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.

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.

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.

Systems and methods for loading and delivering stalk subassemblies and yarn packages are disclosed herein. Such systems and methods can have at least one processor, at least one automated guided vehicle, at least one creel assembly, and an automated creel loading assembly. The at least one automated guided vehicle can be communicatively coupled to the at least one processor. The at least one processor can be configured to selectively direct an automated guided vehicle to engage a respective stalk subassembly. Upon engagement between the automated guided vehicle and the stalk subassembly, the processor can be configured to selectively direct the automated guided vehicle to move about and between the selected operative position within the creel assembly and a loading position proximate the automated creel loading assembly.

Systems and methods for autonomous provision replenishment are disclosed. Parts used in a manufacturing process are stored in an intermediate stock queue. When the parts are consumed by the manufacturing process and the number of parts in the queue falls below a threshold, a provision-replenishment signal is generated. One or more self-driving material-transport vehicles, a fleet-management system, and a provision-notification device.

Systems and methods described herein are directed to an environment involving a plurality of robots, wherein for receipt of a plurality of orders, the systems and methods generate a plurality of task batches to fulfill the plurality of orders; generate a parameter set for execution by the plurality of robots to execute the plurality of task batches. For a determination by a controller that one or more of the plurality of robots is to execute the plurality of task batches, the systems and methods load the parameter set; and control the one or more of the plurality of robots based on the loaded parameter set to execute the task batch.

An automatic working system comprises a self-moving device moving and working in a working region, a handheld device and a control module. The handheld device is configured to move along a perimeter of the working region with a user and comprises a detecting module, detecting the perimeter information of the working region; and an input module, receiving a command of the user for detecting the perimeter information. The control module comprises a perimeter setting unit, generating virtual data of the perimeter, an area calculation unit calculating the area of the working region and a scheduling unit generating a working schedule. The self-moving device comprises a working module, a driving module and a controller. The controller controls the self-moving device to work according to the working schedule.

Systems and methods for AI assisted reconfigurable, fixtureless manufacturing is disclosed. The invention eliminates geometry-setting tools (hard points, pins and nets—traditionally known as 3-2-1 fixturing schemes) and to replace the physical geometry setting with virtual datums driven by learning AI algorithms. A first type of part and a second type of part may be located by a machine vision system and moved by material handling devices and robots to locations within an assembly area. The parts may be aligned with one another and the alignment may be checked by the machine vision system which is configured to locate datums, in the form of features, of the parts and compare such datums to stored virtual datums. The parts may be joined while being held by the material handling devices or robots to form a subassembly in a fixtureless fashion. The material handling devices are able to grasp a number of different types of parts so that a number of different types of subassemblies are capable of being assembled. The system enables one skilled in the art to develop a product design with self-locating parts that will eliminate and minimize the need for geometry setting dedicated line tools and fixtures. This leads to the development of a manufacturing process that utilizes the industry 4.0 technologies to once again eliminate or significantly reduces the need for geometry setting line tools.

The vehicle body assembly station comprises main transport assembly for conveying a vehicle body along a first direction D1 in which at least one assembly robot is provided to move along a second direction D2, and temporary transport assembly whose operation is more accurate than that of the main transport assembly for moving the vehicle body independently from the main transport assembly while the assembly robot is performing operations on the vehicle body, whereby a new coordinate reference system is created by the temporary transport assembly.