Abstract: Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

Abstract: Adjusting a navigation path of a worker land roaming edge device is provided. The worker land roaming edge device is identified moving along a navigation path to an asset to perform a task. It is determined that the worker land roaming edge device cannot reach the asset to perform the task due to an environment change along the navigation path. A set of task performance alternatives is generated in response to determining that the worker land roaming edge device cannot reach the asset to perform the task. A task performance alternative of the set of task performance alternatives is selected based on a set of criteria. The navigation path of the worker land roaming edge device is adjusted in accordance with the selected task performance alternative. The adjusted navigation path is sent to the worker land roaming edge device to dynamically adjust operation of the worker land roaming edge device.

Abstract: Adjusting a navigation path of a worker land roaming edge device is provided. The worker land roaming edge device is identified moving along a navigation path to an asset to perform a task. It is determined that the worker land roaming edge device cannot reach the asset to perform the task due to an environment change along the navigation path. A set of task performance alternatives is generated in response to determining that the worker land roaming edge device cannot reach the asset to perform the task. A task performance alternative of the set of task performance alternatives is selected based on a set of criteria. The navigation path of the worker land roaming edge device is adjusted in accordance with the selected task performance alternative. The adjusted navigation path is sent to the worker land roaming edge device to dynamically adjust operation of the worker land roaming edge device.

Abstract: A method, computer program product, and system include a processor(s) that engages, based on a request for an inference, from a group of sensors of multiple modalities at a physical location, sensor(s) of a main modality to provide data to a pipeline to generate the inference. The pipeline includes one or more machine learning models which generate the inference for a downstream task. The processor(s) obtains raw data from the sensor(s) of the main modality and applies an outlier detector to the raw data. Based on determining that there is an outlier the processor(s) automatically engages sensor(s) of at least one different modality than the main modality from the group of sensors of multiple modalities and obtains new raw data from the sensor(s) of the at least one different modality. The processor(s) applies the one or more machine learning models to the new raw data to derive the inference.

Abstract: Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

Abstract: Optimizing sensing capabilities of a roaming robotic device using Artificial Intelligence (AI) includes receiving data at a control system having a computer from a robotic device. The control system communicating a policy to the robotic device for choosing navigation actions for the robotic device. The received data is analyzed using the control system for determining when the received data meets a threshold for determining quality of the data. The analysis can include generating a model based on the received data where the model includes vector representation of inputs detected by a sensor array at the location. 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.

Abstract: A method for use with a facility including a plurality of physical assets and an Internet of Things (IoT) sub-system including receiving initial asset data, receiving by an enterprise asset management sub-system (EAM) monitoring data, determining by the EAM that a plurality of changed attribute values have changed, storing in the EAM changed asset data, and taking a responsive action by the EAM.