An autonomous mobile apparatus that autonomously docks with a docking station, includes a main body including at least one connection unit connected to the docking station, a drive unit configured to move the main body, and a processor configured to control the drive unit, wherein the processor controls operation of the drive unit in a first mode for causing the main body to move in proximity to the docking station and a second mode for bringing the connection unit into contact with the docking unit of the docking station.

A system for autonomously measuring workpieces, the system comprising one or more mobile robots, configured to move autonomously in a production environment with a plurality of production facilities that produce a plurality of different workpieces, each of the mobile robots comprising a spatial localization system for deriving a location of the mobile robot in the production environment, an autonomous navigation and propulsion unit configured for providing mobility of the mobile robot in the production environment, a wireless communication interface providing a data link to at least one other mobile robot and/or to computation and storage system, wherein a first mobile robot comprises a sensor setup comprising one or more sensors and is configured to use one or more of the sensors for identifying a workpiece to be measured and for determining an at least rough position of the workpiece that allows collecting or measuring the workpiece.

An autonomous vehicle includes a control system, an array of sensors, processing logic, and a switch. The processing logic generates operation instructions based on sensor data and the control system controls the autonomous vehicle based on the operation instructions. The array of sensors generate the sensor data that is related to objects in an external environment. The switch is coupled between the sensors and the processing logic to buffer the processing logic from the sensor data. The switch is further coupled between the processing logic and the control system to provide the operation instructions from the processing logic to the control system. The switch includes a prioritization engine that prioritizes an order of transmission, from the switch to the processing logic, of the first sensor data over the second sensor data based on received vehicle operation data.

A system for transportation includes a vehicle having a user interface, and a robotic process automation system wherein a set of data is captured for each user in a set of users as each user interacts with the user interface, and wherein an artificial intelligence system is trained using the set of data to interact with the vehicle to automatically undertake actions with the vehicle on behalf of the user.

Provided is a multi-vehicle collaborative trajectory planning method, apparatus (600) and system, and a device, a storage medium, and a computer program product. The method comprises: determining a specific number of different multi-vehicle priority schemes for multiple vehicles (S101); determining, by using a sequential planning policy, a corresponding collaborative planning scheme for each multi-vehicle priority scheme (S102); performing quality evaluation on each collaborative planning scheme to obtain a quality evaluation result (S103); and according to the quality evaluation result, determining a target collaborative planning scheme from the specific number of collaborative planning schemes (S104).

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

An unmanned vehicle control method includes acquiring a delivery request for an item, the delivery request comprising delivery information of the item, and determining, according to the delivery information, predicted travelling data associated with delivering the item and at least one of network coverage or network connection quality associated with the predicted travelling data. The method further includes allocating network resources according to the at least one of the network coverage or the network connection quality of the predicted travelling data, and generating a remote driving control instruction according to the predicted travelling data. The method further includes transmitting the remote driving control instruction to an unmanned vehicle using the allocated network resources, so as to cause the unmanned vehicle to drive based on the remote driving control instruction, the unmanned vehicle being configured to transport the item.

Disclosed are a material pushing apparatus and a charging method thereof, and a material pushing machine and a material pushing method thereof. The material pushing apparatus comprises a charger (200) and a material pushing machine (100), and when the material pushing machine moves to the position where the charger is located, the charger can automatically supplement electric energy to the material pushing machine, such that the automation level of the material pushing apparatus is improved.

A safety device according to the present disclosure includes a sensor that is attached to a self-propellable travel device or a robot provided to the travel device, is set with a given detection area on the basis of a position of the sensor, and detects an object existing within the given detection area. The safety device further includes a motion suppressing device that suppresses motions of the travel device and the robot, when the existence of the object within the given detection area is detected by the sensor, and an area changing device that changes the given detection area according to operating states of the travel device and the robot.

A management system includes a course data generation unit that generates course data for each of a plurality of unmanned vehicles such that loading work for the plurality of unmanned vehicles by a loader is sequentially performed on a work site where a plurality of the loaders operates; and a priority determination unit that determine a passage order at an intersection on the work site of the plurality of unmanned vehicles traveling according to the course data so as to reduce a total loading loss indicating a total of loss amounts in operation of each of the plurality of the loaders.