Abstract: A vehicle power supply system includes a power source control device that executes a process of switching a connection state of a driving circuit to the main power source and an auxiliary power source including; a process of switching the connection state so as to transition to a backup state as a voltage decrease of the main power source occurs, and a process of switching the connection state so as to transition to a normal state as the voltage decrease of the main power source is resolved. After a voltage decrease of the main power source occurs, a turning-side control device of a steering control unit sets an output-limited state for a turning-side motor. The output-limited state is canceled upon the power source control device completing switching of the connection state so as to transition.

Abstract: A conveyer includes: a supporting member which supports a roll body having a sheet wound therearound; a conveyance roller which conveys the sheet; a guide member; an urging member which urges the guide member; a sensor; and a controller. The controller is configured: to determine a target position of the guide member and a permissible position in accordance with the target position; to control a rotation amount of the supporting member and/or a rotation amount of the conveyance roller; to determine whether or not a position of the guide member exceeds the permissible position; and to perform a notification of a conveying error of the sheet.

Abstract: A bulk feeder includes a feeder main body, a receiving member provided on the feeder main body and formed with a receiving region configured to receive a component discharged from a component case accommodating multiple components, a track member provided to be vibratable with respect to the feeder main body and formed with a conveyance path through which the multiple components are conveyed and a supply region that communicates with the conveyance path and opens upward to collect the multiple components, an excitation device configured to apply vibration to the track member so that the multiple components are conveyed along the conveyance path, and a connection member having flexibility and configured to connect between the receiving region and the conveyance path so that the multiple components flows through.

Abstract: A power supply system includes a system control unit, an auxiliary power supply, and an auxiliary-power-supply control unit. The system control unit and the auxiliary-power-supply control unit are configured such that information of at least one control unit of the system control unit and the auxiliary-power-supply control unit is able to be output to another control unit of the system control unit and the auxiliary-power-supply control unit. The at least one control unit is configured to output information indicating that an operation of the at least one control unit is stopped to the other control unit when the at least one control unit stops the operation of the at least one control unit.

Abstract: A power generation element includes a magnetic member that produces a large Barkhausen effect and magnetism collection members including an insertion part having the magnetic member inserted therethrough. The magnetism collection member includes a first component on an opposite side of a boundary plane to a magnetic field generation unit and a second component on the same side of the boundary plane as the magnetic field generation unit, the boundary plane passing through a center of an imaginary circle inscribed in the insertion part and having a diameter equal to a length of the insertion part in a third direction perpendicular to first and second directions, the first direction is a direction of the insertion of the magnetic member, and the second direction is a direction in which the magnetic field generation unit is disposed. A volume of the second component is larger than a volume of the first component.

Abstract: The present invention provides a self-location estimation method including: a first step of estimating the self-location of a moving body (1) from the detection information of a plurality of sensors (5) to (8) by using a plurality of algorithms (11) to (13); a second step of determining a weighting factor for each algorithm from one or more state quantities A, B and C, which are obtained by estimation processing for each of a plurality of algorithms, by using a trained neural network (14); and a third step of identifying, as the self-location of the moving body (1), a location obtained by synthesizing the self-locations, which have been estimated by the algorithms, by using weighting factors.

Abstract: Provided is a model parameter learning method by which a model parameter of a learning model used in control of a moving body having movement constraints can be appropriately learned. In this model parameter learning method, a model prediction control algorithm reflecting movement constraints of a robot 1 is used to calculate a time series of learning speed commands such that the movement trajectory of the robot 1 tracks the time series of a movement trajectory of a first pedestrian 5; and a model parameter of a CNN model is learned by an error back propagation method, the CNN model using learning data including the learning speed commands time series as input and outputting a time series of speed commands for a first moving body.

Abstract: In an environment in which a plurality of second pedestrians moves along predetermined movement patterns, a plurality of movement routes Rw when a first pedestrian moves toward a destination point is recognized. Data, in which an environmental image indicating a visual environment in front of a virtual robot when the virtual robot moves along each of the movement routes and a moving direction command indicating a moving direction of the virtual robot are combined, is generated as learning data. In the environmental image, colors corresponding to time-series displacement behaviors of a moving object image region is applied to at least a portion of the moving object image region indicating a pedestrian (moving object) present around a robot. Model parameters of a CNN (action model) is learned using the learning data, and a moving velocity command for a robot is determined using a learned CNN.

Abstract: In an environment in which a plurality of second pedestrians moves along predetermined movement patterns, a plurality of movement routes when a first pedestrian moves toward a destination point is recognized. Data, in which an environmental image indicating a visual environment in front of a virtual robot when the virtual robot moves along each of the movement routes and a moving direction command indicating a moving direction of the virtual robot are combined, is generated as learning data. In the environmental image, colors corresponding to kinds of the moving objects are applied to at least a portion of moving object image regions indicating pedestrians (moving objects) present around a robot. Model parameters of a CNN (action model) is learned using the learning data, and a moving velocity command for the robot is determined using a learned CNN.

Abstract: A method capable of appropriately estimating (specifying) a self-position of a mobile object while appropriately correcting an estimated value of a self-position by an SLAM algorithm is provided. In a self-position estimation method, an actual self-position of a mobile object 1 is specified (fixed) from self-positions estimated by a plurality of algorithms. The plurality of algorithms includes an SLAM algorithm (12) and an algorithm (11) different from the SLAM algorithm. A correction processing unit 16 intermittently corrects an estimated value of a self-position obtained by the SLAM algorithm in accordance with any one self-position out of an estimated value of a self-position obtained by an algorithm other than SLAM and a specified self-position.

Abstract: A mobile body control device includes a route determination unit configured to determine a route of a host mobile body to reduce a change in a movement vector of another mobile body present in the vicinity of the host mobile body, and a control unit configured to move the host mobile body along the route determined by the route determination unit.

Abstract: A mobile object control device includes a route determiner configured to determine a route of a mobile object according to the number of obstacles existing around the mobile object; and a controller configured to move the mobile object along the route determined by the route determiner.

Abstract: This disclosure provides a route determining device capable of determining a route of a moving device such that the moving device smoothly moves to a destination while avoiding an interference with a traffic participant even in a congested traffic environment. A route determining device 1 determines a provisional movement velocity command v_cnn such that an interference between a robot 2 and traffic participants is avoided using the CNN, determines a distance dist between the robot 2 and the traffic participant closest to the robot 2 when the robot is assumed to move from the current position by a command v_cnn in accordance with the reliability P of the command v_cnn, and determines a movement velocity command v of the robot using a DWA such that a target function G including the distance dist and the movement velocity command v of the robot as independent variables has a maximum value.

Abstract: A method for determining a route of a robot is provided such that a moving apparatus can move smoothly to a destination point while avoiding interference with a plurality of moving objects such as traffic participants. In an environment in which a plurality of second pedestrians moves along predetermined movement patterns, a plurality of movement routes when a first pedestrian moves toward a destination point is recognized. Data, in which a compound environmental image constituted of time series of environmental images indicating a visual environment around a virtual robot when the virtual robot moves along each of the plurality of movement routes and a moving direction command indicating a moving direction of the virtual robot are combined, is generated as learning data. Model parameters of a CNN (action model) is learned using the learning data, and a moving velocity command for a robot is determined using a learned CNN.

Abstract: In a learning method, a time series of a movement direction when a reference pedestrian M1 moves to a destination multiple times, a time series of a mask image indicating a positional relationship of nearby pedestrians M2 in a movement direction of the reference pedestrian M1, and a time series of an environment information image 35 are acquired, learning data is created by associating these time series with each other, and a model parameter of a CNN 33a is learned by a back propagation method using the learning data.

Abstract: A learning device 30 extracts feature amount vectors from link data in which movement trajectories of a reference pedestrian M1 and a nearby pedestrian M2 are linked, links the feature amount vectors to condition satisfying data, determines the number of clusters, clusters the feature amount vectors of all clusters corresponding to the number of clusters, acquires the condition satisfying data to which the feature amount vectors are linked as learning data when the clustering of the feature amount vectors of all clusters corresponding to the number of clusters has converged, and learns a model parameter of a movement mode model by a predetermined machine learning algorithm using the learning data.

Abstract: In a self-position estimation method, an actual self-position of a mobile object 1 is specified from self-positions each estimated by using a plurality of algorithms. The plurality of algorithms includes an MCL algorithm (11) and an algorithm (12) different from the MCL algorithm. In a case where reliability of an estimated value of the self-position obtained by using the MCL algorithm is low, the estimated value of the self-position obtained by using the MCL algorithm is corrected using an estimated value of the specified self-position or an estimated value of the self-position obtained by using the algorithm (12).

Abstract: A path determination device and so forth are provided which can determine a path of a robot such that an autonomous mobile robot smoothly moves to a destination while avoiding an interference with a traffic participant even under a traffic environment such as a crowd. A path determination device (1) determines a temporary moving velocity command v_cnn, by using a CNN, such that an interference between a robot (2) and a traffic participant is avoided and determines a moving velocity command v of the robot (2), by using a DWA, such that in a case where the robot (2) is assumed to move from a present position by the temporary moving velocity command v_cnn, an objective function G(v) including a distance dist to the traffic participant closest to the robot (2) and the temporary moving velocity command v_cnn of the robot (2) as independent variables becomes a maximum value.

Abstract: A path determination method includes acquiring plural walking paths Rw of a first pedestrian M1 in a case where walking patterns of plural second pedestrians M2 are set to first to seventh walking patterns, creating plural sets of learning data of a relationship in which a mask image of a virtual robot is associated with a moving direction command for the virtual robot, the mask image and the moving direction command being obtained in a case where the virtual robot moves along each of the plural walking paths Rw in a virtual space, creating a learned model by learning model parameters of a CNN by a gradient method by using the plural sets of learning data, and determining a moving velocity command v for a robot (2) by using the learned model.

Abstract: A camera main body includes a mount, a solid-state imaging element, a main body controller, a card I/F, an accessory information storage section, a rear surface display section, and a finder section. An interchangeable lens is attached to the mount. A setting menu is displayed on the rear surface display section. The main body controller extracts accessory information including an imaging time point in a specific time slot from a plurality of pieces of accessory information read out from a memory card by the card I/F. Further, the main body controller changes a setting menu in accordance with imaging conditions included in accessory information that is extracted as the accessory information including the imaging time point in the specific time slot and is stored in the accessory information storage section.