Abstract: The traveling unit main body and the accommodation unit are adapted to be uncoupled and re-coupled. The traveling unit main body includes a first driving unit and a second driving unit configured to perform at least one of operations of taking out or storing the article from or in the accommodation unit and transporting the article taken out from the accommodation unit. The traveling unit main body performs at least one of the operations of taking out, storing, and transporting the article using the first driving unit and the second driving unit in a state in which the traveling unit main body and the accommodation unit are uncoupled.

Abstract: To effectively enhance the operation efficiency of an autonomous mobile robot, an autonomous mobile robot control system includes a processor and a plurality of environmental cameras. The processor estimates a moving route of each of a plurality of moving bodies on the basis of characteristics of each of the plurality of moving bodies and sets a subset of the plurality of moving bodies whose moving routes overlap among the detected moving bodies as avoidance processing target moving bodies. The processor generates an avoidance procedure for the avoidance processing target moving bodies so the motion of the avoidance processing target moving bodies does not interfere with the motion of other avoidance target moving bodies.

Abstract: A logistics system provides a logistics service utilizing a logistics robot that delivers a package by autonomous traveling. The logistics system includes one or more processors configured to execute a delivery route determination process that determines a delivery route along which the logistics robot delivers the package. When there are a plurality of delivery route candidates from a position of the logistics robot to a delivery destination of the package, the one or more processors select the delivery route from the plurality of delivery route candidates based on at least one of a congestion degree of each of the plurality of delivery route candidates, a time of day, a weather condition, and energy consumption of the logistics robot along each of the plurality of delivery route candidates.

Abstract: At least one of a discharging tendency of waste based on a discharge history of discharge of the waste by a user of a service and a discharge prediction about the waste based on the purchase history of a product by the user is calculated. A need prediction about the service is calculated by using a prediction model which uses, as a parameter, at least one of the discharging tendency of the waste and the discharge prediction about the waste. A level of the service is estimated by a simulation which uses, as parameters, the need prediction and a resource employed for the service. A notification is transmitted to the user suggesting a modification of behavior of at least one of discharge of the waste and purchase of a product by the user based on a comparison result between an estimated level and a stipulated level of the service.

Abstract: A service management device manages a service delivered in a predetermined area. The service management device has an operator interface including a display that displays information for an operator and receiving an input from the operator. The service management device communicates with an autonomous robot used for delivering the service in the predetermined area to acquire, from the autonomous robot, service robot information indicating at least a position and a status of the autonomous robot. The service management device displays a map of the predetermined area and the position of the autonomous robot on the display, based on the service robot information. When the autonomous robot displayed on the display is specified by the operator through the operator interface, the service management device displays a status window indicating the status of the autonomous robot specified by the operator on the display, based on the service robot information.

Abstract: The learning system includes a data generation unit configured to generate learning data based on rehabilitation data and a learning unit configured to perform machine learning using the learning data. A sensor is provided to detect a plurality of motion amounts in a walking motion of a trainee, and it is evaluated that, when one of the motion amounts matches one of abnormal walking criteria, that the walking motion is an abnormal walking pattern that meets the matched abnormal walking criterion. The data generation unit generates each of the pieces of rehabilitation data before and after a change in the results of evaluation of the abnormal walking pattern as learning data. The learning unit sequentially inputs each of the pieces of rehabilitation data as one data set, thereby performing machine learning.

Abstract: The learning apparatus includes a data generation unit configured to generate learning data based on rehabilitation data and a learning unit configured to perform machine learning using the learning data. A sensor is provided to detect a plurality of motion amounts in a walking motion of a trainee, and it is evaluated that, when one of the motion amounts matches one of abnormal walking criteria, that the walking motion is an abnormal walking pattern that meets the matched abnormal walking criterion. The data generation unit generates each of the pieces of rehabilitation data before and after a change in the results of evaluation of the abnormal walking pattern as learning data. The learning unit sequentially inputs each of the pieces of rehabilitation data as one data set, thereby performing machine learning.

Abstract: The server is a learning apparatus including a data acquisition unit and a learning unit. The data acquisition unit acquires profile data and a selected assistance level as learning data. The profile data indicates a profile related to a trainee before executing rehabilitation regarding rehabilitation executed using a walking training apparatus as a rehabilitation support system. The selected assistance level is an assistance level selected at the time of executing the rehabilitation. The learning unit learns to determine a recommended assistance level recommended to be selected when the trainee uses the rehabilitation support system based on the learning data. Further, the learning unit generates a trained model that receives the profile data and outputs the recommended assistance level based on the learning.

Abstract: The method of the present disclosure comprises the following first to fourth steps. The first step is a step of calculating, by simulation, a model time requited for each action of object handling work for an object handled in a distribution center. The second step is a step of determining a start time and an end time of each action based on an analysis of camera image data obtained in the distribution center. The third step is a step of calculating an actual time required for each action from a time difference between the start time and the end time of each action. The fourth step is a step of calculating a difference between the actual time required and the model time required for each action.

Abstract: The method of the present disclosure comprises the following first to fourth steps. The first step is a step of determining a start timing and an end timing of each action of object handling work for a dummy object imitating an object handled in a distribution center, based on an analysis of position data of the dummy object when the dummy object is flowed to a distribution line of the distribution center. The second step is a step of calculating a model time required for each action based on the start timing and the end timing of each action. The third step is a step of calculating an actual time required for each action when the object is actually flowed to the distribution line. The fourth step is a step of calculating a difference between the actual time required and the model time required for each action.

Abstract: The service optimization system performs simulation using a human model modeling service utilization behavior of a customer to predict a demand for a service from real data including at least one of behavior, behavior history, and behavior schedule of the customer related to the service. Also, the service optimization system performs simulation using a service model modeling a relationship between a service parameter determining content of the service, a demand for the service, and a level of the service to determine a setting value of the service parameter for maintaining the level of the service based on the predicted demand. Then, the service optimization system acquires the real data after the service is provided using the determined setting value of the service parameter to feed back the acquired real data to an input of the simulation using the human model.

Abstract: A logistics robot for transporting a package includes: a robot unit configured to make the logistics robot travel; and a storage unit configured to store the package. The robot unit and the storage unit are separable from each other.

Abstract: The plurality of passage areas include a first passage area and a second passage area, the first passage area being a passage area for which a traveling condition for a transportation vehicle carrying an object to be transported is specified, and a second passage area being a passage area for which a traveling condition for restricting traveling of the transportation vehicle more strictly than in the first passage area is specified. The traffic management system includes a traffic management unit configured to change a regulation in at least a part of the second passage area in regard to the permission/prohibition of the traveling of the transportation vehicle based on at least one of information about a demand for the object to be transported and information about a traffic environment.

Abstract: A service management system for managing a service includes: one or more memory devices configured to store user personality information indicating personality for each user of the service; and one or more processors. The one or more processors are configured to execute: a service simulation process that simulates a state of the service to estimate a level of the service, based on demand forecast information for the service; and a change promotion process that notifies a user of change promotion information that promotes a change in a requirement or an action for the service, when the level of the service is lower than a predetermined level. The change promotion process includes notifying the change promotion information according to the personality of the user based on the user personality information.

Abstract: A learning system, a walking training system, a method, a program, and a trained model for performing walking training at appropriate setting parameters are provided. A learning system according to an embodiment includes: a data acquisition unit configured to acquire rehabilitation data from a walking training system including an actuator configured to assist a walking motion of a trainee and a control unit configured to control the actuator in accordance with a setting parameter; a data generation unit configured to generate the rehabilitation data including detection data and the setting parameter as learning data; and a learning unit configured to perform machine learning using the learning data, thereby generating a learning model that receives the detection data and outputs a recommended value of the setting parameter.

Abstract: A conveyance control system according to the present disclosure is a conveyance control system configured to control a conveyance robot to autonomously move and convey a conveyance object to a destination, the conveyance control system including: a reception unit configured to receive, from a scheduled recipient of the conveyance object, a request signal for requesting a third party to collect the conveyance object halfway through conveyance of the conveyance object; and an issuance unit configured to issue, to a terminal of the third party, a temporary electronic key for enabling the conveyance object to be taken out from the conveyance robot based on a set condition when the reception unit receives the request signal.

Abstract: A conveyance system includes a plurality of conveyance robots, a storage unit, a stability information acquisition unit, a selection unit, and a system controller, which serves as a controller. The plurality of conveyance robots convey a wagon that accommodates a conveyed object. The storage unit stores robot information regarding features that the plurality of respective conveyance robots include. The stability information acquisition unit acquires stability information regarding stability when the conveyed object is conveyed. The selection unit selects one of the plurality of conveyance robots based on the robot information and the stability information that have been acquired. The system controller instructs the one conveyance robot that has been selected to convey the conveyed object.

Abstract: A mobile body delivers a package to a designated destination by autonomous traveling. When the mobile body arrives at the destination, door status information including opening/closing status of an entrance door of the destination is acquired, and it is determined whether the entrance door of the destination has been opened based on the acquired door status information. When the entrance door has been opened, a delivery of the package to the destination is authenticated, and a locking device for regulating the delivery of the package from the mobile body is unlocked.

Abstract: A simulation is performed using a digital space reproduced based on a real space in which a service is provided. Based on a simulation result satisfying a predetermined convergence condition, a threshold for detecting occurrence of an abnormality in one or more works performed in association with provision of the service in the real space may be set for each work unit corresponding to the one or more works. When the threshold is set, the time difference between the preset standard working time and the simulated work time included in the simulation result satisfying the convergent condition is calculated. A larger threshold value is assigned to a work unit having a larger time difference than a work unit having a smaller time difference.

Abstract: The method of the present disclosure is a management method for a logistics area with a work line constituted of a plurality of work subjects. The method comprises: simulating, using a digital twin, a work space in which the plurality of work subjects work; monitoring an actual movement of each of the plurality of work subjects in the work space; and displaying each of the plurality of work subjects on a screen simulating the work space as a heat map of which a color is changed in accordance with a degree of deviation between a movement simulated using the digital twin and a monitored actual movement.