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.

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 method according to the disclosure configures a processor to predict metal loss in a structure for remediation. The method uses a machine learning model, trained based upon historical data, to predict metal loss over locations of a structure at a time of the prediction. The method identifies from among the predicted locations a high-risk location on the structure in which a magnitude of metal loss indicates potential remediation being needed, dispatches a robotic vehicle to the high-risk location on the structure and inspects the high-risk location using the robotic vehicle to confirm whether the magnitude of metal loss at the location requires remediation. In further methods, remediation is performed. In still further methods, a three-dimensional visualization of the structure is generated with an overlay which depicts predicted metal loss over the sections of the structure.

An object processing system is disclosed that includes a plurality of track sections, and a plurality of remotely actuatable carriers for controlled movement along at least portions of the plurality of track sections, wherein each of the remotely controllable carriers is adapted to support and transport an object processing bin.

A linear transport system includes a stator having a track and coils provided along the track, carriers with a magnet and movable along the track, scales provided on the carriers, sensors provided along the track at respective intervals and configured to detect the carriers to obtain scale positions of the scales, a parameter memory configured to memorize first cumulative values each corresponding to a corresponding sensor, and position calculation circuitry. Each of the first cumulative values is obtained by accumulating, from a reference position to the corresponding sensor, error correction values based on which errors between the respective intervals and measured values of the respective intervals are corrected. The position calculation circuitry is configured to calculate a position of a detected carrier based on detection data of a detecting sensor that has detected the detected carrier and based on the first cumulative value corresponding to the detecting sensor.

Systems and methods for flexible conveyance in an assembly-line or manufacturing process are disclosed. A fleet of self-driving vehicles and a fleet-management system can be used to convey workpieces through a sequence of workstations at which operations are performed in order to produce a finished assembly. An assembly can be transported to a first workstation using a self-driving vehicle, where an operation is performed on the assembly. Subsequently, the assembly can be transported to a second workstation using the self-driving vehicle. The operation can be performed on the assembly while it is being conveyed by the self-driving vehicle.

A chassis and skin of a delivery Autonomous Ground Vehicle include discrete upper and lower thermal management systems. The lower thermal management system is indirect, as is moves air through a closed duct that is in contact with high-heat dissipating components via heat sinks. The upper thermal management system is direct, as it moves air into the interior cavity of the AGV to cool sensors and other electronic equipment.

An object capturing device includes light emission, receiving, and scanning units, and distance calculation, and object determination units. The scanning unit measures light from the emission unit to head toward a measurement target space to perform scanning, and to guide reflected light from the object with respect to the measurement light to the receiving unit. The distance calculation unit calculates a distance to the object in association with a scanning angle of the scanning unit. The object determination unit determines whether the object is a capture target based on whether a scanning angle range within which a difference between distances is equal to or less than a predetermined threshold value corresponding to a reference scanning angle range of the capture target, and a determination of whether intensity distribution of the reflected light within the scanning angle range corresponds to reference intensity distribution of the reflected light from the capture target.

Material logistics system for coordinating transfer of production material so that production material is available as needed at production stations of a manufacturing facility, in particular a series production facility. Thus, multiple sensors are provided for sensing a production material supply at production stations, as well as a central unit in signal-transmitting connection with the sensors and which, based on output signals transmitted from the sensors, determines logistics data relating to the production material for a particular production station. And also, using logistics data, generates control signals for the transfer of production material and provides them for further data processing units. Furthermore, the central unit uses logistics data to control a driverless transport vehicle, having a transport rack including a transport level, for transporting production material accommodated in containers, for a partially automatic container transfer between a transport rack and a storage rack of a production station.

Apparatus and methods related to routing robots are provided. A roadmap of an environment that includes first and second robots can be received. The roadmap can be annotated with unidirectional lanes connecting conflict regions, where each lane ends so to avoid blocking a conflict region. First and second routes for the respective uses of the first and second robots can be determined, where both the first and second routes include a first lane connected to a first conflict region. A first, higher priority and a second, lower priority can be assigned to the respective first and second robots. It can be determined that the second robot following the second route will block the first robot on the first lane. Based on the first priority being higher than the second priority, the computing device can alter the second route to prevent the second robot from blocking the first robot.