocks of one or more figures may be similar to the operation blocks shown in another figure. A method shown in one figure may be another example embodiment that may include aspects / operations shown in another figure and previously discussed.

[0123] Additional embodiments and examples

[0124] For example, account data, including user profile data, as well as any other personal or other data, may be collected and stored, for example, in a control system 170. It is understood that such data collection is conducted with the user's knowledge and consent and is stored to protect privacy, as will be discussed in more detail below. Such data may include personal data and data about personal belongings.

[0125] In one example, a user can register an account 181 with a user profile 183 on control system 170, which will be discussed in more detail below. For example, the technology described above can be used, such as using a camera to collect data, and the data can be uploaded by the user to the user profile. Users can include, for example, corporate entities, business units, homeowners, or any end-user, human operator, robotic equipment, or other personnel of the business.

[0126] Regarding the collection of data for this disclosure, such uploading or generation of profiles is voluntary and therefore initiated and approved by one or more users. Thus, users can opt in to create an account with a profile according to this disclosure. Similarly, data received by the system or entered or received as input is voluntary and therefore initiated and approved by one or more users. Thus, users can opt in to enter data according to this disclosure. Such user approval also includes the option for the user to cancel such a profile or account and / or entered data, and thus opt out of capturing communications and data at the user's discretion. Furthermore, any data stored or collected is understood to be intended to be securely stored and unavailable without user authorization, and unavailable to public and / or unauthorized users. Such stored data is understood to be deleted at the user's request and deleted in a secure manner. Furthermore, according to this disclosure, any use of such stored data is understood to be solely with the user's authorization and consent.

[0127] In one or more embodiments of the present invention, one or more users may choose to join or register with the control system, voluntarily providing data and / or information during the process with user consent and authorization, wherein the data is stored and used in one or more methods of this disclosure. Furthermore, one or more users may register one or more user electronic devices for use with one or more methods and systems according to this disclosure. As part of registration, users may also identify and authorize access to one or more activities or other systems (e.g., audio and / or video systems). Such optional joining and authorization of data collection and / or storage is voluntary, and users may request deletion of data (including profiles and / or profile data), unregister, and / or opt out of any registration. It is understood that such optional output includes handling all data in a secure manner. The user interface may also allow users or individuals to remove all of their historical data.

[0128] Other additional embodiments and examples

[0129] In one example, artificial intelligence (AI) may be used in whole or in part to generate models or learning models as discussed herein in the embodiments of this disclosure.

[0130] Artificial intelligence (AI) systems can include machines, computers, and computer programs designed to be intelligent or mirror-intelligent. Such systems can include computers that execute algorithms. AI can include machine learning and deep learning. For example, deep learning can include neural networks. AI systems can be cloud-based, that is, using a cloud-based computing environment with computing resources.

[0131] In another example, the control system 170 may be all or part of an artificial intelligence (AI) system. For example, the control system may be one or more components of an AI system.

[0132] It should also be understood that the method 100 according to embodiments of this disclosure can be incorporated into (artificial intelligence) AI devices, components, or can be part of an AI system that can communicate with corresponding AI systems and components and corresponding AI system platforms. Thus, as described above, such a program or application in conjunction with the methods of this disclosure can be part of an AI system. In one embodiment of the invention, it is conceivable that a control system can communicate with an AI system, or in another example, can be part of an AI system. The control system can also represent a software application having a front-end user portion and a back-end portion providing functionality, which in one or more examples can interact with, contain, or be part of a larger system such as an AI system. In one example, an AI device can be associated with an AI system, which can be wholly or partially a control system and / or a content delivery system, and is remote from the AI ​​device. Such an AI system can be represented by one or more servers that store programs on a computer-readable medium that can communicate with one or more AI devices. The AI ​​system can communicate with a control system, and in one or more embodiments, the control system can be wholly or partially of the AI ​​system, or vice versa.

[0133] It should be understood that, as discussed herein, downloads or downloadable data can be initiated using voice commands or by using a mouse, touchscreen, etc. In such examples, the mobile device may be user-initiated, or the AI ​​device may be used with the user's consent and permission. Other examples of AI devices include devices that include microphones, speakers, and access to cellular or mobile networks, communication networks, or the Internet, such as vehicles with computers and cellular or satellite communications, or, in another example, IoT (Internet of Things) devices with cellular or Internet access, such as appliances.

[0134] Further discussion of examples and embodiments

[0135] It is understood that a set or group is a collection of different objects or elements. The objects or elements constituting a set or group can be anything, such as numbers, letters of the alphabet, other sets, multiple people or users, etc. It should also be understood that a set or group can be a single element, such as a single thing or number; in other words, a collection of a single element, such as one or more users, people, or participants. It should also be understood that "machine" and "device" are used interchangeably in this document to refer to machines or devices within one or more AI ecosystems or environments.

[0136] Various embodiments of the invention have been described for illustrative purposes, but are not intended to be exhaustive or limited to the disclosed embodiments. Similarly, the examples of features or functions of the embodiments of this disclosure described herein, whether used in the description of specific embodiments or listed as examples, are not intended to limit the embodiments of this disclosure described herein, or to restrict the disclosure to the examples described herein. These examples are intended to be exemplary and not exhaustive. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein has been chosen to best explain the principles of the embodiments, their practical application, or improvements to existing technologies in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

[0137] Further additional examples and embodiments

[0138] refer to Figure 8Embodiments of the system or computer environment 1000 according to this disclosure include a computer system 1010 shown in the form of a general-purpose computing device. Method 100 may be implemented, for example, in program 1060, including program instructions embodied on a computer-readable storage device or computer-readable storage medium, such as generally referred to as computer memory 1030, and more specifically, as computer-readable storage medium 1050. Such memory and / or computer-readable storage medium includes non-volatile memory or non-volatile storage devices, also known and referred to as non-transitory computer-readable storage media, or non-transitory computer-readable storage medium. For example, such non-volatile memory may also be a disk storage device including one or more hard disk drives. For example, memory 1030 may include storage medium 1034 such as RAM (random access memory) or ROM (read-only memory), and cache memory 1038. Program 1060 may be executed by processor 1020 of computer system 1010 (to execute program steps, code, or program code). Additional data storage devices may also be embodied as database 1110 including data 1114. Computer system 1010 and program 1060 may be a general representation of a user's local computer and program, or provided as a remote service (e.g., as a cloud-based service), and may be provided using a website accessible through communication network 1200 (e.g., interacting with a network, the Internet, or cloud services), as further exemplified. It should be understood that computer system 1010 herein also generally refers to a computer device or a computer included in a device such as a laptop or desktop computer, or one or more servers, either alone or as part of a data center. The computer system may include network adapter / interface 1026 and one or more input / output (I / O) interfaces 1022. I / O interface 1022 allows input and output of data with external devices 1074 that can be connected to the computer system. Network adapter / interface 1026 can provide communication between the computer system and a network generally represented as communication network 1200.

[0139] Computer 1010 can be described in the general context of executable instructions of a computer system, such as program modules executed by the computer system. Typically, program modules can include routines, programs, objects, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. Method steps and system components and techniques can be implemented in modules of program 1060 that perform tasks for each step of the method and system. These modules are generally represented in the diagram as program modules 1064. Program 1060 and program modules 1064 can execute specific steps, routines, subroutines, instructions, or code of a program.

[0140] The methods disclosed herein can run locally on a device such as a mobile device, or as a service on a server 1100, which may be remote and accessible via the communication network 1200. The program or executable instructions may also be provided as a service by a provider. The computer 1010 can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked via the communication network 1200. In a distributed cloud computing environment, program modules can reside in local and remote computer system storage media, including memory storage devices.

[0141] More specifically, the system or computer environment 1000 includes a computer system 1010 shown in the form of a general-purpose computing device with illustrative peripherals. Components of the computer system 1010 may include, but are not limited to, one or more processors or processing units 1020, system memory 1030, and a bus 1014 that couples various system components, including system memory 1030, to the processor 1020.

[0142] Bus 1014 represents one or more of several types of bus architectures, including memory buses or memory controllers, peripheral buses, accelerated graphics ports, and processor or local buses using any of the various bus architectures. By way of example and not limitation, these architectures include Industry Standard Architecture (ISA) buses, Micro Channel Architecture (MCA) buses, Enhanced ISA (EISA) buses, Video Electronics Standards Association (VESA) local buses, and Peripheral Component Interconnect (PCI) buses.

[0143] Computer 1010 may include a variety of computer-readable media. Such media may be any available media accessible by computer 1010 (e.g., a computer system or server) and may include volatile and non-volatile media as well as removable and non-removable media. Computer memory 1030 may include additional computer-readable media in the form of volatile memory, such as random access memory (RAM) 1034 and / or cache memory 1038. Computer 1010 may also include other removable / non-removable, volatile / non-volatile computer storage media, such as portable computer-readable storage media 1072 in one example. In one embodiment, computer-readable storage media 1050 may be provided for reading from and writing to non-removable, non-volatile magnetic media. Computer-readable storage media 1050 may be implemented, for example, as a hard disk drive. Additional memory and data storage devices may be provided, for example, as a storage system 1110 (e.g., a database) for storing data 1114 and communicating with processing unit 1020. The database may be stored on server 1100 or as part of it. Although not shown, a disk drive for reading from and writing to a removable, non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM, or other optical media may be provided. In such an example, each may be connected to bus 1014 via one or more data media interfaces. As will be further described and illustrated below, memory 1030 may include at least one program product that may include one or more program modules configured to perform the functions of embodiments of the present invention.

[0144] For example, the methods(s) described in this disclosure may be embodied in one or more computer programs, generally referred to as program 1060, and may be stored in memory 1030 within computer-readable storage medium 1050. Program 1060 may include program module 1064. Program module 1064 may generally perform the functions and / or methods of embodiments of the invention as described herein. One or more programs 1060 are stored in memory 1030 and may be executed by processing unit 1020. As an example, memory 1030 may store operating system 1052, one or more application programs 1054, other program modules, and program data on computer-readable storage medium 1050. It will be understood that program 1060, operating system 1052, and one or more application programs 1054 stored on computer-readable storage medium 1050 may similarly be executed by processing unit 1020. It should also be understood that application 1054 and program(s) 1060 are generally shown and may include all or part of one or more applications and programs discussed in this disclosure, or vice versa, that is, application 1054 and program 1060 may be all or part of one or more applications or programs discussed in this disclosure. It should also be understood that control system 170 communicating with a computer system may include all or part of computer system 1010 and its components, and / or control system may communicate as a remote computer system with all or part of computer system 1010 and its components to implement the control system functions described in this disclosure. Control system functions may, for example, include storing, processing, and executing software instructions to perform the functions of this disclosure. It should also be understood that... Figure 1 The one or more computers or computer systems shown may similarly include all or part of computer system 1010 and its components, and / or one or more computers may communicate as remote computer systems with all or part of computer system 1010 and its components to perform the computer functions described in this disclosure.

[0145] In embodiments according to this disclosure, one or more programs may be stored in one or more computer-readable storage media, such that the program is embodied and / or encoded in the computer-readable storage media. In one example, the stored program may include program instructions for execution by a processor or a computer system having a processor to perform a method or cause the computer system to perform one or more functions. For example, in one embodiment according to this disclosure, a program embodying a method is embodied in or encoded in a computer-readable storage medium, which includes and is defined as a non-transitory or non-transient computer-readable storage medium. Therefore, embodiments or examples of computer-readable storage media according to this disclosure do not include signals, and embodiments may include one or more non-transitory or non-transient computer-readable storage media. Thus, in one example, a program may be recorded on a computer-readable storage medium and structurally and functionally associated with that medium.

[0146] Computer 1010 can also communicate with one or more external devices 1074, such as a keyboard, pointing device, or display 1080; one or more devices that enable a user to interact with computer 1010; and / or any device that enables computer 1010 to communicate with one or more other computing devices (e.g., network interface card, modem, etc.). This communication can occur via input / output (I / O) interface 1022. Power supply 1090 can also be connected to the computer using a power interface (not shown). Furthermore, computer 1010 can communicate with one or more networks 1200, such as a local area network (LAN), a general wide area network (WAN), and / or a public network (e.g., the Internet), via network adapter / interface 1026. As shown, network adapter 1026 communicates with other components of computer 1010 via bus 1014. It should be understood that, although not shown, other hardware and / or software components may be used in conjunction with computer 1010. Examples include, but are not limited to, microcode, device drivers 1024, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archive storage systems.

[0147] It should be understood that a computer or a program running on computer 1010 can communicate with a server embodied as server 1100 via one or more communication networks embodied as communication network 1200. Communication network 1200 may include transmission media and network links, including, for example, wireless, wired, or fiber optic connections, as well as routers, firewalls, switches, and gateway computers. Communication network may include connections such as wired, wireless communication links, or fiber optic cables. Communication network may represent a global collection of networks and gateways (e.g., the Internet) that communicate with each other using various protocols such as Lightweight Directory Access Protocol (LDAP), Transmission Control Protocol / Internet Protocol (TCP / IP), Hypertext Transfer Protocol (HTTP), Wireless Application Protocol (WAP), etc. Networks may also include many different types of networks, such as intranets, local area networks (LANs), or wide area networks (WANs).

[0148] In one example, the computer can use a network that can use the Internet to access websites on the Web (World Wide Web). In one embodiment, the computer 1010, including a mobile device, can use a communication system or network 1200, which may include the Internet or a Public Switched Telephone Network (PSTN), such as a cellular network. The PSTN may include telephone lines, fiber optic cables, microwave transmission links, cellular networks, and communication satellites. The Internet can facilitate many search and text messaging technologies, such as sending queries to search engines using a cellular phone or laptop via text messaging (SMS), multimedia messaging service (MMS) (as opposed to SMS), email, or a web browser. Search engines can retrieve search results, i.e., links to websites, documents, or other downloadable data corresponding to the query, and similarly, provide the search results to the user via the device as, for example, web pages containing the search results.

[0149] Further additional examples and embodiments

[0150] refer to Figure 9This document describes an example system 1500 for use with embodiments of the present disclosure. System 1500 includes multiple components and elements connected via a system bus 1504. At least one processor (CPU) 1510 is connected to other components via the system bus 1504. Cache memory 1570, read-only memory (ROM) 1512, random access memory (RAM) 1514, input / output (I / O) adapter 1520, sound adapter 1530, network adapter 1540, user interface adapter 1552, display adapter 1560, and display device 1562 are also operatively coupled to the system bus 1504 of system 1500. AR device 1580 is also operatively coupled to bus 1504. AI-enabled robotic device and control system 1580 is also operatively coupled to bus 1504. Such a robotic and control system 1580 may include all or part of the embodiments of the present disclosure and discussed above. Artificial intelligence (AI) system 1575 or AI ecosystem may also be operatively coupled to bus 1504. Power supply 1595 may also be operatively connected to bus 1504 for providing power to components and for functions according to this disclosure. Augmented reality (AR) device 1590 may also be operatively connected to bus 1504 for providing augmented reality output to wearable augmented reality devices such as AR glasses or AR headsets.

[0151] One or more storage devices 1522 are operatively coupled to system bus 1504 via I / O adapter 1520. Storage device 1522 may be, for example, any of the following: disk storage device (e.g., magnetic disk or optical disk storage device), solid-state magnetic device, etc. Storage device 1522 may be the same type of storage device or different types of storage devices. Storage devices may include, for example, but not limited to, hard disk drives or flash memory, and may be used to store one or more programs 1524 or applications 1526. Programs and applications are shown as generic components and may be executed using processor 1510. Program 1524 and / or application 1526 may include all or part of the programs or applications discussed in this disclosure, or vice versa, i.e., program 1524 and application 1526 may be part of other applications or programs discussed in this disclosure.

[0152] System 1500 may include control system 170, which is part of system 100 (described in more detail above) and may communicate with the system bus independently or as part of system 100, and thus may communicate with other components of system 1500 via the system bus. In one example, storage device 1522 may communicate with control system 170 having various functions as described in this disclosure via the system bus.

[0153] In one aspect, speaker 1532 is operatively coupled to system bus 1504 via sound adapter 1530. Transceiver 1542 is operatively connected to system bus 1504 via network adapter 1540. Display 1562 is operatively coupled to system bus 1504 via display adapter 1560.

[0154] On the other hand, one or more user input devices 1550 are operatively coupled to the system bus 1504 via a user interface adapter 1552. The user input device 1550 may be, for example, a keyboard, mouse, keypad, image capture device, motion sensing device, microphone, a device combining the functions of at least two of the aforementioned devices, etc. Other types of input devices may also be used while maintaining the spirit of the invention. The user input device 1550 may be of the same type or different types. The user input device 1550 is used to input information to and output information to the system 1500.

[0155] Other aspects and examples

[0156] This invention can be a system, method, and / or computer program product at any possible level of technical detail integration. The computer program product may include one or more computer-readable storage media having computer-readable program instructions thereon for causing a processor to perform aspects of the invention.

[0157] Computer-readable storage media can be tangible devices capable of retaining and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices such as punch cards or recessed structures with instructions recorded thereon, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0158] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a suitable computing / processing device, or via a network, such as the Internet, a local area network (LAN), a wide area network (WAN), and / or a wireless network, to an external computer or external storage device. The network may include copper cables, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to a computer-readable storage medium within the respective computing / processing device.

[0159] Computer-readable program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages ​​such as Smalltalk, C++, etc.) and procedural programming languages ​​(such as the "C" programming language or similar programming languages). The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of this invention, electronic circuits, including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may execute computer-readable program instructions to personalize the electronic circuits by utilizing the status information of the computer-readable program instructions.

[0160] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0161] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / actions specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other devices to operate in a particular manner, such that the computer-readable storage medium in which the instructions are stored includes an article of writing comprising instructions for implementing aspects of the functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0162] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer-implemented process, such that the instructions, which execute on the computer, other programmable apparatus or other device, perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0163] The flowcharts and block diagrams in the accompanying drawings of this disclosure illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions comprising one or more executable instructions for implementing a specified logical function. In some alternative embodiments, the functions indicated in the blocks may occur in a non-linear order as shown in the figures. For example, two blocks shown consecutively may actually be implemented as a single step, executed simultaneously, substantially simultaneously, with partial or complete time overlap, or these blocks may sometimes be executed in reverse order, depending on the functions involved. It will also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action or executes a combination of dedicated hardware and computer instructions.

[0164] Additional aspects and examples

[0165] It should be understood that although this disclosure includes a detailed description of cloud computing, the implementation of the teachings set forth herein is not limited to a cloud computing environment. Rather, embodiments of the invention can be implemented in conjunction with any other type of computing environment now known or developed hereafter.

[0166] Cloud computing is a service delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with service providers. This cloud model may include at least five features, at least three service models, and at least four deployment models.

[0167] The characteristics are as follows:

[0168] On-demand self-service: Cloud consumers can unilaterally and automatically provide computing power, such as server time and network storage, as needed, without requiring manual interaction with the service provider.

[0169] Wide Area Network (WAN) Access: Capabilities are available on the network and accessed through standard mechanisms that facilitate the use of heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0170] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically allocated and reallocated based on demand. Location independence has significance because consumers typically do not control or know the exact location of the resources provided, but can specify the location at a higher level of abstraction (e.g., country, state, or data center).

[0171] Rapid Flexibility: In some cases, the ability to scale outwards and inwards quickly and flexibly can be provided. For consumers, the available capacity often appears unlimited and can be purchased in any quantity at any time.

[0172] Measurement services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at a level of abstraction appropriate to the service type (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both the providers and consumers of the services being utilized.

[0173] The service model is as follows:

[0174] Software as a Service (SaaS): The capability offered to consumers is the ability to use the provider's applications running on cloud infrastructure. Applications can be accessed from a variety of client devices through a thin client interface such as a web browser (e.g., web-based email). Consumers do not manage or control the underlying cloud infrastructure, including the network, servers, operating system, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0175] Platform as a Service (PaaS): This provides consumers with the ability to deploy consumer-created or acquired applications onto cloud infrastructure using programming languages ​​and tools supported by the provider. Consumers do not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but they have control over the deployed applications and the configuration of any application hosting environments.

[0176] Infrastructure as a Service (IaaS): The capabilities offered to consumers are processing, storage, networking, and other basic computing resources that enable consumers to deploy and run arbitrary software, which may include operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but have control over the operating system, storage, deployed applications, and possibly limited control over selected networking components (e.g., host firewalls).

[0177] The deployment model is as follows:

[0178] Private cloud: Cloud infrastructure operated solely by an organization. It can be managed by the organization or a third party and can exist inside or outside a building.

[0179] Community cloud: Cloud infrastructure shared by several organizations and supporting a specific community with shared concerns (e.g., tasks, security requirements, policies, and compliance considerations). It can be managed by an organization or a third party and can exist on-site or off-site.

[0180] Public cloud: Cloud infrastructure available to the general public or large industrial groups and owned by organizations that sell cloud services.

[0181] Hybrid cloud: A cloud infrastructure is a combination of two or more clouds (private, community, or public) that remain a single entity but are bound together by standardized or proprietary technologies that enable data and applications to be ported together (e.g., cloud bursting for load balancing between clouds).

[0182] Cloud computing environments are service-oriented, focusing on statelessness, loose coupling, modularity, and semantic interoperability. At the heart of cloud computing is the infrastructure of a network of interconnected nodes.

[0183] Now for reference Figure 10The figure depicts an illustrative cloud computing environment 2050. As shown, the cloud computing environment 2050 includes one or more cloud computing nodes 2010 to which local computing devices used by cloud computing consumers can communicate. These local computing devices are, for example, personal digital assistants (PDAs) or cellular phones 2054A, desktop computers 2054B, laptop computers 2054C, and / or automotive computer systems 2054N. The nodes 2010 can communicate with each other. They can be physically or virtually grouped (not shown) in one or more networks, such as private clouds, community clouds, public clouds, or hybrid clouds, or combinations thereof, as described above. This allows the cloud computing environment 2050 to provide cloud consumers with infrastructure, platforms, and / or software-as-a-service that eliminates the need for them to maintain resources on their local computing devices. It should be understood that... Figure 10 The types of computing devices 2054A-N shown are intended to be illustrative only, and the computing node 2010 and cloud computing environment 2050 can communicate with any type of computing device on any type of network and / or network-addressable connection (e.g., using a web browser).

[0184] Now for reference Figure 11 This demonstrates the 2050 cloud computing environment ( Figure 10 This provides a set of functional abstractions. It should be understood beforehand that... Figure 11 The components, layers, and functions shown are for illustrative purposes only, and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

[0185] The hardware and software layer 2060 includes hardware and software components. Examples of hardware components include: a mainframe 2061, a RISC (Reduced Instruction Set Computer) based server 2062, a server 2063, a blade server 2064, a storage device 2065, and a network and networking component 2066. In some embodiments, software components include network application server software 2067 and database software 2068.

[0186] The virtualization layer 2070 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual server 2071, virtual storage device 2072, virtual network 2073, including virtual private network; virtual application and operating system 2074, and virtual client 2075.

[0187] In one example, the management layer 2080 may provide the functionality described below. Resource Provisioning 2081 provides dynamic procurement of computing resources and other resources used to perform tasks within the cloud computing environment. Metering and Pricing 2082 provides cost tracking when utilizing resources in the cloud computing environment, as well as billing or invoicing for the consumption of these resources. In one example, these resources may include application software licenses. Security provides authentication for cloud consumers and tasks, and protection for data and other resources. User Portal 2083 provides access to the cloud computing environment for consumers and system administrators. Service Level Management 2084 provides cloud resource allocation and management to ensure the required service level is met. Service Level Agreement (SLA) Planning and Fulfillment 2085 provides pre-scheduling and procurement of cloud resources, where future needs are anticipated according to the SLA.

[0188] The workload layer 2090 provides examples of functions that can be utilized in a cloud computing environment. Examples of workloads and functions that can be provided from this layer include: mapping and navigation 2091; software development and lifecycle management 2092; virtual classroom education delivery 2093; data analysis and processing 2094; transaction processing 2095; and managing AI-enabled robotic devices 2096, for example, managing roaming robotic devices using a control system.

Claims

1. A computer-implemented method for dynamically adjusting sensors and models of an autonomous roaming robot device to perform actions using artificial intelligence (AI), comprising: In response to the identification of an asset at a location using one or more of a plurality of sensors on the roaming robot device, data about the asset is received at a computer of the robot device from the one or more sensors, the robot device having instructions received from a control system for inspecting the location or an item at the location; The computer of the robot device analyzes the received and historical data for the asset, in order to refer to the relevant inspection instructions and inspection history of the asset; Based on the analysis of the received data and the historical data, the computer of the robot device is used to select a sensor from the plurality of sensors; Based on the identification of the assets and the analysis of the received data and the historical data, the computer of the robotic device is used to load an AI model; The selected sensors are used to perform asset inspections based on the AI ​​model to generate asset inspection data; Use one or more of the plurality of sensors on the roaming robot device to identify a second asset at the location; In response to the identification of the second asset at the location, data about the second asset is received from the one or more sensors at the computer of the robotic device; The computer is used to analyze data of the second asset, wherein the analysis includes historical data of the second asset. Using the analysis of data on the second asset, another AI model is generated based on the identification of the second asset; Analyze the other AI model to generate a second inspection protocol for the second asset; A second sensor is selected from the plurality of sensors based on the second inspection protocol to initiate an inspection of the second asset; as well as Perform a second asset check using the selected second sensor to generate second asset check data.

2. The method according to claim 1, further comprising: Analyze the applicability of multiple AI models to the assets; The AI ​​model is selected from the multiple AI models based on the identification of the assets, the analysis of the data, and the analysis of the multiple AI models. The computer of the robotic device is used to load the selected AI model; and The asset inspection is performed using the selected AI model and the selected sensors.

3. The method according to claim 1, wherein, The AI ​​model includes an inspection protocol for the assets.

4. The method according to claim 1, further comprising: Based on the asset inspection data, determine the action to be taken for the asset; as well as The action is transmitted to the control system.

5. The method according to claim 3, wherein, The inspection protocol includes inspection types and inspection actions for those inspection types.

6. The method according to claim 1, further comprising: The computer uses the robotic device to analyze the asset inspection data from the asset inspection; as well as The next action will be determined based on the analysis of the asset inspection data.

7. The method according to claim 6, further comprising: The next action is initiated at the robot device, and the next action includes a second sensor among the plurality of sensors on the robot device.

8. The method according to claim 7, further comprising: The third sensor among the plurality of sensors is activated based on the type of the asset. Load the relevant AI model input for the type of the asset to be used in the generation of the AI ​​model; The asset inspection data is captured using the third sensor; Analyze the asset inspection data to determine another action based on the analysis of the asset inspection data; as well as The next action is initiated at the robot device, and the next action includes a second sensor among the plurality of sensors on the robot device.

9. The method according to claim 1, further comprising: Identify multiple assets for inspection by the robotic device; Identify the AI ​​model corresponding to each of the multiple identified assets from among multiple AI models; Receive data for each of the assets respectively; Analyze the received data for each of the assets separately; Select sensors for each of the assets; Load the AI ​​model for each of the assets respectively; and Perform an asset check on each of the assets separately.

10. The method according to claim 1, wherein, The selection of the sensor is performed by the computer of the robotic device in the absence of instructions from the control system regarding the selection.

11. The method according to claim 1, wherein, The computer of the robotic device initiates the implementation of the asset inspection based on the inspection protocol derived from the AI ​​model, and the implementation of the asset inspection by the robotic device is in response to the computer of the robotic device.

12. The method according to claim 1, wherein, The computer of the robotic device responds to the AI ​​model by initiating the execution of the asset inspection.

13. The method according to claim 1, wherein, The selection of the sensor and the implementation of the asset inspection are not initiated in response to the control system.

14. The method according to claim 1, further comprising: Based on the second asset inspection data, determine the second action for the second asset; as well as The second action is transmitted to the control system.

15. The method according to claim 1, wherein, The actions include alerts regarding the asset.

16. The method according to claim 1, wherein, The actions include communication with a person's device.

17. The method according to claim 1, wherein, The actions include maintenance activities for the assets.

18. A system for dynamically adjusting sensors and models of an autonomous roaming robot device to perform actions using artificial intelligence (AI), comprising: A computer system includes: a computer processor, a computer-readable storage medium, and program instructions stored on the computer-readable storage medium, the program instructions being executable by the computer processor to cause the computer system to perform the following functions: In response to the identification of an asset at a location using one or more of a plurality of sensors on the roaming robot device, data about the asset is received at a computer of the robot device from the one or more sensors, the robot device having instructions received from a control system for inspecting the location or an item at the location; The computer of the robot device analyzes the received and historical data for the asset, in order to refer to the relevant inspection instructions and inspection history of the asset; The computer using the robot device selects a sensor from the plurality of sensors based on the analysis of the received data and the historical data. Based on the identification of the assets and the analysis of the received data and the historical data, the computer of the robotic device loads an AI model; Asset inspections are performed using the selected sensors based on the AI ​​model to generate asset inspection data; Use one or more of the plurality of sensors on the roaming robot device to identify a second asset at the location; In response to the identification of the second asset at the location, data about the second asset is received from the one or more sensors at the computer of the robotic device; The computer is used to analyze data of the second asset, wherein the analysis includes historical data of the second asset. Using the analysis of data on the second asset, another AI model is generated based on the identification of the second asset; Analyze the other AI model to generate a second inspection protocol for the second asset; Based on the second inspection protocol, a second sensor is selected from the plurality of sensors to initiate the inspection of the second asset; and Perform a second asset check using the selected second sensor to generate second asset check data.

19. A computer program product for an autonomous roaming robot device, the computer program product being used to dynamically adjust sensors and models for performing actions using artificial intelligence (AI), the computer program product including a computer-readable storage medium having program instructions embodied therein, the program instructions being executable by a computer to cause the computer to perform functions, the functions including functions for: In response to the identification of an asset at a location using one or more of a plurality of sensors on the roaming robot device, data about the asset is received at a computer of the robot device from the one or more sensors, the robot device having instructions received from a control system for inspecting the location or an item at the location; The computer of the robotic device analyzes the received and historical data for the asset, with reference to the relevant inspection instructions and inspection history of the asset; The computer using the robot device selects a sensor from the plurality of sensors based on the analysis of the received data and the historical data. Based on the identification of the assets and the analysis of the received data and the historical data, an AI model is loaded using the computer of the robotic device; Asset inspections are performed using the selected sensors based on the AI ​​model to generate asset inspection data; Use one or more of the plurality of sensors on the roaming robot device to identify a second asset at the location; In response to the identification of the second asset at the location, data about the second asset is received from the one or more sensors at the computer of the robotic device; The computer is used to analyze data of the second asset, wherein the analysis includes historical data of the second asset. Using the analysis of data on the second asset, another AI model is generated based on the identification of the second asset; Analyze the other AI model to generate a second inspection protocol for the second asset; A second sensor is selected from the plurality of sensors based on the second inspection protocol to initiate an inspection of the second asset; as well as Perform a second asset check using the selected second sensor to generate second asset check data.

20. A system for dynamically adjusting sensors and models of an autonomous roaming robot device to perform actions using artificial intelligence (AI), comprising modules for performing the steps of the method according to any one of claims 1-17.

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