Abstract: A driving system includes a central controller, a plurality of driving paths forming a least crossing position, and a plurality of autonomous driving devices configured to drive along at least one of the driving paths through the crossing position. The central controller is configured to determine a plurality of target driving devices from among the autonomous driving devices that are within a certain range of the crossing position, designate one of the target driving devices as a master driving device and the other of the target driving device as slave driving devices. The master driving device controls passage of the slave driving devices through the crossing position.

Abstract: A driving system includes a central controller, a plurality of driving paths forming a least crossing position, and a plurality of autonomous driving devices configured to drive along at least one of the driving paths through the crossing position. The central controller is configured to determine a plurality of target driving devices from among the autonomous driving devices that are within a certain range of the crossing position, designate one of the target driving devices as a master driving device and the other of the target driving device as slave driving devices. The master driving device controls passage of the slave driving devices through the crossing position.

Abstract: A material control system (MCS) to select an optimum transport route by calculating a prospective transport time between a source node and a destination node includes a transport order manager to receive and manage layout information, and real-time job information of a load and a waiting job number of a storehouse according to respective unit links. The MCS also includes an optimum route generating part to calculate the prospective transport time by receiving the layout information and the real-time job information in a predetermined period of time from the transport order manager and by modeling the information. The optimum route generating part also selects a transport route having a minimum prospective transport time as the optimum transport route. Thus, a transport flow of production lines may be balanced, and interruption between transport vehicles may be minimized, thereby enabling overall transport efficiency to be maximized.

Abstract: A method of predicting a carrying time in an automatic warehouse system. Input variables of a real automatic warehouse system are set in a virtual automatic warehouse system. The virtual automatic warehouse system is modeled based upon the set input variables. An optimal weighting value is set according to the modeling. A predicted carrying time is calculated based upon data input corresponding to the set variables and past history data related to a moving time(s) in the automatic warehouse system. An error between the predicted carrying time and a real carrying time in the automatic warehouse system is calculated. An optimal carrying path in the automatic warehouse system is selected according to the predicted carrying time if the error is within a preset allowable range.

Abstract: A method of predicting a carrying time in an automatic warehouse system. Input variables of a real automatic warehouse system are set in a virtual automatic warehouse system. The virtual automatic warehouse system is modeled based upon the set input variables. An optimal weighting value is set according to the modeling. A predicted carrying time is calculated based upon data input corresponding to the set variables and past history data related to a moving time(s) in the automatic warehouse system. An error between the predicted carrying time and a real carrying time in the automatic warehouse system is calculated. An optimal carrying path in the automatic warehouse system is selected according to the predicted carrying time if the error is within a preset allowable range.