A dynamic planning controller receives a maneuver for a robot and a current state of the robot and transforms the maneuver and the current state of the robot into a nonlinear optimization problem. The nonlinear optimization problem is configured to optimize an unknown force and an unknown position vector. At a first time instance, the controller linearizes the nonlinear optimization problem into a first linear optimization problem and determines a first solution to the first linear optimization problem using quadratic programming. At a second time instance, the controller linearizes the nonlinear optimization problem into a second linear optimization problem based on the first solution at the first time instance and determines a second solution to the second linear optimization problem based on the first solution using the quadratic programming. The controller also generates a joint command to control motion of the robot during the maneuver based on the second solution.

A dynamic planning controller receives a maneuver for a robot and a current state of the robot and transforms the maneuver and the current state of the robot into a nonlinear optimization problem. The nonlinear optimization problem is configured to optimize an unknown force and an unknown position vector. At a first time instance, the controller linearizes the nonlinear optimization problem into a first linear optimization problem and determines a first solution to the first linear optimization problem using quadratic programming. At a second time instance, the controller linearizes the nonlinear optimization problem into a second linear optimization problem based on the first solution at the first time instance and determines a second solution to the second linear optimization problem based on the first solution using the quadratic programming. The controller also generates a joint command to control motion of the robot during the maneuver based on the second solution.

A dynamic planning controller receives a maneuver for a robot and a current state of the robot and transforms the maneuver and the current state of the robot into a nonlinear optimization problem. The nonlinear optimization problem is configured to optimize an unknown force and an unknown position vector. At a first time instance, the controller linearizes the nonlinear optimization problem into a first linear optimization problem and determines a first solution to the first linear optimization problem using quadratic programming. At a second time instance, the controller linearizes the nonlinear optimization problem into a second linear optimization problem based on the first solution at the first time instance and determines a second solution to the second linear optimization problem based on the first solution using the quadratic programming. The controller also generates a joint command to control motion of the robot during the maneuver based on the second solution.

An engineering system for planning an automation entity, wherein the engineering system is configured to store and display first objects for at least one automation device and second objects for at least one operator system in a structured manner in accordance with a technological hierarchy, and to generate from the first objects first runtime data for loading into the automation device and from the second objects second runtime data for loading into the operator system, and wherein components of the first runtime data are operatively connected to components of the second runtime data such that it is possible to distinguish between a planning error and the consequences of a delta loading of runtime data.

An engineering system for planning an automation entity, wherein the engineering system is configured to store and display first objects for at least one automation device and second objects for at least one operator system in a structured manner in accordance with a technological hierarchy, and to generate from the first objects first runtime data for loading into the automation device and from the second objects second runtime data for loading into the operator system, and wherein components of the first runtime data are operatively connected to components of the second runtime data such that it is possible to distinguish between a planning error and the consequences of a delta loading of runtime data.

This disclosure describes an apparatus and method for monitoring distributed industrial control systems using a flexible hierarchical model. A method includes providing, a plurality of hierarchically-organized industrial control devices in an industrial control network. The method includes executing, by each of a plurality of the industrial control devices, a publisher application or a subscriber application that is associated with a hierarchical level of the industrial control network. The method includes associating each publisher application or subscriber application with an application hierarchy property that identities the associated hierarchical level in the industrial control network. The method includes executing a process, by one of the industrial control devices according to the application hierarchy properties.

Methods, apparatus, systems, and computer-readable media are provided for determining and assigning intermediate handoff checkpoints for low-resolution robot planning. In various implementations, a global path planner may identify a task to be performed by a robot in an environment. In various implementations, the global path planner may determine, based at least in part on one or more attributes of the environment or the task, an intermediate handoff checkpoint for the robot to reach by a scheduled time while the robot performs the task. In various implementations, the global path planner may determine that a measure of reactivity that would be attributable to the robot upon the robot being assigned the intermediate handoff checkpoint satisfies a reactivity threshold. In various implementations, the global path planner may provide, to a local path planner associated with the robot, data indicative of the intermediate handoff checkpoint.

A dynamic planning controller receives a maneuver for a robot and a current state of the robot and transforms the maneuver and the current state of the robot into a nonlinear optimization problem. The nonlinear optimization problem is configured to optimize an unknown force and an unknown position vector. At a first time instance, the controller linearizes the nonlinear optimization problem into a first linear optimization problem and determines a first solution to the first linear optimization problem using quadratic programming. At a second time instance, the controller linearizes the nonlinear optimization problem into a second linear optimization problem based on the first solution at the first time instance and determines a second solution to the second linear optimization problem based on the first solution using the quadratic programming. The controller also generates a joint command to control motion of the robot during the maneuver based on the second solution.

Methods, apparatus, systems, and computer-readable media are provided for determining and assigning intermediate handoff checkpoints for low-resolution robot planning. In various implementations, a global path planner may identify a task to be performed by a robot in an environment. In various implementations, the global path planner may determine, based at least in part on one or more attributes of the environment or the task, an intermediate handoff checkpoint for the robot to reach by a scheduled time while the robot performs the task. In various implementations, the global path planner may determine that a measure of reactivity that would be attributable to the robot upon the robot being assigned the intermediate handoff checkpoint satisfies a reactivity threshold. In various implementations, the global path planner may provide, to a local path planner associated with the robot, data indicative of the intermediate handoff checkpoint.