Apparatus and methods related to routing robots are provided. A roadmap of an environment that includes first and second robots can be received. The roadmap can be annotated with unidirectional lanes connecting conflict regions, where each lane ends so to avoid blocking a conflict region. First and second routes for the respective uses of the first and second robots can be determined, where both the first and second routes include a first lane connected to a first conflict region. A first, higher priority and a second, lower priority can be assigned to the respective first and second robots. It can be determined that the second robot following the second route will block the first robot on the first lane. Based on the first priority being higher than the second priority, the computing device can alter the second route to prevent the second robot from blocking the first robot.

Embodiments are directed towards automatically identifying, configuring, monitoring, controlling, managing, and maintaining a machine, via collection computers in communication with the machine components. The components include ID Tags that store identification data, such as a component type and a unique identifier. Interrogation of the ID Tags enables the automatic identification and configuration of the machine. Data provided by the sensors, during usage of the machine, enables the remote monitoring and managing of the usage, as well as maintaining of the machine. Machine maintenance includes automatically predicting and scheduling the replacement of various components. Embodiments provide suggestions for suppliers of replacement components, as well as suggestions for alternative components that may be better optimized for the configuration and usage of the machine. Heuristics and crowd-generated data, via machine user social networks, inform predictive analyses employed to automatically identify, configure, manage, operate, and maintain the machine.

Various embodiments of the present technology generally relate to unmanned aerial vehicle (UAV) scale rendered analysis, orthomosaic, and 3D mapping and landing platform systems. More specifically, some embodiments relate to systems, methods, and means for the collection and processing of images captured during a UAV flight sequence. In some embodiments, the UAV landing platform retrieves flight information and initial map information over a unidirectional virtual private network from a multitenant cloud-based scheduling application. The UAV landing platform sends the initial map information to a UAV over a WiFi, Bluetooth, or radio frequency network and initiates a drone flight sequence once the drone flight sequence has been approved by a local user. The UAV landing platform receives property image data from a UAV after a UAV flight sequence has ended and transmits the received property image data back to the cloud application.

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 cigarette device management system includes an information management module, and a maintenance prediction module, a device state acquisition module and a purchase statistics module which are in communication connection with it. The maintenance prediction module is built by an artificial neural network model and is configured to predict a failure interval. The device state acquisition module acquires a device operating parameter and an operating duration of the device in real time, and compares the device operating parameter with a safety parameter range. In accordance with the system, the maintenance timing of the device can be predicted, the purchase quantity of the spare part and the purchase timing of the spare part also can be predicted.

An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

In examples, a method of controlling customer access to an assay system comprises (a) receiving a system identifier; (b) identifying said system identifier; and (c) utilizing information obtained from the system identifier to perform one or more operations selected from: (i) enabling full access to said system and/or a consumable used in said system; (ii) enabling partial access to said system and/or a consumable used in said system; or (iii) denying access to said system and/or a consumable used in said system.

In a production system, a control unit controls a first apparatus configured to start to operate after a waiting time and a second apparatus configured to starting to operation after a waiting time. A first sensor outputs a value that changes in response to operation of the first apparatus. A second sensor outputs a value that changes in response to operation of the second apparatus. The control unit compares a first time period from a starting of the operation of the first apparatus to an occurrence of a change in the value of the first sensor with a predetermined first threshold value, and compares a second time period from a starting of the operation of the second apparatus to an occurrence of a change in the value of the second sensor with a predetermined second threshold value.

A computer-implemented method for automating the development of industrial machine learning applications includes one or more sub-methods that, depending on the industrial machine learning problem, may be executed iteratively. These sub-methods include at least one of a method to automate the data cleaning in training and later application of machine learning models, a method to label time series (in particular signal data) with help of other timestamp records, feature engineering with the help of process mining, and automated hyper-parameter tuning for data segmentation and classification.

Techniques for inspection of articles having multiple features of one or more types are disclosed. Input data indicative of one or more selected features of interest is used for inspection by a given inspection system characterized by associated imaging configuration data. The input data is analyzed to extract information regarding one or more inspection tasks, and an inspection plan data is generated, to be used as a recipe data for operation of the given inspection system to provide measured data in accordance with the one or more inspection tasks. Selected inspection mode data corresponding to the inspection task data may be retrieved from a database system and utilized to generate the inspection plan data.