An EUV stocker and an EUV pod device is disclosed. The EUV stocker includes an AI driven dynamic control circuitry, an AI controlled safety interlock, and an independent air return control device. The EUV stocker includes a Mass Flow Control (MFC) that operates in conjunction with one or more valves. The EUV stocker further includes a hydrocarbon detecting assembly, oxygen detecting assembly, pressure detecting assembly, and temperature detecting assembly and more to maintain the required condition within the EUV stocker. The EUV stocker also includes automated transportation devices such as AMHS, OHT, MR, AGV, RGV, or the like to provide a safe EUV mask storage environment for operators.

An industrial robot adapted for operation in a factory environment includes: sensors, actuators, a robot controller and a wireless interface configured to establish a sidelink to a further industrial robot or a group of industrial robots after a successful proximity verification. The industrial robot is configured to participate in execution of a utility task, which is carried out in collaboration with the further industrial robot or at least some members of the group of industrial robots, said collaboration including an exchange of operational data over the sidelink. An example utility task is the coordinated transfer of an object by multiple participating industrial robots. Another example is the collecting of map information by multiple participating industrial robots.

In general, various embodiments of the present disclosure provide methods, systems, computer-readable medium, and/or the like for coordinating movement of items within a facility. In various embodiments, a method is provided that comprises: obtaining requests that involve moving items between locations found at a facility; obtaining geographical data that comprises amounts of time involved in moving the items between the locations; obtaining state data that comprises current locations of resources available to execute the requests; and generating a movement plan that identifies a specific resource for each request by: processing the requests via iterations, wherein each iteration involves: processing the requests yet to be assigned a specific resource to identify a highest priority request; generating, based on the geographical and state data, an estimated amount of time for each eligible resource; and identifying, based on the estimated amount of time, the specific resource to execute the highest priority request.

An EUV stocker and an EUV pod device is disclosed. The EUV stocker includes an AI driven dynamic control circuitry, an AI controlled safety interlock, and an independent air return control device. The EUV stocker includes a Mass Flow Control (MFC) that operates in conjunction with one or more valves. The EUV stocker further includes a hydrocarbon detecting assembly, oxygen detecting assembly, pressure detecting assembly, and temperature detecting assembly and more to maintain the required condition within the EUV stocker. The EUV stocker also includes automated transportation devices such as AMHS, OHT, MR, AGV, RGV, or the like to provide a safe EUV mask storage environment for operators.

Provided are an omnidirectional moving robot device, and a system and method for object conveyance using a plurality of moving robot devices. The omnidirectional moving robot device includes a sensor, a traveling unit including an omnidirectional wheel disposed in a housing, and a control unit configured to transmit a traveling command signal to the traveling unit by using data measured by the sensor.

A robot arrangement is disclosed including one or more robots for moving a component into an assembly position adjacent a fixed structure. The robots are configured to operate collectively to move the component from an initial position located in a coarse adjustment zone into a fine rotational adjustment zone within a set distance of the fixed structure. The coarse adjustment zone is at least the set distance from the fixed structure; and in the fine rotational adjustment zone, robots are configured to collectively perform a rotational alignment cycle to rotationally align the component with the assembly position of the component ready for joining the component to the fixed structure and, upon completion of the rotational alignment cycle, collectively perform a translational movement to move the component into the assembly position.

A binding system including: a binding device including a binding machine that binds reinforcing bars with a wire, and a transfer robot that moves the binding machine to a binding position by a relative movement between the binding machine and the reinforcing bars; an information acquisition unit configured to acquire binding related information related to an operation of binding the reinforcing bars with the wire; and a position information acquisition unit configured to acquire position information of the binding position in a manner associable with the binding related information for the binding position acquired by the information acquisition unit.

A neural network-based error compensation method for mass transfer of mini-light-emitting diode (Mini-LED) chips includes the following steps. (S1) An automated optical re-inspection result is obtained as a first result. (S2) The first result is sorted and normalized to obtain a second result. (S3) Nearest-neighbor interpolation is performed on a Mini-LED chip with a transfer status identifier being abnormal, and a differential of path variables of each Mini-LED chip is calculated. (S4) A multi-layer neural network model is defined. A loss function is constructed. A weight of the multi-layer neural network model is updated with the loss function, until no overfitting is observed. (S5) A chip transfer path is generated and input into the multi-layer neural network model to obtain a predicted transfer error value, and mass transfer of the Mini-LED chip is performed based on the predicted transfer error value.