A robot fleet management platform includes one or more processors configured to execute instructions. The instructions include receiving a job request comprising information descriptive of job deliverable and request-specific constraints for delivering the job deliverable. The instructions include applying content and structural filters to content received in association with a job request to identify portions thereof suitable for robot automation. The instructions include establishing a set of robot tasks, each defining at least a type of robot and a task objective, based on the portions of the job request that are suitable for robot automation and meet a first fleet objective. The instructions include applying fleet configuration services to the job content and the set of robot tasks to produce a fleet resource configuration data structure for the job request that associates at least one robot operating unit with each task in the set of tasks and robot adaptation instructions.

A method of transfer learning for a specific production process of an industrial plant includes providing data templates defining expected data for a production process, and providing plant data, wherein the data templates define groupings for the expected data according to their relation in the industrial plant; determining a process instance and defining a mapping with the plant data; determining historic process data; determining training data using the determined process instance and the determined historic process data, wherein the training data comprises a structured data matrix, wherein columns of the data matrix represent the sensor data that are grouped in accordance with the data template and wherein rows of the data matrix represent timestamps of obtaining the sensor data; providing a pre-trained machine learning model using the determined process instance; and training a new machine learning model using the provided pre-trained model and the determined training data.

A method and associated system for applying an optimized processing treatment to items in an industrial treatment line are described. First, settings for a value of a working parameter in the treatment line are defined. The settings are associated with value ranges of a parameter representative of an external property of items. Then, a value of the parameter representative of the external property of the item is measured. A setting for the value of the working parameter associated with a value range from the set of value ranges comprising the measured value is compared to the current setting of the working parameter. When a difference is detected between the associated setting and the current setting, the current setting is changed to the associated setting.

Computation is made of a production plan and sequence satisfying constraint conditions using an interaction model. A system includes a storage device to store management information about specifications on each of a plurality of things to be produced and a computational device to compute a planned sequence of things to be produced in a production process to produce the plurality of things using an interaction model, based on the management information. In this system, the computational device computes an interaction model in which an assignment event of a sequential position in a production process to each of the things is assumed as a variable and a constraint condition regarding the production process is set as strength of an interaction between variables.

Machine elements can be displaced along disjoined path sections by position-controlled machine axes. Movement guidance of the machine elements from the end of a path section to the beginning of a directly following path section along a previously unknown path is provided, wherein location, velocity and acceleration are continuous along the previously unknown path and at the transitions between the path sections and the previously unknown path. Velocity, acceleration and jerk are limited. A preliminary axis guidance and a corresponding required axis time is determined for each of the axes. A greatest required axis time is determined therefrom which is then set as a final axis guidance for this axis. For other axes whose preliminary axis times are smaller than the greatest required axis time, the respective preliminary axis guidance is matched to the greatest required axis time, which is then adopted for the other axes as final axis guidance.

A robot fleet management platform includes a job configuration system that determines tasks to be performed by robots of a robot fleet based on a job request and a first fleet objective. A proxy service applies fleet configuration services to the tasks to produce a data structure. An intelligence layer activates intelligence services to produce a robot task and associated contextual information that facilitates robot selection and task ordering. A job workflow system generates a workflow defining a performance order of the tasks. A workflow simulation system simulates performance of the job request based on the workflow to recursively redefine the tasks, the data structure, or the workflow until the simulation result satisfies a second fleet objective. In response to the simulation result satisfying the set of fleet objectives, a plan generator generates a job execution plan based on the set of robot tasks, the data structure, and the workflow.

Techniques are disclosed for designing manufacturing facilities. A design application imports a computer-aided design (CAD) model and metadata associated with a manufacturing facility to generate a data set that specifies geometric and manufacturing constraints of the manufacturing facility. The design application performs optimization operations based on the data set to identify one or more high-performing designs that assign components to docks in the manufacturing facility. The optimization operations can include genetic optimization operations that generate multiple generations of designs, each of which is evaluated based on a travel distance, congestion, and number of turns associated with paths traversed by components for the design.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train one or more machine learning models to recognize an object.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a variable lighting assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a control system configured to adjust the variable lighting assembly. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train a set of machine learning models to control the variable focus liquid lens optical assembly to optimize collection of data for processing by the set of machine learning models.

An autonomous additive manufacturing platform includes sensors positioned in, on, and/or near a part and configured to collect sensor data related to the part. An adaptive intelligence system is configured to receive the sensor data from the sensors. The adaptive intelligence system includes a machine learning system configured to input the sensor data as training data into one or more machine learning models. The machine learning models are configured to transform the sensor data into simulation data. A digital twin system is configured to create a part twin based on the simulation data. The part twin provides for representation of the part and simulation of a possible future state of the part via the simulation data. An artificial intelligence system is configured to execute simulations on the digital twin system. The machine learning models are utilized to make classifications, predictions, and other decisions relating to the part.