Disclosed is a dual-robot position/force multivariate-data-driven method using reinforcement learning. A master robot adopts an ideal position meta-control strategy, learns a desired position by a reinforcement learning algorithm, and feeds back an actual position to a desired position, and a goal is to generate an optimal force while the robot interacts with the environment, as to minimize a position error; and a slave robot, based on a force meta-control strategy of position deviation of the master robot, adopts a damping proportional-derivative (PD) control strategy suitable for an unknown environment, and learns a desired acting force by the reinforcement learning algorithm, namely a minimum force for driving the slave robot to approach a desired reference point. The present invention may improve the dexterity of dual-robot collaboration, solve a parameter optimization problem in position/force control.

A system includes a first sensor having a fixed location relative to a workspace, a second sensor, at least one robotic manipulator coupled to a manipulation tool, and a control system in communication with the at least one robotic manipulator. The control system is configured to determine a location of a workpiece in the workspace based on first sensor data from the first sensor and a three-dimensional (3D) model corresponding to the workpiece. The control system is configured to map a set of 2D coordinates from a second 2D image from the second sensor to a set of 3D coordinates based on the location, and to generate one or more control signals for the at least one robotic manipulator based on the set of 3D coordinates.

Methods and systems according to one or more examples are provided for testing an automated platform, such as a robot. In one example, a system comprises a first robot configured to perform one or more processing operations on a workpiece. The system further comprises a second robot configured to simulate one or more parameters of the workpiece and an associated processing operation to provide one or more test conditions corresponding to each of the one or more processing operations the first robot would perform on the workpiece to test the first robot.

According to at least one aspect, the present disclosure provides a robot system for automatically assembling a modular component and an assembly target, comprising: an assembly robot including a first manipulator, an assembly tool coupled to the first manipulator and configured to assemble the modular component and the assembly target, and a first camera configured to capture images in a direction in which the assemble tool faces; a loading robot including a second manipulator and a gripper coupled to the second manipulator and configured to grip the modular component; and a control device configured to control the assembly robot and the loading robot.

A dual-arm robot assembling system including a controlling unit, a GUI, a first robotic-arm, and a second robotic-arm is disclosed. The GUI provides a graphic program editing page, which provides multiple instruction blocks used for editing a graphical program executed by the assembling system. At least one of the first robotic arm and the second robotic arm is disposed with a point-teaching tool thereon. Before the controlling unit controls the two robotic arms to perform an assembling operation based on the graphical program, a manager may directly drag the two robotic arms through the point-teaching tool, so as to implement a point-teaching procedure for the two robotic arms. Therefore, the assembling system may accomplish the assembling operation through the two robotic arms with cooperative movement.

A human augmented robotics intelligence operation system can include a plurality of robots, each robot having a plurality of sensors; a robot control unit; and one or more articulating joints; a cloud-based robotic intelligence engine having; a communication module; a historical database; and a processor; and a human augmentation platform. The processor can be configured to make a probabilistic determination regarding the likelihood of successfully completing the particular user command. When the probabilistic determination is above a pre-determined threshold, the processor sends necessary executable commands to the robot control unit. Alternatively, when the probabilistic determination is below the predetermined threshold, the processor generates an alert and flags the operation for human review.

Methods and apparatuses for performing automated operations using a high-density robotic cell. An apparatus comprises a first plurality of robotic devices; a second plurality of robotic devices; and a control system. Each of the second plurality of robotic devices is coupled to a single function end effector. The control system controls the second plurality of robotic devices to concurrently perform tasks at a plurality of locations on an assembly, while the first plurality of robotic devices independently maintain a clamp-up at each of the plurality of locations.

A collaborative-robot control system is provided in the invention. The collaborative-robot control system includes a plurality of test machines, a plurality of collaborative robots, a first control system and a second control system. The plurality of test machines are configured in a plurality of paths. When the second control system assigns a first collaborative robot of the plurality of collaborative robots in a waiting area to a first test machine in a first path of the plurality of paths and the first collaborative robot is being blocked by a second collaborative robot of the plurality of collaborative robots in the first path, the second control system generates a push-forward command and transmits the push-forward command to the first control system. The first control system sends the push-forward command to the second collaborative robot to order the second collaborative robot to leave the first path first.

Examples provide a system for decanting items from a set of cases into a set of storage totes in preparation for induction into an automated tote storage device. A set of robotic decanting devices includes at least one robotic de-palletizing device configured to remove a selected case comprising a set of items from a pallet at a de-palletizing station. A stationary robotic case opener device opens each case as it moves along a conveyor device. A set of sensor devices scans cases and/or contents of cases to identify each item removed from each case. A stationary robotic picker device removes each item from each case and places each item into an appropriate destination tote. A robotic tote transfer device moves the destination tote to an induction point of the storage device. A decant manager component updates inventory to include items placed into each tote inducted into the storage device.

Systems and methods for reconfigurable, fixtureless manufacturing are provided. Material handling robots grasp and move parts within an assembly area to adjoin one another in a predetermined orientation. While the parts remain grasped and suspended within the assembly area, out of contact with any fixtures, work surfaces, jigs, and locators, a machine vision system performs an alignment scan to determine locations of datums on the parts which are transmitted to a controller for comparison against stored virtual datums for a subassembly comprising the joined parts. The location of the datums are transmitted to a joining robot which joins the parts to form the subassembly. The machine vision system performs an inspection scan of the datums on the parts after joining.