Augmenting automation in facilities with robotics and generative artificial intelligence

1 . A method comprising:

receiving, at an edge server, sensor data from sensors in a facility, wherein a sensor plug and play model executing at the edge server dynamically discovers the sensors in the facility;

determining a context of the facility in response to a generative artificial intelligence model at the edge server analyzing the sensor data;

generating, by the generative artificial intelligence model, an action in response to the context, wherein the action is a robot assisted inspection of equipment in the facility;

performing on-the-fly resource reservations of hardware and software to identify reserved resources at the edge server for performing the action;

using the reserved resources at the edge server in providing commands to a robot to perform the action;

determining a context change in the facility in response to the generative artificial intelligence model analyzing the sensor data, wherein the context change includes detection, by a sensor, of a pressure drop on a gauge on a pipeline; and

using the reserved resources in providing updated commands to the robot in response to the context change in the facility, wherein the updated commands include instructing the robot to search and check all gauges in a vicinity of the pipeline.

2 . The method of claim 1 , wherein the sensor data is multimodal data obtained from mobile location sensors and fixed location sensors in the facility.

3 . The method of claim 2 , wherein the mobile location sensors are on robots and drones associated with the facility.

4 . The method of claim 1 , wherein the sensor data is obtained from the robot performing the robot assisted inspection of the equipment in the facility and the action modifies the robot assisted inspection being performed by the robot.

5 . The method of claim 1 , wherein the context change in the facility includes a detected anomaly, physical differences in equipment, or a hazardous condition.

6 . The method of claim 5 , wherein the action further includes providing instructions to a drone to fly over a location where the anomaly is detected and the reserved resources provide commands to the drone to fly over the location and to capture video of the location.

7 . The method of claim 1 , further comprising:

displaying a report with the context and the action.

8 . The method of claim 1 , wherein using the reserved resources in providing the commands to the robot further includes dynamic spawning of services on heterogeneous hardware at the edge server.

9 . The method of claim 1 , wherein the gauge on the pipeline is connected to an exit of a compressor, and wherein the updated commands include instructing the robot to search and check all the gauges in a vicinity of the compressor.

10 . A device, comprising:

a memory to store data and instructions; and

a processor operable to communicate with the memory, wherein the processor is operable to:

receive, at an edge server, sensor data from sensors in a facility, wherein a sensor plug and play model executing at the edge server dynamically discovers the sensors in the facility;

determine a context of the facility in response to a generative artificial intelligence model at the edge server analyzing the sensor data;

generate, by the generative artificial intelligence model, an action in response to the context;

perform on-the-fly resource reservations of hardware and software to identify reserved resources at the edge server for performing the action;

use the reserved resources at the edge server in providing commands to equipment in the facility to perform the action;

determine a context change in the facility in response to the generative artificial intelligence model analyzing the sensor data, wherein the context change includes a hazardous condition; and

use the reserved resources in providing updated commands to the equipment in response to the context change in the facility, wherein the updated commands instruct the equipment to perform an emergency shutdown of the equipment.

11 . The device of claim 10 , wherein the sensor data is multimodal data obtained from mobile location sensors and fixed location sensors in the facility.

12 . The device of claim 11 , wherein the mobile location sensors are on robots and drones associated with the facility.

13 . The device of claim 10 , wherein the processor is further operable to obtain the sensor data from a robot performing an inspection of the equipment in the facility.

14 . The device of claim 10 , wherein the context identifies a-context change in the facility includes a detected anomaly or physical differences in equipment.

15 . The device of claim 14 , wherein the action further includes providing instructions to a drone to fly over a location where the anomaly is detected to capture video of the location.

16 . The device of claim 10 , wherein the processor is further operable to display, on a user interface, a report with the context and the action.

17 . The device of claim 10 , wherein the processor is further configured to dynamically spawn services on heterogeneous hardware at the edge server to use in providing the commands to the equipment in the facility to perform the action.

18 . A non-transitory computer-readable storage medium including instructions that, when executed by a processor, cause the processor to:

receive, at an edge server, sensor data from sensors in a facility, wherein a sensor plug and play model executing at the edge server dynamically discovers the sensors in the facility;

determine a context of the facility in response to a generative artificial intelligence model at the edge server analyzing the sensor data;

generate, by the generative artificial intelligence model, an action in response to the context, wherein the action is a robot assisted inspection of equipment in the facility;

perform on-the-fly resource reservations of hardware and software to identify reserved resources at the edge server for performing the action;

use the reserved resources at the edge server in providing commands to a robot to perform the action, wherein using the reserved resources at the edge server includes dynamic spawning of services on heterogeneous hardware at the edge server;

determine a context change in the facility in response to the generative artificial intelligence model analyzing the sensor data, wherein the context change includes detection, by a sensor, of a hydrogen sulfide gas concentration above a normal value; and

use the reserved resources in providing updated commands to the robot in response to the context change in the facility, wherein the updated commands include re-routing a path of the robot to search for a cause of the hydrogen sulfide gas concentration above the normal value.