Embodiments are directed to a system and methods for ingesting location event data and encoding location data in the event data to a proximity. The encoding includes geohashing latitude and longitude for each event to a proximity for analysis and throughput.

An operation of a processing machine with redundant actuators is controlled according to a reference trajectory by selecting, from a set of points forming a segment of the reference trajectory to be processed for a period of time, a subset of points corresponding to a fraction of the period of time. The subset of points is selected such that the redundant actuators are capable to position the worktool at each point in the subset within the period of time and are capable to maintain the worktool at the last point of the subset after the period of time while satisfying constraints on motion of the redundant actuators. The segment of the reference trajectory is modified in the time domain and the control inputs for controlling the motion of the redundant actuators are determined using the modified segment of the reference trajectory.

Embodiments are directed to a system and method for ingesting location event data and identifying a journey for a vehicle from the event data. The journey identification includes identifying whether a given vehicle is moving for purposes of driving to a journey destination.

Embodiments are directed to a system and method for ingesting location event data and identifying a journey for a vehicle from the event data. The journey identification includes identifying whether a given vehicle is moving for purposes of driving to a journey destination.

Embodiments are directed to a system and methods for ingesting location event data and encoding location data in the event data to a proximity. The encoding includes geohashing latitude and longitude for each event to a proximity for analysis and throughput.

A simulation apparatus includes a machine learning device for learning a change in a machining route in machining of a workpiece. The machine learning device observes data indicating the changed machining route and data indicating a machining condition of the workpiece as a state variable, and also acquires determination data for determining whether or not a cycle time obtained by simulation using the changed machining route is appropriate, and learns by associating the machining condition of the workpiece with the change in the machining route, using the state variable and the determination data.

A simulation apparatus includes a machine learning device for learning a change in a machining route in machining of a workpiece. The machine learning device observes data indicating the changed machining route and data indicating a machining condition of the workpiece as a state variable, and also acquires determination data for determining whether or not a cycle time obtained by simulation using the changed machining route is appropriate, and learns by associating the machining condition of the workpiece with the change in the machining route, using the state variable and the determination data.

A processing device is provided and electrically connected to a drive controller for driving a control object. The drive controller has a predetermined control structure that includes a feedback system and a control model part, and that enables model follow-up control according to the control models, and has the predetermined control structures corresponding to the control objects, respectively. The processing device determines a common control gain to set a predetermined control gain in the control model part of each predetermined control structure corresponding to the control objects to the common control gain for all the control model parts when synchronous control of the control objects is performed, and instructs the drive controller to set the common control gain for the control model part corresponding to each predetermined control structure.

The trajectory planning system for integrating a computer numerical control (CNC) machine, trajectory planning device, trajectory planning method, and computer program thereof are provided. The aforementioned trajectory planning device includes a determining module. The determining module configures the related processing speed or processing acceleration based on type of the processing segment so as to provide a processing setting. Furthermore, the aforementioned trajectory planning device determines the processing segment whether the linear or circular segment and plans the tangent or normal acceleration of related processing segment so as to optimize the processing setting and finish the high order processing trajectory planning.

A processing device is provided and electrically connected to a drive controller for driving a control object. The drive controller has a predetermined control structure that includes a feedback system and a control model part, and that enables model follow-up control according to the control models, and has the predetermined control structures corresponding to the control objects, respectively. The processing device determines a common control gain to set a predetermined control gain in the control model part of each predetermined control structure corresponding to the control objects to the common control gain for all the control model parts when synchronous control of the control objects is performed, and instructs the drive controller to set the common control gain for the control model part corresponding to each predetermined control structure.