Abstract: A device intelligence architecture configures and controls on-site devices and performs environment monitoring to facilitate effective device functionality. The architecture facilitates efficient use of devices (e.g., robotics) in an unstructured and dynamic environment, which will allow deployments in many more environments than is currently possible. The architecture stores and shares information between segregated devices to avoid the silo effect of vendor specific stacks. The architecture also models capabilities of devices and maps actions to intelligence packages to deploy a specific intelligence package to the device. The architecture also implements distributed processing. For instance, computationally intensive tasks may be offloaded to the back end processing, with action updates resulting from the processing pushed to the devices.

Abstract: Unmanned vehicle (UV) movement and data control may include controlling a UV according to a movement plan. Formatted movement and status metadata may be received from a sensor of the UV during movement of the UV. The movement and status metadata may include time and location information for the UV during the movement of the UV. An unformatted data stream may be received from the sensor of the UV. The time and location information may be injected into metadata of the unformatted data stream to generate a time and location correlated (TLC) stream. The TLC stream may be analyzed to identify an event related to the UV, and a notification related to the event may be generated.

Abstract: Unmanned vehicle (UV) movement and data control may include controlling a UV according to a movement plan. Formatted movement and status metadata may be received from a sensor of the UV during movement of the UV. The movement and status metadata may include time and location information for the UV during the movement of the UV. An unformatted data stream may be received from the sensor of the UV. The time and location information may be injected into metadata of the unformatted data stream to generate a time and location correlated (TLC) stream. The TLC stream may be analyzed to identify an event related to the UV, and a notification related to the event may be generated.

Abstract: Unmanned vehicle (UV) control may include receiving a UV work order and generating a mission request based on the UV work order. The mission request may identify an objective of a mission, assign a UV and a sensor to the mission from a fleet of UVs and sensors, and assign a first movement plan to the mission based on the identified objective of the mission. The assigned UV may be controlled according to the assigned first movement plan, and communication data may be received from the assigned sensor. The communication data may be analyzed to identify an event related to the mission. The identified event and the first movement plan may be analyzed to assign a second movement plan to the mission based on the analysis of the identified event and the first movement plan to meet the identified objective of the mission.

Abstract: Unmanned vehicle (UV) control may include receiving a UV work order and generating a mission request based on the UV work order. The mission request may identify an objective of a mission, assign a UV and a sensor to the mission from a fleet of UVs and sensors, and assign a first movement plan to the mission based on the identified objective of the mission. The assigned UV may be controlled according to the assigned first movement plan, and communication data may be received from the assigned sensor. The communication data may be analyzed to identify an event related to the mission. The identified event and the first movement plan may be analyzed to assign a second movement plan to the mission based on the analysis of the identified event and the first movement plan to meet the identified objective of the mission.

Abstract: Unmanned vehicle (UV) control may include receiving a UV work order and generating a mission request based on the UV work order. The mission request may identify an objective of a mission, assign a UV and a sensor to the mission from a fleet of UVs and sensors, and assign a first movement plan to the mission based on the identified objective of the mission. The assigned UV may be controlled according to the assigned first movement plan, and communication data may be received from the assigned sensor. The communication data may be analyzed to identify an event related to the mission. The identified event and the first movement plan may be analyzed to assign a second movement plan to the mission based on the analysis of the identified event and the first movement plan to meet the identified objective of the mission.

Abstract: A device intelligence architecture configures and controls on-site devices and performs environment monitoring to facilitate effective device functionality. The architecture facilitates efficient use of devices (e.g., robotics) in an unstructured and dynamic environment, which will allow deployments in many more environments than is currently possible. The architecture stores and shares information between segregated devices to avoid the silo effect of vendor specific stacks. The architecture also models capabilities of devices and maps actions to intelligence packages to deploy a specific intelligence package to the device. The architecture also implements distributed processing. For instance, computationally intensive tasks may be offloaded to the back end processing, with action updates resulting from the processing pushed to the devices.

Abstract: Unmanned vehicle (UV) control may include receiving a UV work order and generating a mission request based on the UV work order. The mission request may identify an objective of a mission, assign a UV and a sensor to the mission from a fleet of UVs and sensors, and assign a first movement plan to the mission based on the identified objective of the mission. The assigned UV may be controlled according to the assigned first movement plan, and communication data may be received from the assigned sensor. The communication data may be analyzed to identify an event related to the mission. The identified event and the first movement plan may be analyzed to assign a second movement plan to the mission based on the analysis of the identified event and the first movement plan to meet the identified objective of the mission.