Described herein are systems, methods, and non-transitory computer readable media for triggering a sensor operation of a second sensor (e.g., a camera) based on a predicted time of alignment with a first sensor (e.g., a LiDAR), where operation of the second sensor is simulated to determine the predicted time of alignment. In this manner, the sensor data captured by the two sensors is ensured to be substantially synchronized with respect to the physical environment being sensed. This sensor data synchronization based on predicted alignment of the sensors solves the technical problem of lack of sensor coordination and sensor data synchronization that would otherwise result from the latency associated with communication between sensors and a centralized controller and/or between sensors themselves.

A priori geo-referenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the geo-referenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

An information providing device includes an information acquisition part configured to acquire environment information which indicates a growth environment of grass in a predetermined area and state information which indicates a growth state of the grass in the predetermined area, and an information generating part configured to determine a good area which indicates an area in which the growth state of the grass in the predetermined area is good and a bad area which indicates an area in which the growth state of the grass in the predetermined area is bad based on the state information, and configured to generate management information which indicates a content of treatments to be performed with respect to the good area and the bad area based on the environment information.

An information processing apparatus includes an n-th parameter adjuster and an (n+1)-th parameter adjuster. The n-th parameter adjuster adjusts an n-th parameter set so that an n-th evaluation value set based on the n-th parameter set approaches an n-th target value set. The (n+1)-th parameter adjuster adjusts an (n+1)-th parameter set so that an (n+1)-th evaluation value set based on the (n+1)-th parameter set approaches an (n+1)-th target value set. In addition, the n-th parameter adjuster acquires, based on initial value set or search value set of the n-th parameter set, an n-th actual measured value set or an n-th predicted value set, acquires an (n+1)-th target value set based on the initial value set or the search value set of the n-th parameter set, and searches for the n-th parameter set that optimizes the (n+1)-th target value set under a restriction that the n-th evaluation value set approaches the n-th target value set using the acquired n-th actual measured value set or the n-th predicted value set and the acquired (n+1)-th target value set.

A control apparatus plans a movement route of a moving body based on a recognition result of an exterior environment of the moving body; corrects the planned movement route, based on a recognition result of an obstacle in the exterior environment of the moving body; and controls the movement of the moving body based on the corrected movement route. The recognition result of the obstacle includes a recognition result of a side portion of the obstacle with respect to the movement route of the moving body, and in the correction, the planned movement route is corrected based on a correction amount continuously obtained based on the recognition result of the obstacle associated with advancement of the moving body.

The present disclosure provides a system and a method for controlling semiconductor manufacturing equipment. The system includes a sensor, a sensor interface, and an analysis unit. The sensor provides a sensor signal. The sensor interface receives the sensor signal and generates an input signal for a database server. A front-end subsystem receives the input signal from the database server and performs a comparison process to generate a data signal. A calculation subsystem performs an artificial intelligence analytical process to generate an optimal parameter set and a simulated result map according to the data signal. A message and tuning subsystem generates an alert signal and a feedback signal according to the optimal parameter set and the simulated result map, and the message and tuning subsystem transmits the alert message to a user of the semiconductor manufacturing equipment.

A preemptive system and method for determining one or more actions or stimuli according to input sensor data without explicit input from the user. The sensors are networked in an edge computing environment, which supports transmission and analysis of large amounts of data locally. Such an edge computing environment avoids the drawbacks of transmitting large amounts of data remotely. The edge computing environment is able to communicate remotely with a networked computer for further analysis assistance, for example for receiving previously trained AI models.

This control device 10 comprises a model construction unit 11 that constructs a model for simulating a control object 20, a problem subdivision unit 12 that subdivides the model constructed by the model construction unit 11, a control measure calculation unit 13 that predicts the future status of the control object 20 using the model subdivided by the problem subdivision unit 12 and that calculates a control measure for the control object 20 on the basis of the predicted future status, and an operation command generation unit 14 that generates operation commands to the control object 20 on the basis of the control measure calculated by the control measure calculation unit 13.

A method for controlling a process within a system, in particular a grinding process in a grinding device, comprising the following steps: .detecting 1 of state variables (s.sub.t) of the system; creating 2 of at least two process models (PM), each describing the effects of actuating actions (a.sub.t) on the state variables (s.sub.t) of the system, wherein the structure of the at least two process models (PM) differs from each other; controlling 3 of the process within the system by executing actuating actions (a.sub.t) under consideration of predefined control objectives and the process model, which currently provides the best prediction for the process running in the system. A device for carrying out the method according to the invention.

Embodiments of the present invention provide a method of controlling a vehicle, comprising predicting a first parameter of a vehicle state at each of a plurality of points in time in dependence on a first parameter of a current vehicle state and a first model associated with the vehicle, predicting a second parameter of the vehicle state at each of the plurality of points in time in dependence on a second parameter of the current vehicle state, the predicted first parameter of the vehicle state and a second model associated with the vehicle, and determining one or more control inputs for the vehicle at each of the points in time in dependence on the predicted first and second parameters of the vehicle state at each of the plurality of points in time and desired first and second parameters of the vehicle state at each of the plurality of points in time.