JP2021160905A - Charging system for unmanned fork lift - Google Patents

Abstract

To provide an unmanned fork lift charging system capable of suppressing reduction of a truck operation rate.SOLUTION: A charging system 1 comprises: a truck 10 transmitting own vehicle positional information; an unmanned fork lift 20 for performing a plurality of pieces of cargo handling work, and loading and unloading work to the truck 10 which arrives at a prescribed place P; an arrival time estimating part for estimating arrival time of the truck 10 to the prescribed place P; a work schedule time calculating part for calculating loading and unloading work schedule time when, loading and unloading work is performed; a remaining battery estimating a remaining battery of the unmanned fork lift 20 at the loading and unloading work schedule time; a charging station 30 for charging; and a schedule generating part for generating a charging schedule when the estimated remaining battery becomes equal to or lower than a predetermined prescribed amount, in which the unmanned fork lift 20 recovers the remaining battery according to the charging schedule, and after recovering so that, the remaining battery does not become equal to or lower than the predetermined constant amount during the loading and unloading work, then, performs loading and unloading work.SELECTED DRAWING: Figure 1

Description

The present invention relates to a charging system for an unmanned forklift that performs loading or unloading work on a truck.

It is common practice to load a truck onto a truck with a forklift and to unload the truck with a forklift (see, for example, Patent Document 1).

An unmanned forklift is known as a type of forklift (see, for example, Patent Document 2). The unmanned forklift will perform cargo handling work according to a preset cargo handling schedule, and when the remaining battery level falls below a predetermined amount, move to a charging station that charges the unmanned forklift truck to recover the remaining battery level. It is programmed in.

Japanese Unexamined Patent Publication No. 2010-235223 Japanese Unexamined Patent Publication No. 11-21098

By the way, in the case where an unmanned forklift performs loading work or unloading work, in order to improve the operating rate of the unmanned forklift, other cargo handling work (for example, warehousing work, warehousing work) until the unloading work is performed. It is preferable to perform work, transfer work, or loading / unloading work for other trucks).

However, if the remaining battery level drops due to the cargo handling work performed before the loading / unloading work, it may be necessary to charge the unmanned forklift at the charging station when performing the loading / unloading work. In such a case, since the unmanned forklift interrupts the loading and unloading work and recovers the remaining battery level at the charging station, there is a problem that the residence time of the truck at a predetermined place becomes long and the operating rate of the truck decreases. ..

The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a charging system for an unmanned forklift that can suppress a decrease in the operating rate of a truck.

In order to solve the above problem, the charging system for an unmanned forklift according to claim 1 follows a truck that transmits position information of its own vehicle while traveling to a predetermined location and a cargo handling schedule preset at the predetermined location. Based on an unmanned forklift that performs a plurality of cargo handling operations and also performs loading and unloading operations for the truck that has arrived at the predetermined location, and the position information transmitted from the truck. Based on the arrival time estimation unit that estimates the arrival time of the truck to the predetermined location and the estimated arrival time that is the estimation result of the arrival time, the scheduled unloading work time at which the unloading work is performed is calculated. The scheduled work time calculation unit, the battery remaining amount estimation unit that estimates the battery remaining amount of the unmanned forklift at the scheduled loading and unloading work time, and the unmanned forklift truck to recover the battery remaining amount reduced by the cargo handling work. In order to interrupt the charging between the cargo handling work and the loading / unloading work when the charging station for charging and the estimated battery remaining amount which is the estimation result of the remaining battery amount are equal to or less than a predetermined predetermined amount. The unmanned forklift is provided with a schedule generator for generating the charging schedule of the above, and the unmanned forklift moves to the charging station according to the charging schedule to recover the remaining battery level, and the remaining battery level is preliminarily used during the loading / unloading operation. It is characterized in that the loading and unloading work is performed after the recovery is performed so that the amount does not fall below a predetermined amount.

The unmanned forklift charging system according to claim 2 is the unmanned forklift charging system according to claim 1, wherein the schedule generator sets the end time of the charging before the estimated arrival time. do.

The unmanned forklift charging system according to claim 3 stores an arrival time estimation model generated by machine learning using past congestion occurrence data as teacher data in the unmanned forklift charging system according to claim 1 or 2. The arrival time estimation unit further includes an arrival time estimation model storage unit, which estimates the arrival time based on the arrival time estimation model, the position information of the track, and the date and time information.

The unmanned forklift charging system according to claim 4 is generated by machine learning using past battery remaining amount transition data as teacher data in the unmanned forklift charging system according to any one of claims 1 to 3. The battery remaining amount estimation model storage unit for storing the battery remaining amount estimation model is further provided, and the battery remaining amount estimation unit includes the battery remaining amount estimation model and the battery information related to the battery remaining amount received from the unmanned forklift. It is characterized in that the remaining battery level at the scheduled loading / unloading work time is estimated based on the above.

According to the present invention, it is possible to provide a charging system for an unmanned forklift that can suppress a decrease in the operating rate of a truck.

It is a schematic diagram of the charging system for an unmanned forklift which concerns on one Embodiment of this invention. It is a block diagram of the management server which concerns on the same embodiment. It is a flowchart which shows the generation process of the charge schedule which concerns on this embodiment. (A) is a Gantt chart showing the operation of the unmanned forklift according to the embodiment, and (B) is a graph showing the transition of the remaining battery level. (A) is a Gantt chart showing the operation of the unmanned forklift according to the modified example, and (B) is a graph showing the transition of the remaining battery level. (A) is a Gantt chart showing the operation of the unmanned forklift according to the comparative example, and (B) is a graph showing the transition of the remaining battery level.

The unmanned forklift charging system 1 (hereinafter referred to as “charging system 1”) of the present invention will be described with reference to the drawings.
As shown in FIG. 1, the charging system 1 of the present embodiment powers a truck 10 for transporting a load (not shown), one or more unmanned forklifts 20 for carrying out cargo handling work, and an unmanned forklift 20. It is provided with a charging station 30 for supplying power and a management server 40 for managing an unmanned forklift 20.

The truck 10 is a freight vehicle that handles logistics between bases, and travels in a predetermined manner in order to transport a load from a predetermined place P to another place or to transport a load from another place to a predetermined place P. Drive along the route. The truck 10 acquires the position information of the own vehicle by, for example, GPS, and transmits the position information of the own vehicle to the management server 40 while traveling to the predetermined place P. The position information of the track 10 is acquired (transmitted) at predetermined time intervals (that is, periodically) or when the track 10 passes a predetermined point.

The unmanned forklift 20 is a vehicle that receives a cargo handling schedule from the management server 40 and performs cargo handling work at a predetermined location P based on the cargo handling schedule. Further, when the truck 10 arrives at the predetermined place P, the unmanned forklift 20 performs a loading / unloading work of loading or unloading the truck 10. Specifically, when the truck 10 for transporting the load from the predetermined place P to another place arrives at the predetermined place P, the unmanned forklift 20 performs the loading work on the truck 10 and performs the load loading work on the other place. When the truck 10 for transporting the load to the predetermined place P arrives at the predetermined place P, the load unloading work is performed on the truck 10.

Further, the unmanned forklift 20 includes a rechargeable battery 21 that supplies electric power to the power source, and transmits battery information including information on the remaining charge amount (remaining battery amount) of the battery 21 to the management server 40. When the remaining battery level of the unmanned forklift 20 falls below a predetermined fixed amount (for example, 15% of the maximum stored amount), the unmanned forklift 20 moves to the charging station 30 to recover the remaining battery level, and the remaining battery level is predetermined. When the battery recovers to a specified amount (for example, 80% of the maximum storage amount) or more, the charging station 30 departs for work. Further, the unmanned forklift 20 receives the charging schedule from the management server 40 and moves to the charging station 30 to recover the remaining battery level based on the received charging schedule regardless of the remaining battery level. When the amount is recovered so that it does not fall below a certain amount during the loading / unloading work, the charging station 30 departs for the work.

The charging station 30 is composed of a contact type or non-contact type power supply device, and charges the battery 21 of the unmanned forklift 20 by supplying power to the unmanned forklift 20 which is in contact with or is located in the vicinity of the power supply device. Recover the battery level. The charging station 30 automatically starts power supply when the battery 21 becomes rechargeable, and automatically stops power supply when the battery becomes unchargeable or the remaining battery level reaches the maximum storage amount.

The management server 40 is configured to be able to communicate with the track 10, and receives position information from the track 10. The management server 40 is also configured to be able to communicate with the unmanned forklift 20, receives battery information from the unmanned forklift 20, and transmits the cargo handling schedule and the charging schedule to the unmanned forklift 20.

FIG. 2 shows the configuration included in the management server 40.
As shown in FIG. 2, the management server 40 includes a position information receiving unit 41, an estimation model storage unit 42, an arrival time estimation unit 43, a work information storage unit 44, a schedule storage unit 45, a scheduled work time calculation unit 46, and battery information. It includes a receiving unit 47, a battery remaining amount estimation unit 48, a schedule generation unit 49, and a schedule transmission unit 51.

The position information receiving unit 41 is composed of a communication device that directly or indirectly communicates with the track 10. The position information receiving unit 41 receives the position information of the track 10.

The estimation model storage unit 42 stores in advance an arrival time estimation model for estimating the arrival time of the track 10 to the predetermined location P, and functions as an arrival time estimation model storage unit. The arrival time estimation model is generated by machine learning using past congestion occurrence data as teacher data. The past congestion data used to generate the arrival time estimation model includes, for example, the date, day, and time of the congestion, the section where the congestion occurred, and the traffic volume in that section (for example, a car per unit time). Data such as the number of passages) can be included.

Further, the estimation model storage unit 42 stores in advance a battery remaining amount estimation model for estimating the battery remaining amount of the unmanned forklift 20 at a predetermined time, and functions as a battery remaining amount estimation model storage unit. The battery remaining amount estimation model is generated by machine learning using the past battery remaining amount transition data as teacher data. The past battery level transition data used to generate the battery level estimation model can include, for example, data such as the date and time, the battery level, and the operating status of the battery-equipped vehicle.

The arrival time estimation unit 43 includes the arrival time estimation model stored in the estimation model storage unit 42 and the position information of the truck 10 received by the position information receiving unit 41 (that is, the position information of the own vehicle transmitted from the truck 10). , The arrival time of the truck 10 to the predetermined place P is estimated based on the date and time information related to the date, the day, and the time when the truck 10 acquired the position information. Specifically, for example, the arrival time estimation unit 43 estimates the travel time required to travel from the own vehicle position of the truck 10 to the predetermined location P by using the arrival time estimation model, and the truck 10 acquires the position information. The estimated arrival time of the truck 10 to the predetermined location P is calculated by adding the travel time required to the time.

The work information storage unit 44 stores in advance work information related to cargo handling work and loading / unloading work performed by the unmanned forklift 20. Specifically, for example, the work information storage unit 44 stores, as work information, the work required time for performing each of the plurality of cargo handling work and the loading / unloading work. The work required time can be used to calculate the start time and end time of the scheduled cargo handling work and unloading work.

The schedule storage unit 45 stores the cargo handling schedule of the unmanned forklift 20. That is, the schedule storage unit 45 stores the cargo handling schedule generated by the schedule generation unit 49. The cargo handling schedule can be used to identify the cargo handling operations performed by the unmanned forklift 20.

The scheduled work time calculation unit 46 calculates the scheduled unloading work time at which the unloading work is performed based on the estimation result (that is, the estimated arrival time) of the arrival time of the truck 10 by the arrival time estimation unit 43. Specifically, the scheduled work time calculation unit 46 refers to the cargo handling schedule stored in the schedule storage unit 45, the work information stored in the work information storage unit 44, and the like, so that the scheduled work schedule can be loaded and unloaded. Calculate the time. The scheduled unloading work time is the time from the scheduled unloading work start time to the scheduled unloading work end time. The scheduled unloading work start time is, for example, the earliest time when the unloading work becomes possible after the estimated arrival time, and the scheduled unloading work end time is the work information storage unit 44 with respect to the scheduled unloading work start time. It is calculated by adding the time required for loading and unloading work stored in advance to.

The battery information receiving unit 47 is composed of a communication device that directly or indirectly communicates with the unmanned forklift 20. The battery information receiving unit 47 receives the battery information of the unmanned forklift 20.

The battery remaining amount estimation unit 48 includes the battery remaining amount estimation model stored in the estimation model storage unit 42 and the battery information of the unmanned forklift 20 received by the battery information receiving unit 47 (that is, the battery information transmitted from the unmanned forklift 20). Based on the above, the remaining battery level of the unmanned forklift 20 at the scheduled loading and unloading work time is estimated. Specifically, for example, the battery remaining amount estimation unit 48 estimates the battery usage amount up to an arbitrary time in the scheduled loading / unloading work time using the battery remaining amount estimation model, and the battery remaining amount included in the received battery information. By subtracting the battery usage from the amount, the remaining battery level at the scheduled loading and unloading work time is calculated.

The schedule generation unit 49 generates a cargo handling schedule related to the cargo handling work performed by the unmanned forklift 20. Specifically, the schedule generation unit 49 sets unworked cargo handling work as a cargo handling work candidate, selects a plurality of cargo handling work to be performed by the unmanned forklift 20 from a plurality of cargo handling work candidates, and performs the cargo handling work for the unmanned forklift. Generate a cargo handling schedule as 20 does.

Further, the schedule generation unit 49 determines whether or not the estimation result of the remaining battery amount (that is, the estimated remaining amount of the battery) by the battery remaining amount estimation unit 48 is equal to or less than a predetermined predetermined amount, so that the scheduled work time It is estimated whether or not the remaining amount of the battery becomes a certain amount (for example, 15% of the maximum stored amount) or less at the scheduled loading / unloading work time calculated by the calculation unit 46. When the schedule generation unit 49 determines that the estimated battery remaining amount is less than or equal to a predetermined amount, it estimates that the battery remaining amount will be less than a certain amount at the scheduled loading / unloading work time, and is unmanned between the cargo handling work and the loading / unloading work. Generate a charging schedule for interrupting the charging of the forklift 20. Specifically, the schedule generation unit 49 determines a charging time for recovering the remaining battery power so that it does not fall below a certain amount during loading and unloading work, and the unmanned forklift 20 charges the battery at the charging station 30 during the charging time. Generate a charging schedule to recover. The schedule generation unit 49 sets the start time and end time of charging before the estimated arrival time of the truck 10 to the predetermined location P. The length of the charging time (that is, the length from the start time to the end time of charging) is preferably set according to the time required for the loading and unloading work.

The schedule transmission unit 51 is composed of a communication device that directly or indirectly communicates with the unmanned forklift 20. The schedule transmission unit 51 transmits the cargo handling schedule and the charging schedule generated by the schedule generation unit 49 to the unmanned forklift 20.

The flow of generating the charging schedule will be described with reference to FIG. It is preferable that the process shown in FIG. 3 is performed every time the position information receiving unit 41 receives the position information of the track 10.
As shown in FIG. 3, first, the arrival time estimation unit 43 estimates the arrival time of the truck 10 based on the arrival time estimation model, the position information of the truck 10, and the date and time information (step S1). That is, in step S1, the estimated arrival time is calculated.

Next, the scheduled work time calculation unit 46 calculates the scheduled unloading work time at which the unloading work is performed based on the estimated arrival time calculated in step S1 (step S2). That is, in step S2, the scheduled start time of the loading / unloading work and the scheduled end time of the loading / unloading work are calculated.

Next, the battery remaining amount estimation unit 48 estimates the battery remaining amount of the unmanned forklift 20 at the scheduled loading / unloading work time calculated in step S2 based on the battery remaining amount estimation model and the battery information of the unmanned forklift 20. Step S3). Specifically, for example, in step S3, the estimated battery remaining amount at the scheduled end time of the loading / unloading work at which the remaining battery amount is the smallest by performing the loading / unloading work is calculated.

Next, the schedule generation unit 49 determines whether or not the estimated battery remaining amount calculated in step S3 is equal to or less than a predetermined predetermined amount (for example, 15% of the maximum stored amount) (step S4). That is, in step S4, it is estimated whether or not the remaining battery level becomes a certain amount (for example, 15% of the maximum stored amount) or less during the scheduled loading / unloading work time.

If it is determined in step S4 that the estimated remaining battery level is not less than the predetermined amount (step S4: NO), that is, if it is estimated that the remaining battery level does not fall below a certain amount during the scheduled loading and unloading work time, a schedule is generated. The unit 49 determines that the loading / unloading work will not be interrupted due to the charging of the unmanned forklift 20, and does not generate the charging schedule.

On the other hand, when it is determined in step S4 that the estimated remaining battery level is less than or equal to the predetermined amount (step S4: YES), that is, when the remaining battery level is estimated to be less than or equal to a certain amount during the scheduled loading and unloading work time. The schedule generation unit 49 determines the charging time to be interrupted between the cargo handling work and the loading / unloading work (step S5).

Then, the schedule generation unit 49 generates a charging schedule so that the unmanned forklift 20 recovers the remaining battery level at the charging station 30 at the charging time determined in step S5 (step S6). In this way, the unmanned forklift 20 moves to the charging station 30 before the estimated arrival time calculated in step S1 according to the charging schedule to recover the remaining battery level.

Next, an example of the operation of the unmanned forklift 20 according to the charging schedule generated by the present embodiment will be described together with the operation according to the comparative example. FIG. 4A is a Gantt chart showing the operation of the unmanned forklift 20 according to the embodiment, and FIG. 4B is a graph showing the transition of the remaining battery level in the embodiment in a simplified manner. Further, FIG. 6A is a Gantt chart according to a comparative example, and FIG. 6B is a graph showing a simplified transition of the remaining battery level in the comparative example.

In the comparative example shown in FIGS. 6A and 6B, the unmanned forklift 20 performs cargo handling operations A and B in order according to the cargo handling schedule, and when the truck 10 arrives at the predetermined location P at time t1, the cargo handling operation B is completed. Later, the loading / unloading work W is started. When the remaining battery level of the unmanned forklift 20 falls below a predetermined fixed amount (15% of the maximum storage amount) during the loading / unloading work W, the unmanned forklift 20 interrupts the loading / unloading work W and moves to the charging station 30 to move the battery. Restore the remaining amount. Then, when the remaining battery level of the unmanned forklift 20 exceeds a specified amount (80% of the maximum stored amount), the unmanned forklift 20 leaves the charging station 30 and restarts the loading / unloading work W. Therefore, the truck 10 needs to wait at the predetermined place P for the time T1 from the time t1 to the completion of the loading / unloading work W.

On the other hand, in the embodiment shown in FIGS. 4A and 4B, when the time t1 is estimated as the arrival time of the truck 10 at the predetermined place P, the loading / unloading work W is scheduled based on the estimation result. The scheduled loading and unloading work time Ts is calculated. The scheduled unloading work time Ts is the scheduled time when the unloading work W is performed after the cargo handling work A and B. In this case, the remaining battery level is loaded as shown by the alternate long and short dash line in FIG. 4 (A). It is estimated that the scheduled wholesale work time Ts will be less than or equal to a predetermined fixed amount (15% of the maximum stored amount). Therefore, a charging schedule for interrupting charging between the cargo handling work A and the loading / unloading work W is generated, and the unmanned forklift 20 moves to the charging station 30 according to the charging schedule after completing the cargo handling work A. Recover the battery level. Then, the unmanned forklift 20 waits until time t1 after recovering so that the remaining battery level does not fall below a predetermined fixed amount during the loading / unloading work W (that is, maintaining a certain amount or more). When the truck 10 arrives at the predetermined place P at time t1, the loading / unloading work W is started, and the cargo handling work B is performed after the loading / unloading work W is completed. In this way, the time T2 from the time t1 to the completion of the unloading work W is shorter than the time T1 shown in the comparative example of FIG. 6B because the unloading work W is not interrupted. Further, since the unloading work W is performed before the cargo handling work B is completed, the unloading work W is started earlier than the scheduled unloading work time Ts.

In this embodiment, the following effects can be obtained.
(1) When the estimated remaining battery level at the scheduled loading / unloading work time Ts becomes less than or equal to a predetermined predetermined amount, a charging schedule for interrupting charging between the cargo handling work A and the loading / unloading work W is generated. The unmanned forklift 20 moves to the charging station 30 according to the charging schedule to recover the remaining battery level, recovers the remaining battery level so as not to fall below a predetermined fixed amount during the loading / unloading operation W, and then unloads. Perform work W. Therefore, the loading / unloading work W is not interrupted, the loading / unloading work W can be completed quickly, and a decrease in the operating rate of the truck 10 can be suppressed.

(2) The end time of charging the unmanned forklift 20 is set before the estimated arrival time (time t1). Therefore, by completing the charging of the unmanned forklift 20 before the estimated arrival time, the loading / unloading work W can be started immediately after the truck 10 arrives at the predetermined location P, and the reduction in the operating rate of the truck 10 is effective. Can be suppressed.

(3) The arrival time of the track 10 to the predetermined location P is determined based on the arrival time estimation model generated by machine learning using the past congestion occurrence data as the teacher data, the position information of the track 10, and the date and time information. Presumed. Therefore, by updating the arrival time estimation model (that is, by developing machine learning), the arrival time estimation accuracy can be improved.

(4) Based on the battery remaining amount estimation model generated by machine learning using the past battery remaining amount transition data as teacher data and the battery information related to the battery remaining amount, the battery remaining amount at the scheduled loading / unloading work time Ts. Is estimated. Therefore, by updating the battery level estimation model (that is, by developing machine learning), the battery level estimation accuracy can be improved.

The present invention is not limited to the above embodiment, and the above configuration can be changed. For example, the following changes can be made and implemented, or the following changes can be combined and implemented.

-If the remaining battery level does not fall below a certain amount during the loading and unloading work, for example, as shown in FIGS. 5A and 5B, the unmanned forklift 20 recovers the remaining battery level before loading and unloading. It may be configured to perform cargo handling work before the work is started. FIG. 5 (A) is a Gantt chart showing the operation of the unmanned forklift 20 according to the modified example, and FIG. 5 (B) is a graph showing the transition of the remaining battery level in the modified example in a simplified manner.

In the modification shown in FIGS. 5A and 5B, the unmanned forklift 20 moves to the charging station 30 according to the charging schedule after completing the cargo handling work A, and the battery remains, as in the embodiment shown in FIG. Restore the amount. Then, after the unmanned forklift 20 recovers so that the remaining battery level does not fall below a predetermined fixed amount (15% of the maximum stored amount) during the loading / unloading work W (that is, the remaining battery level is a specified amount). After recovering to 80% of the maximum storage capacity), the cargo handling work B is performed until the time t1 after leaving the charging station 30, and when the truck 10 arrives at the predetermined place P at the time t1, the cargo handling work B is interrupted and the loading / unloading work is performed. W is performed, and after the loading / unloading work W is completed, the cargo handling work B is restarted. In this modified example, since a part of the cargo handling operation B is performed before the loading / unloading operation W, it is possible to suppress a decrease in the operating rate of the unmanned forklift 20 as compared with the embodiment shown in FIG.

-If it is possible to estimate whether or not the remaining battery level at the scheduled loading / unloading work time is below a certain amount, the remaining battery level estimated by the battery remaining amount estimation unit 48 is arbitrary other than the scheduled end time of the loading / unloading work. It may be the remaining battery level at the time of. Specifically, for example, the battery remaining amount estimation unit 48 estimates the battery remaining amount at the scheduled start time of loading / unloading work, and the schedule generation unit 49 estimates the estimated battery remaining amount by a predetermined amount (for example, maximum storage capacity). When it is 30% or less of the amount), it is estimated that the remaining battery amount at the scheduled loading / unloading work time will be a predetermined fixed amount (for example, 15% of the maximum stored amount) or less, and the estimated remaining battery amount is the predetermined amount. If it exceeds, it may be estimated that the remaining battery level at the scheduled loading / unloading work time does not fall below a certain amount.

-If the remaining battery level of the unmanned forklift 20 can be estimated, the method of estimating the remaining battery level may be appropriately changed. That is, for example, the remaining battery level at the scheduled loading / unloading work time can be estimated based on the operating time of the unmanned forklift 20, and the remaining battery level can be estimated without using the battery remaining amount estimation model generated by machine learning. It can also be estimated.

-If the arrival time of the truck 10 to the predetermined place P can be estimated, the method of estimating the arrival time may be appropriately changed. That is, for example, the arrival time can be estimated without using the arrival time estimation model generated by machine learning.

-If the decrease in the operating rate of the truck 10 can be suppressed, the charging schedule generation method may be appropriately changed. That is, for example, the start time and end time of the charging time can be changed as appropriate, and the charging schedule generation process shown in FIG. 3 can be changed.

1 Charging system for unmanned forklift 10 Track 20 Unmanned forklift 21 Battery 30 Charging station 40 Management server 42 Estimated model storage (arrival time estimation model storage, battery remaining amount estimation model storage)
43 Arrival time estimation unit 46 Scheduled work time calculation unit 48 Battery level estimation unit 49 Schedule generation unit P Predetermined location

Claims (4)

A truck that transmits the location information of the own vehicle while driving to a predetermined location,
An unmanned forklift that performs a plurality of cargo handling operations according to a preset cargo handling schedule at the predetermined site, and also performs a loading / unloading operation for the truck that has arrived at the predetermined site.
An arrival time estimation unit that estimates the arrival time of the truck to the predetermined location based on the position information transmitted from the truck.
Based on the estimated arrival time, which is the estimation result of the arrival time, the scheduled work time calculation unit that calculates the scheduled loading / unloading work time at which the loading / unloading work is performed, and the scheduled work time calculation unit.
A battery level estimation unit that estimates the battery level of the unmanned forklift at the scheduled loading / unloading time, and a battery level estimation unit.
A charging station that charges the unmanned forklift in order to recover the remaining battery level that has decreased due to the cargo handling work.
A schedule for generating a charging schedule for interrupting the charging between the cargo handling operation and the loading / unloading operation when the estimated battery remaining amount, which is the estimation result of the remaining battery amount, becomes equal to or less than a predetermined predetermined amount. Equipped with a generator
The unmanned forklift moves to the charging station according to the charging schedule to recover the remaining battery level, and then recovers the remaining battery level so as not to fall below a predetermined fixed amount during the loading / unloading operation. , A charging system for unmanned forklifts, characterized by performing the loading and unloading work. The charging system for an unmanned forklift according to claim 1, wherein the schedule generation unit sets the end time of the charging before the estimated arrival time. It is further equipped with an arrival time estimation model storage unit that stores an arrival time estimation model generated by machine learning using past congestion occurrence data as teacher data.
The charging system for an unmanned forklift according to claim 1 or 2, wherein the arrival time estimation unit estimates the arrival time based on the arrival time estimation model, the position information of the truck, and the date and time information. It also has a battery level estimation model storage unit that stores the battery level estimation model generated by machine learning using past battery level transition data as teacher data.
The battery remaining amount estimation unit is characterized in that the battery remaining amount estimation unit estimates the battery remaining amount at the scheduled loading / unloading work time based on the battery remaining amount estimation model and the battery information related to the battery remaining amount received from the unmanned forklift. The charging system for an unmanned forklift according to any one of claims 1 to 3.

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