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.

Techniques are disclosed for task priority scheduling and resource allocation of autonomous agents. The scheduling may utilize sensor data characteristics to facilitate scheduling decisions. The present disclosure also provides refinement of task priority scheduling utilizing fleet management information-based scene and environment information, such as by using information available to the fleet management controller.

Generally discussed herein are devices, systems, and methods for clustering based on context triggered piecewise hashing (CTPH). A method can include determining a first index of a first CTPH string of the file. The first index can include contiguous bits of the CTPH string. The first index can be smaller than the CTPH string, such as to be a proper subset of the CTPH string. The method can include determining the first index matches a second index of a cluster of files and in response to determining the first index matches the second index of the cluster, associating the file with the cluster. The method can include determining that the file includes malware based on the cluster.

The invention relates to a method for the distributed calculation of calculation tasks by means of field devices of an industrial plant, wherein a plurality of field devices are coupled to a task distribution unit by means of a data link, the field devices effect a control of the industrial plant in an operating state in each case, at least one of the field devices receives a calculation task from the task distribution unit in an idle state and changes to a calculation state in which the calculation task is processed.

An approach for fully automating the use of robotic devices in a laboratory workflow includes defining sequences for automating tasks and equipment involved in such a workflow, and calculating a path for each sequence that resolves get, handoff, and placement procedures. The approach develops a schedule that executes resolved pathways in and between each device. The approach is provided with an easy-to-use interface, in which a user drags and drops devices to automatically configure them, defines operations to be performed by these devices, and then runs the laboratory workflow. The interface also provides the ability to monitor progress of the workflow, and make modifications and adjustments as needed.

A synchronization primitive provides robots with locks, monitors, semaphores, or other mechanisms for reserving temporary access to a shared limited set of resources required by the robots in performing different tasks. Through non-conflicting establishment of the synchronization primitives across the set of resources, robots can prioritize the order with which assigned tasks are completed and minimize wait times for resources needed to complete each of the assigned tasks, thereby maximizing the number of tasks simultaneously executed by the robots and optimizing task completion. The synchronization primitives and resulting resource allocation can be implemented with a centralized coordinator or with peer-to-peer robotic messaging, whereby private keys and blockchains secure the precedence and establishment of synchronization primitives by different robots. Moreover, synchronization primitives can be established with queues to further optimize the immediate and future allocation of resources to different robots.

This application discloses a computing system implementing a line balancing tool to generate a structured bill of materials for a wire harness based on a harness design and available fabrication processes. The computing system can decompose the structured bill of materials into tasks and assign the tasks to workstations in a production line configured to manufacture the wire harness. The computing system can determine dependencies between a plurality of the tasks and verify the tasks assigned to the workstation conform to the dependencies between the plurality of the tasks. The dependencies can indicate an order for performance of the operations associated with the tasks. The computing system can identify unassigned tasks capable of assignment to one or more of the workstations and determine which of the workstations the unassigned assembly tasks, if assigned, would conform with the dependencies between the plurality of the assembly tasks.

A method, computer system, and computer program product for optimizing a number of robots for operation of a process at a target system. The method may include providing a plurality of available robots to carry out tasks in the process at the target system. The method may monitor the target system by carrying out the process or part of the process with a varying number of robots to determine the processor utilization whilst the robots are executing a varying number of tasks. The method may balance process constraints of the execution of the process with physical system constraints of the target system by measuring a relationship between a number of tasks at a transactional level and the processor utilization. The method may output the optimized number of robots to be allocated for the process or part of the process.

A robot cluster scheduling system includes a user layer, an intermediate layer, an application layer, a plug-in layer and a data persistence layer. The intermediate layer includes a processor mapping module and a state acquisition module. The application layer includes a task scheduling module and a traffic scheduling module. The plug-in layer includes a task solving engine and a traffic planning engine. The task solving engine is configured to determine a target robot according to a parameter of a task and state data. The traffic planning engine is configured to determine a target route. The task solving engine and the traffic planning engine each provide an application programming interface (API).

A system and a method are disclosed where an autonomous robot captures an image of an object to be transported from a source to a destination. The robot generates a bounding box within the image surrounding the object. The robot applies a machine-learned model to the image with the bounding box, the machine-learned model configured to identify an object type of the object, and to identify features of the object based on the identified object type and the image. The robot determines which of the identified features of the object are visible to the autonomous robot, and determines a three-dimensional pose of the object based on the features determined to be visible to the autonomous robot.