A method of controlling at least one of a first robot and a second robot to collaborate within a system, the first robot and the second robot being physically separate to one another, the method including: receiving sensed data associated with the second robot; determining position and/or orientation of the second robot using the received sensed data; determining an action for the second robot using the determined position and/or orientation of the second robot; and providing a control signal to the second robot to cause the second robot to perform the determined action to collaborate with the first robot.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.

An exemplary multi-robot system can include, for example, a first robot(s), which can include a communication arrangement and a sensor arrangement configured to detect a presence of an object(s) within a predetermined distance from the first robot(s), and determine a distance from the first robot(s) to the object(s), where the first robot(s) can broadcast a query to the object(s) using the communication arrangement, identify the object(s) as a second robot(s) or a non-robot based on a response received from the object(s). The sensor arrangement can be a Light Detection and Ranging (LiDAR) sensor arrangement. The LiDAR sensor arrangement can be a two-dimensional LiDAR sensor arrangement.

Approaches presented herein enable maneuvering collaborative robots to rescue persons in a hydrological disaster. A plurality of robots are dispersed in a body of water to spread out and seek victims using cooperative foraging techniques within resource constraints. A location of victims located by a robot using sensing techniques is communicated to other robots. A situational assessment is performed using victim location information to determine a number of robots to deploy to the location. The deployed robots are directed to perform coordinated maneuvers to create a connected floatation unit to support floatation of victims for rescue.

Methods and systems are disclosed to generate and display targeted information related to a plurality of robots working in an operating environment. Plurality of nodes executing at the plurality of robots in communication with plurality of server nodes executing behaviors related to an active plan being executed on the working robots. The nodes running on the robots create Snapshots related to the executing behaviors. Information is then captured based on parent context related to the executing behaviors. The nodes populate a plurality of fields of the Snapshots with values related to at least one or more of captured information, operating environment, and the robots. The Snapshots are closed with a result of the execution of the behaviors. The Snapshots are aggregated and reported by the nodes, as part of the targeted information for display. Customized search queries or visual interfaces can be used to fix or diagnose faults or errors.

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.

A robot fleet management platform includes a resources data store that maintains a robot inventory indicating robots that can be assigned to a robot fleet and, for each respective robot, a set of baseline features of the robot and a respective status. The resources data store maintains a components inventory indicating different components that can be provisioned to one or more multi-purpose robots and, for each component, a respective set of extended capabilities corresponding to the component and a respective status. The robot fleet management platform receives a request for a robotic fleet to perform a job, determines a job definition data structure based on the request, determines a robot fleet configuration data structure corresponding to the job based on the set of tasks and the robot inventory, determines a respective configuration for each assigned robot based on the components inventory, configures the assigned robots, and deploys the robotic fleet.

This disclosure relates to system and method for cleaning surfaces using drones. The method includes identifying, by a first drone, a cleaning strip in contact with a surface. The cleaning strip connects a second drone and a third drone. The second drone is communicatively coupled with the third drone. The first drone is communicatively coupled with each of the second drone and the third drone. The method further includes releasing a cleaning agent at a preconfigured pressure upon a current target region of the surface near the cleaning strip. The method further includes relocating the cleaning strip upon the current target region. The method further includes performing a set of oscillations through the cleaning strip across the current target region. Each of the set of oscillations includes coordinating a displacement of the cleaning strip by a predefined distance, alternating towards each of ends of the cleaning strip.

A robotic fleet management platform includes a resources data store that maintains a fleet resource inventory indicating fleet resources that can be assigned to a robotic fleet and, for each fleet resource, maintenance history, predicted maintenance need, and a preventive maintenance schedule. The platform includes a maintenance management library of fleet resource maintenance requirements for determining maintenance workflows, service actions, and service parts for at least one fleet resource in the fleet resource inventory. The platform calculates predicted maintenance need of a fleet resource based on anticipated component wear and anticipated component failure of the at least one fleet resource according to machine learning-based analysis of the maintenance status data. The platform monitors a health state of the fleet resource based on sensor data. The platform initiates a service action of the at least one item of maintenance for the fleet resource based on the fleet resource maintenance requirements.