Patent Description:
In one example, operations across industries can use instruments with Internet of Things (IoT) devices and sensors embedded in various equipment and location to monitor and assess processes. Using analytics and machine learning techniques, data collected from such connected operations using sensors can be analyzed to assist decision making, automation, and efficiency for processes and for making business decisions. However, in many environments, the equipment is mission-critical, having legacy technology, or too costly to instrument, e.g., nuclear power plants, large chemical furnaces, or large power grid stations. In such environments, having a robotic device such as a roaming edge device (RED) with versatile sensing ability may be a cost-effective solution. Instead of deploying a large number of sensors in legacy equipment or replacing legacy equipment, a small number of REDs can be mobile in the environment autonomously to sense the operational situations and collect important operational data that can be analyzed.

However, in such scenarios, data collected and received from sensors via the REDs can be less than optimal, subpar, poor quality. Such poor quality data can include, for example, data that is corrupted, or distorted, or has a low Signal-to-Noise Ratio.

"<NPL>, discloses an architecture that considers Quality of Data, Quality of Network, and Quality of Context to determining overall Quality of loT experience in the context of Autonomous Vehicles.

"<NPL>, discloses an unsupervised deep neural network based out-of-distribution (OOB) detector on an real-time embedded autonomous Dickiebot.

The present disclosure recognizes the shortcomings and problems associated with current techniques for Artificial Intelligence (Al) enabled adaptive navigation for enhancing task performance for an autonomous roaming robotic device, and more specifically, optimizing sensing capabilities for a mobile robotic device, e.g., a roaming edge device (RED). The present invention can, in accordance with a preferred embodiment, include a robotic device or RED acting outside a scope of an original plan or policy to initiate an updated plan or policy to improve the quality of the data collection. In such scenarios where a RED is in a location for collecting data, noise and variability in sensed information can be caused by many factors in the operational environment. In one example, the dynamic nature of the operational environment can lead to low quality sensed data. Unlike fixed sensors embedded in the environment, e.g., CCTV cameras, the physical navigation of a RED carrying the sensors may introduce errors in positioning and orientation of sensors. Further, the position and orientation of the objects in the environment may also change over time, e.g., number and sizes of boxes in a warehouse. The operational status of the equipment in the environment may change based on production plans, e.g., initialization of a processes, steady state manufacturing, and ramp down. Any of the above variations in the environment can lead to poor quality of sensed information and low accuracy in downstream Artificial Intelligence (Al) based decision-making. The variations in the environment can be difficult to predict or plan for, and since the acceptable quality and nature of sensed information depends on the specific algorithms being used in the downstream Al pipeline, it can be difficult to ensure the quality of sensed information across multiple application scenarios and environments. For energy conservation, the downstream Al pipeline may reside outside of the RED on an edge or a Cloud server communication range, for example, over a WAN (Wide Area Network). Thus, the evaluation of the quality of sensed information may not happen on the RED and there may be constraints on the amount of raw data that may be shared with the edge or Cloud server due to the cost, latency, or sensitivity of raw data. Thereby the present invention preferably addresses the challenge of accounting for the variations in the environment being sensed and dynamically adjusting the navigation of RED to optimize the sensing quality. Also, the present invention preferably addresses scenarios where the RED may not have the ability to evaluate the quality of the sensed and collected data or information.

In an aspect according to the present invention, a computer-implemented method for optimizing sensing capabilities of a roaming robotic device using Artificial Intelligence (Al), comprises receiving data, at a control system having a computer, from a robotic device. The robotic device operating at a site using a policy received from the control system, the policy includes operating actions for the robotic device, and the data is generated from sensors on the robotic device in response to a sensing task being performed by the robotic device. The method comprises analyzing the received data using the control system for determining when the received data meets a threshold for determining quality of the data, in response to the receiving of the data from the robotic device at the site. The method comprises the analysis including generating a model based on a sample set of data, the analysis also including vector representation of the received data as input data, and the analysis includes comparing the vector representation of the input data to the model. The method comprises determining when the received data does not meet the threshold for determining quality based on the analysis including the comparing of the vector representation of the input to the model. The method comprises, in response to the received data at the control system not meeting the threshold for determining quality, the robotic device communicating with the control system to collaborate in updating the policy to choose a next action.

In a preferred embodiment, the next action includes a navigation action including the robotic device moving to a next location in the site and initiating additional data collection by the robotic device for the sensing task.

In a preferred embodiment, the next action includes the robotic device adjusting a camera on the robotic device.

In a preferred embodiment, the next action is selected from a group comprising: moving to a next location; adjusting a camera on the robotic device; adjusting a sensor on the robotic deice; and waiting for a period of time.

In a preferred embodiment, the method includes creating an out of distribution detector (OOD) from the sample set of data to determine a distance between the distribution of data in the sample set of data, and the input data; and using the distance in the determining when the received data does not meet the threshold for determining quality.

In a preferred embodiment, the robotic device is a roaming edge device (RED) located at an edge of communications for roaming edge devices at the site.

In a preferred embodiment, the threshold for determining quality includes a measured level of electronic noise which is acceptable for quality and thereby when the received data exceeds the threshold, the received data is unacceptable in relation to quality.

In a preferred embodiment, the method includes updating the model, using the computer; the updated model includes the following; updating the received data; updating the analyzing of the data; and secondly communicating with the control system to collaborate in another updating of the policy for choosing navigation action, in response to the updated received data not meeting the threshold for determining quality.

In a preferred embodiment, the method includes iteratively generating the model to produce updated models.

In a preferred embodiment, the site where the robotic device is operating has low quality electronic wireless communication capabilities.

In a preferred embodiment, the method further including receiving data at the control system from the robot; and the received data at the control system meeting the threshold for determining quality of the data.

In a preferred embodiment, the robotic device communicates with the control system at least in part as a cloud server to collaborate in updating the policy to choose a navigation action, and the cloud server being in communication with the robot at the location.

In another aspect according to the present invention, a system using a computer for optimizing sensing capabilities of a roaming robotic device using Artificial Intelligence (Al), includes a computer system. The computer system comprises; a computer processor, a computer-readable storage medium, and program instructions stored on the computer-readable storage medium being executable by the processor, to cause the computer system to perform the following functions to; receive data, at a control system having a computer, from a robotic device, the robotic device operating at a site using a policy received from the control system, the policy includes operating actions for the robotic device, the data being generated from sensors on the robotic device in response to a sensing task being performed by the robotic device; analyze the received data using the control system for determining when the received data meets a threshold for determining quality of the data, in response to the receiving of the data from the robotic device at the site; generate a model, as part of the analysis, based on a sample set of data, the analysis also including vector representation of the received data as input data, and the analysis includes comparing the vector representation of the input data to the model; determine when the received data does not meet the threshold for determining quality based on the analysis including the comparing of the vector representation of the input to the model; and in response to the received data at the control system not meeting the threshold for determining quality, the robotic device communicates with the control system to collaborate in updating the policy to choose a next action.

In preferred embodiment, the next action includes a navigation action including the robotic device moving to a next location in the site and initiating additional data collection by the robotic device for the sensing task.

In preferred embodiment, the next action includes the robotic device adjusting a camera on the robotic device.

In a preferred embodiment, the next action is selected from a group consisting of: moving to a next location; adjusting a camera on the robotic device; adjusting a sensor on the robotic deice; and waiting for a period of time.

In a preferred embodiment, the system includes creating an out of distribution detector (OOD) from the sample set of data to determine a distance between the distribution of data in the sample set of data, and the input data; using the distance in the determining when the received data does not meet the threshold for determining quality.

In an aspect according to the present invention, a computer program product for optimizing sensing capabilities of a roaming robotic device using Artificial Intelligence (Al), has the computer program product comprising a computer readable storage medium having program instructions embodied therewith, The program instructions executable by a computer to cause the computer to perform functions, by the computer, comprising the functions to; receive data, at a control system having a computer, from a robotic device, the robotic device operating at a site using a policy received from the control system, the policy includes operating actions for the robotic device, the data being generated from sensors on the robotic device in response to a sensing task being performed by the robotic device; analyze the received data using the control system for determining when the received data meets a threshold for determining quality of the data, in response to the receiving of the data from the robotic device at the site; generate a model, as part of the analysis, based on a sample set of data, the analysis also including vector representation of the received data as input data, and the analysis includes comparing the vector representation of the input data to the model; determine when the received data does not meet the threshold for determining quality based on the analysis including the comparing of the vector representation of the input to the model; and in response to the received data at the control system not meeting the threshold for determining quality, the robotic device communicates with the control system to collaborate in updating the policy to choose a next action.

One or more preferred embodiments of the present invention will now be described, by way of example only, and with reference to the drawings listed below. The various features of the drawings are not to scale as the illustrations are for clarity in facilitating one skilled in the art in understanding the invention in conjunction with the detailed description.

The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. The description includes various specific details to assist in that understanding, but these are to be regarded as merely exemplary, and assist in providing clarity and conciseness. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the invention. In addition, descriptions of well-known functions and constructions may be omitted.

The terms and words used in the following description and claims are not limited to the bibliographical meanings, but are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention is provided for illustration purpose only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents.

Thus, for example, reference to "a component surface" includes reference to one or more of such surfaces unless the context clearly dictates otherwise.

Embodiments and figures of the present disclosure may have the same or similar components as other embodiments. Such figures and descriptions of illustrate and explain further examples and embodiments according to the present disclosure.

Referring to <FIG>, <FIG> and <FIG>, according to embodiments of the present disclosure, a computer-implemented method <NUM> for optimizing sensing capabilities of a roaming robotic device using Artificial Intelligence (AI) includes features described below. Embodiments of the present disclosure include operational actions and/or procedures. The computer-implemented method <NUM> includes a series of operational blocks for implementing an embodiment according to the present disclosure which can include the system shown in <FIG>. The operational blocks of the methods and systems according to the present disclosure can include techniques, mechanism, modules, and the like for implementing the functions of the operations in accordance with the present disclosure.

The method <NUM> includes receiving data <NUM> at a control system <NUM> having a computer <NUM> from a robotic device <NUM>. The robotic device can operate at a site or location <NUM>, and the control system can communicate a policy <NUM> to the robotic device <NUM> for choosing an action <NUM> (which can include a navigation action) for the robotic device, as in block <NUM>. The control system can access a knowledge corpus or historical database <NUM> which can be part of a computer system <NUM> communicating with the control system <NUM>. The Al enabled robotic device <NUM> can include a computer <NUM> having a processor <NUM> and storage medium <NUM> which can store an application <NUM>, which can embody, in all or part, the operations of one or more methods of the present disclosure. The location can include a sensor <NUM> which represents one or more sensors or a sensor array, a machine <NUM> which is representative of one or more pieces of equipment, devices, or types of machines.

In one example, the location where the robotic device is operating has low quality electronic wireless communication capabilities. For instance, sub-optimal electronic wireless communication capabilities.

The computer <NUM> can be integral to or communicating with the robotic device <NUM> in a device <NUM>. A computer <NUM> remote from the device <NUM> can electronically communicate, in all or in part, with the computer <NUM> as part of the control system <NUM>. The control system can include the computer <NUM> having a computer readable storage medium <NUM> which can store one or more programs <NUM>, and a processor <NUM> for executing program instructions. The control system can also include a storage medium which can include registration and/or account data <NUM> and profiles <NUM> of users or entities (such entities can include robotic entities) as part of user accounts <NUM>. User accounts <NUM> can be stored on a storage medium <NUM> which is part of the control system <NUM>. The user accounts <NUM> can include registrations and account data <NUM> and user profiles <NUM>. The control system can also include a computer <NUM> having a computer readable storage medium <NUM> which can store programs or code embedded on the storage medium. The program code can be executed by a processor <NUM>. The computer <NUM> can communicate with a database <NUM>. The control system <NUM> can also include a database <NUM> for storing all or part of such data as described above, and other data.

The control system can also communicate with a computer system <NUM> which can include a learning engine/module <NUM> and a knowledge corpus or database <NUM>. The computer system <NUM> can also communicate with the computer <NUM> of the device <NUM> and can be remote from the user device <NUM>. In another example, the computer system <NUM> can be all or part of the control system, or all or part of the device <NUM>. The depiction of the computer system <NUM> as well as the other components of the system <NUM> are shown as one example according to the present disclosure.

The new or different Al (Artificial Intelligence) ecosystem, or technology/communication or IT (Information Technology) ecosystem can include a local communications network <NUM> which can communicate with the communications network <NUM>. The system <NUM> can include a learning engine/module <NUM>, which can be at least part of the control system or communicating with the control system, for generating a model or learning model. In one example, the learning model can model workflow in a new Al or IT ecosystem for machine/devices in the new ecosystem.

In another example, the computer <NUM> can be part of a device <NUM>. The computer can include a processor <NUM> and a computer readable storage medium <NUM> where an application <NUM> can be stored which can in one example, embody all or part of the method of the present disclosure. The application can include all or part of instructions to implement the method of the present disclosure, embodied in code and stored on a computer readable storage medium. The device <NUM> can include a display. The device <NUM> can operate, in all or in part, in conjunction with a remote server by way of a communications network <NUM>, for example, the Internet. The method includes analyzing <NUM> the received data using the control system for determining when the received data meets a threshold <NUM> for determining quality <NUM> of the data, in response to the receiving of the data from the robotic device in the location or site, as in block <NUM>.

The method includes the analysis including generating a model <NUM> based on a sample set of data, the analysis also including vector representation of the received data as input data, and the analysis includes comparing the vector representation of the input data to the model, as in block <NUM>. The model can include vector representation of inputs detected by a sensor array <NUM> at the location, the inputs being at least part of the received data. A model can also be generated by an Al system such as an output at least in part of an Al system analysis using machine learning.

The method includes determining when the received data does not meet the threshold for determining quality based on the analysis including the comparing of the vector representation of the input to the model, as in block <NUM>.

When the threshold is met at block <NUM>, the method can return to block <NUM>. When the threshold is not met, the method can continue to block <NUM>.

The method includes, in response to the received data at the control system not meeting the threshold for determining quality, the robotic device communicating with the control system to collaborate in updating <NUM> the policy <NUM> to choose a next or updated action <NUM>, as in block <NUM>.

In one example, the next action includes a navigation action including the robotic device moving to a next location in the site and initiating additional data collection by the robotic device for the sensing task.

In another example, the next action includes the robotic device adjusting a camera on the robotic device.

In another example, the next action is selected from a group consisting of: moving to a next location; adjusting a camera on the robotic device; adjusting a sensor on the robotic deice; and waiting for a period of time.

In another example, the method can include creating an out of distribution detector (OOD) from the sample set of data to determine a distance between the distribution of data in the sample set of data, and the input data, and using the distance in the determining when the received data does not meet the threshold for determining quality.

In another example, the robotic device is a roaming edge device (RED) located at an edge of communications for roaming edge devices at the site.

In another example, the threshold for determining quality includes a measured level of electronic noise which is acceptable for quality and thereby when the received data exceeds the threshold, the received data is unacceptable in relation to quality.

In another example, a cloud server <NUM> using cloud communications <NUM> is communicable or communicating with the robot can have increased communication capabilities with the robot at the location in comparison to the control system. In another example, the method can include receiving data at the cloud system from the robot, and the received data as the cloud system meets the threshold for determining quality of the data.

In one example, as part of the analysis of received data including data in the knowledge corpus and historical database <NUM>, which can be populated by historical data gathered from sensors, mobile robots, and machines.

In one example, the robotic device can be a roaming edge device (RED) located at an edge of communications for roaming edge devices at the location. In another example, the robotic device can be at least semi-autonomous, and the robotic device can perform a task being initiated by the robotic device, without a communication for the task from the control system.

In another example, the task can be initiated by the robotic device and can include a computer local to the robotic device communicating and collaborating with the cloud server resulting in the updated policy. The computer local to the robotic device can initiate the task based on the updated policy, and the robotic device can perform the task in response to the computer local to the robotic device.

In another example, the computer local to the robotic device can initiate the task using Al analysis of data provided by the sensor array at the location, and the sensor array can include environmental data at the location. In another example, the task is not initiated in response to the control system. In another example, the environmental data can include an environmental condition. In another example, the threshold for determining quality includes a measured level of electronic noise which is acceptable for quality, and thereby when the received data exceeds the threshold, and the received data is unacceptable in relation to quality. In another example, the location where the robotic device is operating can have low quality electronic wireless communication capabilities, e.g., sub-optimal electronic wireless communication capabilities.

In another example, the cloud server can have increased communication capabilities with the robot at the location in comparison to the control system.

Referring to <FIG>, in another embodiment according to the present disclosure a method <NUM> can continue from block <NUM> of the method <NUM> shown in <FIG>, and the method <NUM> includes initiating a task or action by the robotic device, including a computer local to the robotic device communicating and collaborating with the cloud server, resulting in the updated policy, as in block <NUM>.

The method <NUM> includes initiating the task based on the updated policy using the computer local to the robotic device, as in block <NUM>. The method includes the robotic device performing the task in response to the computer local to the robotic device, as in block <NUM>.

Referring to <FIG>, in another embodiment according to the present disclosure a method <NUM> can continue from block <NUM> of the method <NUM> shown in <FIG>, and the method <NUM> includes updating the model, using the computer, the updated model includes updating the received data, as in block <NUM>. The method <NUM> includes updating the analyzing of the data, as in block <NUM>. The method <NUM> includes secondly communicating with the control system to collaborate in another updating of the policy for choosing navigation action, in response to the updated received data not meeting the threshold for determining quality, as in block <NUM>. The method can further include iteratively updating the updated notification based detecting a change of a parameter of the event.

Referring to <FIG>, the device <NUM>, also can be referred to as a user device or an administrator's device, includes a computer <NUM> having a processor <NUM> and a storage medium <NUM> where an application <NUM>, can be stored. The application can embody the features of the method of the present disclosure as instructions. The user can connect to a learning engine <NUM> using the device <NUM>. The device <NUM>, which includes the computer <NUM> and a display or monitor <NUM>. The application <NUM> can embody the method of the present disclosure and can be stored on the computer readable storage medium <NUM>. The device <NUM> can further include the processor <NUM> for executing the application/software <NUM>. The device <NUM> can communicate with a communications network <NUM>, e.g., the Internet.

It is understood that the user device <NUM> is representative of similar devices which can be for other users, as representative of such devices, which can include, mobile devices, smart devices, laptop computers etc..

In one example, the system of the present disclosure can include a control system <NUM> communicating with the user device <NUM> via a communications network <NUM>. The control system can incorporate all or part of an application or software for implementing the method of the present disclosure. The control system can include a computer readable storage medium <NUM> where account data and/or registration data <NUM> can be stored. User profiles <NUM> can be part of the account data and stored on the storage medium <NUM>. The control system can include a computer <NUM> having computer readable storage medium <NUM> and software programs <NUM> stored therein. A processor <NUM> can be used to execute or implement the instructions of the software program. The control system can also include a database <NUM>.

In another example and embodiment, profiles can be saved for users/participants. Such profiles can supply data regarding the user and history of deliveries for analysis. In one example, a user can register or create an account using the control system <NUM> which can include one or more profiles <NUM> as part of registration and/or account data <NUM>. The registration can include profiles for each user having personalized data. For example, users can register using a website via their computer and GUI (Graphical User Interface) interface. The registration or account data <NUM> can include profiles <NUM> for an account <NUM> for each user. Such accounts can be stored on the control system <NUM>, which can also use the database <NUM> for data storage. A user and a related account can refer to, for example, a person, or an entity, or a corporate entity, or a corporate department, or another machine such as an entity for automation such as a system using, in all or in part, artificial intelligence.

Additionally, the method and system is discussed with reference to <FIG>, which is a functional system <NUM> which includes components and operations for embodiments according to the present disclosure, and is used herein for reference when describing the operational steps of the methods and systems of the present disclosure. Additionally, the functional system <NUM>, according to an embodiment of the present disclosure, depicts functional operations indicative of the embodiments discussed herein.

Referring to <FIG>, in one embodiment according to the present disclosure, a system <NUM> can be used to identify objects related to an event for use regarding the event by using networked computer system resources. In <FIG> similar components may have the same reference numerals as the system <NUM> shown in <FIG>, the system <NUM> can include or operate in concert with a computer implemented method as shown in <FIG> and <FIG>.

Generally referring to <FIG>, in one embodiment according to the present disclosure, a system and method <NUM> can be used for optimizing sensing capabilities of a roaming robotic device using Artificial Intelligence (Al). The method and system includes Al enabled adaptive navigation for enhancing task performance for an autonomous roaming robotic device, and more specifically, optimizing sensing capabilities for a mobile robotic device, e.g., a roaming edge device (RED).

Methods and system according to the present disclosure can reduce noise and variability in sensed information caused by the dynamic nature of the operational environment, such as introduced by the physical navigation of a RED carrying the sensors. Present methods and system can account for the position and orientation of objects in an environment changing over time, and changes in operational status of equipment in the environment by enhancing quality of sensed information. The methods and systems can account for variations in the environment being sensed and dynamical adjusting the navigation of a RED to optimize the sensing quality. In one example, a method of online reinforcement learning is combined with a versatile outlier detection method as a reward function to address the challenges identified above. The action and state space can be pre-defined based on the navigational and sensing capabilities of the RED. However, the reward function may not be local to the RED and is in the control of the edge or the cloud server where the Al task is deployed. For example, if the RED is a mobile robot, the action space can include movement in a <NUM>-D (three dimensional) environment, a change in pose, or a configuration of each of the sensors. The state space can include a vector representation of the sensed inputs across the sensor array, e.g., optical camera, thermal camera, microphones, and determining ranges by targeting an object with a laser and measuring the time for the reflected light to return. Given a specific Al task, e.g., inspecting a fire extinguisher, a reward function could be defined to correspond to the quality of the sensed state, e.g., whether or not a fire extinguisher is visible in the optical camera. One approach to defining the reward function that works across a variety of Al tasks is based on the likelihood that a given sensed state is out-of-distribution for the Al pipeline for the Al task. A reward function can include a reinforcement of what the Al task should accomplish. A reward function provides guidance to the robot, for example, whether an Al task is completed with satisfactory result or whether it needs to be adjusted to improve the quality. In another embodiment, after multiple trials of achieving a better reward function, the robot has the option to notify a human for assistance when the robot fails to improve the sensing quality.

Referring to <FIG>, in one example, a method and system according to the present disclosure can include an Al system and method <NUM> including defining an action space and state space which include sensors, as in block <NUM>. For example, given a specific RED, the action space and the state space are defined to include all sensors and navigation capabilities available to the RED.

The method <NUM> includes a robot or RED receiving an Al task, at block <NUM>. One or more Al tasks can be defined, and each can include features that are expected, model architectures, and training and inference algorithms.

The method includes the robot identifying an Al model for the Al task, as in block <NUM>. Given training data collected for the Al task, an Al pipeline is trained that may include one or more machine learning models.

The method also includes an Al model being optimized with training data, as in block <NUM>. A fingerprint based out of distribution (OOD) detector can be created based on the training set, as in block <NUM>.

The method includes the robot sensing and collecting sample data for the Al task, as in block <NUM>.

The method includes comparing the OOD detector distance or distance measurement between sample data collected by the robot and the distance data from a training set, as in block <NUM>. For each machine learning model, a fingerprint-based out-of-distribution (OOD) detector can be created from the same training data as that used to train the model. The detector produces a distance measurement between an input sample and the distribution of samples in the training set.

The method includes creating a rewards function, as in block <NUM>. A reward function based on the OOD detector distance and a policy for choosing the next navigation action can be created. An agent that can communicate with the edge or a cloud server is deployed on the RED and the Al pipeline along with the reward function and policy learning are deployed on the edge or cloud server. A standard mission of navigation and sensing tasks can be recorded on the RED and replayed. During mission replay, the agent deployed on the RED can send the sensor data along with action taken to the edge/cloud server. The agent on the RED may optionally compute a feature projection on the data and send it to the edge/cloud server. The Al pipeline in the edge/cloud server can evaluate the quality of the sensor data based on the OOD detector, in one example, when the sensor data is used to make predictions using the Al pipeline.

The method includes determining when an OOD detector distance of a sample data within an acceptable threshold, as in block <NUM>. When the acceptable threshold at block <NUM> is met the method returns to block <NUM>. When the acceptable threshold at block <NUM> is not met, the method proceeds to block <NUM> which includes determining when the robot tried all sensing and navigation options. When the robot has tried all sensing and navigation options at block <NUM>, the method can proceed to the robot notifying a human for assistance at block <NUM>. When all sensing and navigation options have not been tried, the method can proceed to the robot adjusting navigation and sensing capabilities, as in block <NUM>, for example, cooperatively communicating with a cloud server which is accessible for collaboration.

Further, if the input is found to be OOD beyond a threshold, navigation actions are generated via the current policy. The RED and edge/cloud server can collaborate in updating the policy, as in block <NUM>, until the sensor data is of acceptable quality. In another example, a maximum number of trials can be defined for the RED. If the RED fails to improve the sensing quality, it can notify a human to ask for assistance, as in block <NUM>, or it can make specific request to improve the environmental conditions of where the sensing occurs.

Thereby, a method and system can provide adaptive navigation to optimize the quality of sensed information which includes an Al pipeline that is designed to accomplish a specific machine learning task. A task-agnostic reinforcement learning algorithm can have state and action spaces defined on the RED sensors and navigation capabilities. The reward function can be based on the distance of a sensor state from the training distribution, and the policy function that is learned via a reinforcement learning technique. A pre-defined mission can be executed by the RED with a local agent transmitting the sensor state to an edge/cloud server. The edge/cloud server can evaluate the quality of the sensor state based on an OOD detector, and if found to be OOD, then can update the policy and select a next navigation action to be executed by the agent and communicates it to the agent on RED. The agent on the RED can receive the next navigation action from edge/cloud server and execute it until the sample is found to be acceptable for the Al task.

Operational blocks and system components shown in one or more of the figures may be similar to operational blocks and system components in other figures. The diversity of operational blocks and system components depict example embodiments and aspects according to the present disclosure. For example, methods shown are intended as example embodiments which can include aspects/operations shown and discussed previously in the present disclosure, and in one example, continuing from a previous method shown in another flow chart.

In the embodiment of the present disclosure shown in <FIG> and <FIG>, a computer can be part of a remote computer or a remote server, for example, remote server <NUM> (<FIG>). In another example, the computer <NUM> can be part of a control system <NUM> and provide execution of the functions of the present disclosure. In another embodiment, a computer can be part of a mobile device and provide execution of the functions of the present disclosure. In still another embodiment, parts of the execution of functions of the present disclosure can be shared between the control system computer and the mobile device computer, for example, the control system function as a back end of a program or programs embodying the present disclosure and the mobile device computer functioning as a front end of the program or programs.

The computer can be part of the mobile device, or a remote computer communicating with the mobile device. In another example, a mobile device and a remote computer can work in combination to implement the method of the present disclosure using stored program code or instructions to execute the features of the method(s) described herein. In one example, the device <NUM> can include a computer <NUM> having a processor <NUM> and a storage medium <NUM> which stores an application <NUM>, and the computer includes a display <NUM>. The application can incorporate program instructions for executing the features of the present disclosure using the processor <NUM>. In another example, the mobile device application or computer software can have program instructions executable for a front end of a software application incorporating the features of the method of the present disclosure in program instructions, while a back end program or programs <NUM>, of the software application, stored on the computer <NUM> of the control system <NUM> communicates with the mobile device computer and executes other features of the method. The control system <NUM> and the device (e.g., mobile device or computer) <NUM> can communicate using a communications network <NUM>, for example, the Internet.

Thereby, the method <NUM> according to an embodiment of the present disclosure, can be incorporated in one or more computer programs or an application <NUM> stored on an electronic storage medium <NUM>, and executable by the processor <NUM>, as part of the computer on mobile device. For example, a mobile device can communicate with the control system <NUM>, and in another example, a device such as a video feed device can communicate directly with the control system <NUM>. Other users (not shown) may have similar mobile devices which communicate with the control system similarly. The application can be stored, all or in part, on a computer or a computer in a mobile device and at a control system communicating with the mobile device, for example, using the communications network <NUM>, such as the Internet. It is envisioned that the application can access all or part of program instructions to implement the method of the present disclosure. The program or application can communicate with a remote computer system via a communications network <NUM> (e.g., the Internet) and access data, and cooperate with program(s) stored on the remote computer system. Such interactions and mechanisms are described in further detail herein and referred to regarding components of a computer system, such as computer readable storage media, which are shown in one embodiment in <FIG> and described in more detail in regards thereto referring to one or more computer systems <NUM>.

Thus, in one example, a control system <NUM> is in communication with the computer <NUM> or device <NUM>, and the computer can include the application or software <NUM>. The computer <NUM>, or a computer in a mobile device <NUM> communicates with the control system <NUM> using the communications network <NUM>.

In another example, the control system <NUM> can have a front-end computer belonging to one or more users, and a back-end computer embodied as the control system.

Also, referring to <FIG>, a device <NUM> can include a computer <NUM>, computer readable storage medium <NUM>, and operating systems, and/or programs, and/or a software application <NUM>, which can include program instructions executable using a processor <NUM>. These features are shown herein in <FIG>, and other similar components and features are also in an embodiment of a computer system shown in <FIG> referring to a computer system <NUM>, which may include one or more computer components.

The method according to the present disclosure, can include a computer for implementing the features of the method, according to the present disclosure, as part of a control system. In another example, a computer as part of a control system can work in corporation with a mobile device computer in concert with communication system for implementing the features of the method according to the present disclosure. In another example, a computer for implementing the features of the method can be part of a mobile device and thus implement the method locally.

Specifically, regarding the control system <NUM>, a device(s) <NUM>, or in one example devices which can belong to one or more users, can be in communication with the control system <NUM> via the communications network <NUM>. In the embodiment of the control system shown in <FIG>, the control system <NUM> includes a computer <NUM> communicating with a database <NUM> and one or more programs <NUM> stored on a computer readable storage medium <NUM>. In the embodiment of the disclosure shown in <FIG>, the device <NUM> communicates with the control system <NUM> and the one or more programs <NUM> stored on a computer readable storage medium <NUM>. The control system includes the computer <NUM> having a processor <NUM>, which also has access to the database <NUM>.

The control system <NUM> can include a storage medium <NUM> for maintaining a registration <NUM> of users and their devices for analysis of the audio input. Such registration can include user profiles <NUM>, which can include user data supplied by the users in reference to registering and setting-up an account. In an embodiment, the method and system which incorporates the present disclosure includes the control system (generally referred to as the back-end) in combination and cooperation with a front end of the method and system, which can be the application <NUM>. In one example, the application <NUM> is stored on a device, for example, a computer or device on location, and can access data and additional programs at a back end of the application, e.g., control system <NUM>.

The control system can also be part of a software application implementation, and/or represent a software application having a front-end user part and a back-end part providing functionality. In an embodiment, the method and system which incorporates the present disclosure includes the control system (which can be generally referred to as the back-end of the software application which incorporates a part of the method and system of an embodiment of the present application) in combination and cooperation with a front end of the software application incorporating another part of the method and system of the present application at the device, as in the example shown in <FIG> of a device <NUM> and computer <NUM> having the application <NUM>. The application <NUM> is stored on the device or computer and can access data and additional programs at the back end of the application, for example, in the program(s) <NUM> stored in the control system <NUM>.

The program(s) <NUM> can include, all or in part, a series of executable steps for implementing the method of the present disclosure. A program, incorporating the present method, can be all or in part stored in the computer readable storage medium on the control system or, in all or in part, on a computer or device <NUM>. It is envisioned that the control system <NUM> can not only store the profile of users, but in one embodiment, can interact with a website for viewing on a display of a device such as a mobile device, or in another example the Internet, and receive user input related to the method and system of the present disclosure. It is understood that <FIG> depicts one or more profiles <NUM>, however, the method can include multiple profiles, users, registrations, etc. It is envisioned that a plurality of users or a group of users can register and provide profiles using the control system for use according to the method and system of the present disclosure.

It is understood that the features shown in some of the FIGS. , for example block diagrams, are functional representations of features of the present disclosure. Such features are shown in embodiments of the systems and methods of the present disclosure for illustrative purposes to clarify the functionality of features of the present disclosure.

The methods and systems of the present disclosure can include a series of operation blocks for implementing one or more embodiments according to the present disclosure. In some examples, operational blocks of one or more FIGS. may be similar to operational blocks shown in another figure. A method shown in one FIG. may be another example embodiment which can include aspects/operations shown in another FIG. and discussed previously.

Account data, for instance, including profile data related to a user, and any data, personal or otherwise, can be collected and stored, for example, in the control system <NUM>. It is understood that such data collection is done with the knowledge and consent of a user, and stored to preserve privacy, which is discussed in more detail below. Such data can include personal data, and data regarding personal items.

In one example a user can register <NUM> have an account <NUM> with a user profile <NUM> on a control system <NUM>, which is discussed in more detail below. For example, data can be collected using techniques as discussed above, for example, using cameras, and data can be uploaded to a user profile by the user. A user can include, for example, a corporate entity, or department of a business, or a homeowner, or any end user.

Regarding collection of data with respect to the present disclosure, such uploading or generation of profiles is voluntary by the one or more users, and thus initiated by and with the approval of a user. Thereby, a user can opt-in to establishing an account having a profile according to the present disclosure. Similarly, data received by the system or inputted or received as an input is voluntary by one or more users, and thus initiated by and with the approval of the user. Thereby, a user can opt-in to input data according to the present disclosure. Such user approval also includes a user's option to cancel such profile or account, and/or input of data, and thus opt-out, at the user's discretion, of capturing communications and data. Further, any data stored or collected is understood to be intended to be securely stored and unavailable without authorization by the user, and not available to the public and/or unauthorized users. Such stored data is understood to be deleted at the request of the user and deleted in a secure manner. Also, any use of such stored data is understood to be, according to the present disclosure, only with the user's authorization and consent.

In one or more embodiments of the present invention, a user(s) can opt-in or register with a control system, voluntarily providing data and/or information in the process, with the user's consent and authorization, where the data is stored and used in the one or more methods of the present disclosure. Also, a user(s) can register one or more user electronic devices for use with the one or more methods and systems according to the present disclosure. As part of a registration, a user can also identify and authorize access to one or more activities or other systems (e.g., audio and/or video systems). Such opt-in of registration and authorizing collection and/or storage of data is voluntary and a user may request deletion of data (including a profile and/or profile data), un-registering, and/or opt-out of any registration. It is understood that such opting-out includes disposal of all data in a secure manner. A user interface can also allow a user or an individual to remove all their historical data.

In one example, Artificial Intelligence (Al) can be used, all or in part, for generating a model or a learning model as discussed herein in embodiments of the present disclosure. An Artificial Intelligence (Al) System can include machines, computer, and computer programs which are designed to be intelligent or mirror intelligence. Such systems can include computers executing algorithms. Al can include machine learning and deep learning. For example, deep learning can include neural networks. An Al system can be cloud based, that is, using a cloud-based computing environment having computing resources.

In another example, the control system <NUM> can be all or part of an Artificial Intelligence (Al) system. For example, the control system can be one or more components of an Al system.

It is also understood that the method <NUM> according to an embodiment of the present disclosure, can be incorporated into (Artificial Intelligence) Al devices, components or be part of an Al system, which can communicate with respective Al systems and components, and respective Al system platforms. Thereby, such programs or an application incorporating the method of the present disclosure, as discussed above, can be part of an Al system. In one embodiment according to the present invention, it is envisioned that the control system can communicate with an Al system, or in another example can be part of an Al system. The control system can also represent a software application having a front-end user part and a back-end part providing functionality, which can in one or more examples, interact with, encompass, or be part of larger systems, such as an Al system. In one example, an Al device can be associated with an Al system, which can be all or in part, a control system and/or a content delivery system, and be remote from an Al device. Such an Al system can be represented by one or more servers storing programs on computer readable medium which can communicate with one or more Al devices. The Al system can communicate with the control system, and in one or more embodiments, the control system can be all or part of the Al system or vice versa.

It is understood that as discussed herein, a download or downloadable data can be initiated using a voice command or using a mouse, touch screen, etc. In such examples a mobile device can be user initiated, or an Al device can be used with consent and permission of users. Other examples of Al devices include devices which include a microphone, speaker, and can access a cellular network or mobile network, a communications network, or the Internet, for example, a vehicle having a computer and having cellular or satellite communications, or in another example, IoT (Internet of Things) devices, such as appliances, having cellular network or Internet access.

It is understood that a set or group is a collection of distinct objects or elements. The objects or elements that make up a set or group can be anything, for example, numbers, letters of the alphabet, other sets, a number of people or users, and so on. It is further understood that a set or group can be one element, for example, one thing or a number, in other words, a set of one element, for example, one or more users or people or participants. It is also understood that machine and device are used interchangeable herein to refer to machine or devices in one or more Al ecosystems or environments.

The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Likewise, examples of features or functionality of the embodiments of the disclosure described herein, whether used in the description of a particular embodiment, or listed as examples, are not intended to limit the embodiments of the disclosure described herein, or limit the disclosure to the examples described herein. Such examples are intended to be examples or exemplary, and non-exhaustive. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Referring to <FIG>, an embodiment of system or computer environment <NUM>, according to the present disclosure, includes a computer system <NUM> shown in the form of a generic computing device. The method <NUM>, for example, may be embodied in a program <NUM>, including program instructions, embodied on a computer readable storage device, or a computer readable storage medium, for example, generally referred to as computer memory <NUM> and more specifically, computer readable storage medium <NUM>. Such memory and/or computer readable storage media includes non-volatile memory or non-volatile storage, also known and referred to non-transient computer readable storage media, or non-transitory computer readable storage media. For example, such non-volatile memory can also be disk storage devices, including one or more hard drives. For example, memory <NUM> can include storage media <NUM> such as RAM (Random Access Memory) or ROM (Read Only Memory), and cache memory <NUM>. The program <NUM> is executable by the processor <NUM> of the computer system <NUM> (to execute program steps, code, or program code). Additional data storage may also be embodied as a database <NUM> which includes data <NUM>. The computer system <NUM> and the program <NUM> are generic representations of a computer and program that may be local to a user, or provided as a remote service (for example, as a cloud based service), and may be provided in further examples, using a website accessible using the communications network <NUM> (e.g., interacting with a network, the Internet, or cloud services). It is understood that the computer system <NUM> also generically represents herein a computer device or a computer included in a device, such as a laptop or desktop computer, etc., or one or more servers, alone or as part of a datacenter. The computer system can include a network adapter/interface <NUM>, and an input/output (I/O) interface(s) <NUM>. The I/O interface <NUM> allows for input and output of data with an external device <NUM> that may be connected to the computer system. The network adapter/interface <NUM> may provide communications between the computer system a network generically shown as the communications network <NUM>.

The computer <NUM> may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. The method steps and system components and techniques may be embodied in modules of the program <NUM> for performing the tasks of each of the steps of the method and system. The modules are generically represented in the figure as program modules <NUM>. The program <NUM> and program modules <NUM> can execute specific steps, routines, sub-routines, instructions or code, of the program.

The method of the present disclosure can be run locally on a device such as a mobile device, or can be run a service, for instance, on the server <NUM> which may be remote and can be accessed using the communications network <NUM>. The program or executable instructions may also be offered as a service by a provider. The computer <NUM> may be practiced in a distributed cloud computing environment where tasks are performed by remote processing devices that are linked through a communications network <NUM>. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.

More specifically, the system or computer environment <NUM> includes the computer system <NUM> shown in the form of a general-purpose computing device with illustrative periphery devices. The components of the computer system <NUM> may include, but are not limited to, one or more processors or processing units <NUM>, a system memory <NUM>, and a bus <NUM> that couples various system components including system memory <NUM> to processor <NUM>.

The bus <NUM> represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus.

The computer <NUM> can include a variety of computer readable media. Such media may be any available media that is accessible by the computer <NUM> (e.g., computer system, or server), and can include both volatile and non-volatile media, as well as removable and non-removable media. Computer memory <NUM> can include additional computer readable media in the form of volatile memory, such as random access memory (RAM) <NUM>, and/or cache memory <NUM>. The computer <NUM> may further include other removable/non-removable, volatile/non-volatile computer storage media, in one example, portable computer readable storage media <NUM>. In one embodiment, the computer readable storage medium <NUM> can be provided for reading from and writing to a non-removable, non-volatile magnetic media. The computer readable storage medium <NUM> can be embodied, for example, as a hard drive. Additional memory and data storage can be provided, for example, as the storage system <NUM> (e.g., a database) for storing data <NUM> and communicating with the processing unit <NUM>. The database can be stored on or be part of a server <NUM>. Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic 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 can be provided. In such instances, each can be connected to bus <NUM> by one or more data media interfaces. As will be further depicted and described below, memory <NUM> may include at least one program product which can include one or more program modules that are configured to carry out the functions of embodiments of the present invention.

The method(s) described in the present disclosure, for example, may be embodied in one or more computer programs, generically referred to as a program <NUM> and can be stored in memory <NUM> in the computer readable storage medium <NUM>. The program <NUM> can include program modules <NUM>. The program modules <NUM> can generally carry out functions and/or methodologies of embodiments of the invention as described herein. The one or more programs <NUM> are stored in memory <NUM> and are executable by the processing unit <NUM>. By way of example, the memory <NUM> may store an operating system <NUM>, one or more application programs <NUM>, other program modules, and program data on the computer readable storage medium <NUM>. It is understood that the program <NUM>, and the operating system <NUM> and the application program(s) <NUM> stored on the computer readable storage medium <NUM> are similarly executable by the processing unit <NUM>. It is also understood that the application <NUM> and program(s) <NUM> are shown generically, and can include all of, or be part of, one or more applications and program discussed in the present disclosure, or vice versa, that is, the application <NUM> and program <NUM> can be all or part of one or more applications or programs which are discussed in the present disclosure. It is also understood that a control system <NUM>, communicating with a computer system, can include all or part of the computer system <NUM> and its components, and/or the control system can communicate with all or part of the computer system <NUM> and its components as a remote computer system, to achieve the control system functions described in the present disclosure. The control system function, for example, can include storing, processing, and executing software instructions to perform the functions of the present disclosure. It is also understood that the one or more computers or computer systems shown in <FIG> similarly can include all or part of the computer system <NUM> and its components, and/or the one or more computers can communicate with all or part of the computer system <NUM> and its components as a remote computer system, to achieve the computer functions described in the present disclosure.

In an embodiment according to the present disclosure, one or more programs can be stored in one or more computer readable storage media such that a program is embodied and/or encoded in a computer readable storage medium. In one example, the stored program can 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 embedment according to the present 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-transient or non-transitory computer readable storage medium. Thus, embodiments or examples according to the present disclosure, of a computer readable storage medium do not include a signal, and embodiments can include one or more non-transient or non-transitory computer readable storage mediums. Thereby, in one example, a program can be recorded on a computer readable storage medium and become structurally and functionally interrelated to the medium.

The computer <NUM> may also communicate with one or more external devices <NUM> such as a keyboard, a pointing device, a display <NUM>, etc.; one or more devices that enable a user to interact with the computer <NUM>; and/or any devices (e.g., network card, modem, etc.) that enables the computer <NUM> to communicate with one or more other computing devices. Such communication can occur via the Input/Output (I/O) interfaces <NUM>. Still yet, the computer <NUM> can communicate with one or more networks <NUM> 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 <NUM>. As depicted, network adapter <NUM> communicates with the other components of the computer <NUM> via bus <NUM>. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with the computer <NUM>. Examples, include, but are not limited to: microcode, device drivers <NUM>, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc..

It is understood that a computer or a program running on the computer <NUM> may communicate with a server, embodied as the server <NUM>, via one or more communications networks, embodied as the communications network <NUM>. The communications network <NUM> may include transmission media and network links which include, for example, wireless, wired, or optical fiber, and routers, firewalls, switches, and gateway computers. The communications network may include connections, such as wire, wireless communication links, or fiber optic cables. A communications network may represent a worldwide collection of networks and gateways, such as the Internet, that use various protocols to communicate with one another, such as Lightweight Directory Access Protocol (LDAP), Transport Control Protocol/Internet Protocol (TCP/IP), Hypertext Transport Protocol (HTTP), Wireless Application Protocol (WAP), etc. A network may also include a number of different types of networks, such as, for example, an intranet, a local area network (LAN), or a wide area network (WAN).

In one example, a computer can use a network which may access a website on the Web (World Wide Web) using the Internet. In one embodiment, a computer <NUM>, including a mobile device, can use a communications system or network <NUM> which can include the Internet, or a public switched telephone network (PSTN) for example, a cellular network. The PSTN may include telephone lines, fiber optic cables, microwave transmission links, cellular networks, and communications satellites. The Internet may facilitate numerous searching and texting techniques, for example, using a cell phone or laptop computer to send queries to search engines via text messages (SMS), Multimedia Messaging Service (MMS) (related to SMS), email, or a web browser. The search engine can retrieve search results, that is, links to websites, documents, or other downloadable data that correspond to the query, and similarly, provide the search results to the user via the device as, for example, a web page of search results.

Referring to <FIG>, an example system <NUM> for use with the embodiments of the present disclosure is depicted. The system <NUM> includes a plurality of components and elements connected via a system bus <NUM>. At least one processor (CPU) <NUM>, is connected to other components via the system bus <NUM>. A cache <NUM>, a Read Only Memory (ROM) <NUM>, a Random Access Memory (RAM) <NUM>, an input/output (I/O) adapter <NUM>, a sound adapter <NUM>, a network adapter <NUM>, a user interface adapter <NUM>, a display adapter <NUM> and a display device <NUM>, are also operatively coupled to the system bus <NUM> of the system <NUM>. An AR device <NUM> can also be operatively coupled to the bus <NUM>. An Al enabled robotic device and control system <NUM> can also be operatively coupled to the bus <NUM>. Such a robot and control system <NUM> can incorporate all or part of embodiments of the present disclosure and discussed hereinbefore. An artificial intelligence (Al) system <NUM> or an Al ecosystem can also be operatively coupled to the bus <NUM>. A power supply <NUM> can also be operatively connected to the bus <NUM> for providing power to components and for functions according to the present disclosure. An augmented reality (AR) device <NUM> can also be operatively connected to the bus <NUM> for providing augmented reality output to a wearable augmented reality device, such as AR glasses or an AR headset.

One or more storage devices <NUM> are operatively coupled to the system bus <NUM> by the I/O adapter <NUM>. The storage device <NUM>, for example, can be any of a disk storage device (e.g., a magnetic or optical disk storage device), a solid state magnetic device, and so forth. The storage device <NUM> can be the same type of storage device or different types of storage devices. The storage device can include, for example, but not limited to, a hard drive or flash memory and be used to store one or more programs <NUM> or applications <NUM>. The programs and applications are shown as generic components and are executable using the processor <NUM>. The program <NUM> and/or application <NUM> can include all of, or part of, programs or applications discussed in the present disclosure, as well vice versa, that is, the program <NUM> and the application <NUM> can be part of other applications or program discussed in the present disclosure.

The system <NUM> can include the control system <NUM> which is part of the system <NUM> (described in further detail hereinbefore) and can communicate with the system bus independently or as part of the system <NUM>, and thus can communicate with the other components of the system <NUM> via the system bus. In one example, the storage device <NUM>, via the system bus, can communicate with the control system <NUM> which has various functions as described in the present disclosure.

In one aspect, a speaker <NUM> is operatively coupled to system bus <NUM> by the sound adapter <NUM>. A transceiver <NUM> is operatively coupled to system bus <NUM> by the network adapter <NUM>. A display <NUM> is operatively coupled to the system bus <NUM> by the display adapter <NUM>.

In another aspect, one or more user input devices <NUM> are operatively coupled to the system bus <NUM> by the user interface adapter <NUM>. The user input devices <NUM> can be, for example, any of a keyboard, a mouse, a keypad, an image capture device, a motion sensing device, a microphone, a device incorporating the functionality of at least two of the preceding devices, and so forth. Other types of input devices can also be used, while maintaining the scope of the present invention. The user input devices <NUM> can be the same type of user input device or different types of user input devices. The user input devices <NUM> are used to input and output information to and from the system <NUM>.

The flowchart and block diagrams in the Figures of the present disclosure illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention.

Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.

Infrastructure as a Service (laaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).

Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.

As shown, cloud computing environment <NUM> includes one or more cloud computing nodes <NUM> with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 2054A, desktop computer 2054B, laptop computer 2054C, and/or automobile computer system 2054N may communicate. It is understood that the types of computing devices 2054A-N shown in <FIG> are intended to be illustrative only and that computing nodes <NUM> and cloud computing environment <NUM> can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

As depicted, the following layers and corresponding functions are provided:
Hardware and software layer <NUM> includes hardware and software components.

Claim 1:
A computer-implemented method for optimizing sensing capabilities of an Artificial Intelligence, Al, enabled roaming robotic device (<NUM>)comprising:
receiving data (<NUM>), at a control system (<NUM>) having a computer (<NUM>), from the robotic device, the robotic device operating at a site using a policy (<NUM>) received from the control system, the policy includes operating actions (<NUM>) for the robotic device, the data being generated from sensors on the robotic device in response to a sensing task being performed by the robotic device, wherein the robotic device is a roaming edge device, RED located at an edge of communications for roaming edge devices at the site;
analyzing (<NUM>) the received data using the control system for determining when the received data meets a threshold for determining quality of the data, in response to the receiving of the data from the robotic device at the site;
the analysis including generating (<NUM>) an Al model based on a sample set of data, the analysis also including vector representation of the received data as input data, and the analysis includes comparing the vector representation of the input data to the model;
determining (<NUM>) whether the received data does not meet the threshold for determining quality based on the analysis including the comparing of the vector representation of the input to the model;
in response to the received data at the control system not meeting the threshold for determining quality, the robotic device communicating (<NUM>) with the control system to collaborate in updating (<NUM>) the policy to choose a next action;
creating (<NUM>) an out of distribution detector, OOD, from the sample set of data to determine a distance between the distribution of data in the sample set of data, and the input data; and
using the distance in the determining when the received data does not meet the threshold for determining quality.