A system includes a machine tool 10, a robot 25 having a camera 31, and a transfer device 35 having the robot 25 mounted thereon, and an identification figure is arranged in a machining area of the machine tool 10.

A dynamic planning controller receives a maneuver for a robot and a current state of the robot and transforms the maneuver and the current state of the robot into a nonlinear optimization problem. The nonlinear optimization problem is configured to optimize an unknown force and an unknown position vector. At a first time instance, the controller linearizes the nonlinear optimization problem into a first linear optimization problem and determines a first solution to the first linear optimization problem using quadratic programming. At a second time instance, the controller linearizes the nonlinear optimization problem into a second linear optimization problem based on the first solution at the first time instance and determines a second solution to the second linear optimization problem based on the first solution using the quadratic programming. The controller also generates a joint command to control motion of the robot during the maneuver based on the second solution.

A production system includes a machine tool (10), a robot (25) having a camera (31), an automatic guided vehicle (35) having the robot (25) mounted thereon, and a controller (40) controlling the automatic guided vehicle (35) and the robot (25), and has an identification figure arranged in a machining area of the machine tool (10). The controller (40) stores, as a reference image, an image of the identification figure captured by the camera (31) with the robot (25) in an image capturing pose in a teaching operation. When repeatedly operating the automatic guided vehicle (35) and the robot (25), the controller (40) estimates an amount of error between a pose of the robot (25) in the teaching operation and a current pose of the robot (25) based on the reference image and an image of the identification figure captured by the camera (31) with the robot (25) in the image capturing pose, and corrects operating poses of the robot (25) based on the estimated amount of error.

A task table for use in a robotic system comprising a robot mounted on a carriage configured to move the robot along a path is disclosed. The task table includes a substantially horizontal table surface, and one or more mounting arms to which the table surface is affixed at a first distal end and comprising, at a second distal end opposite the first distal end, one or more mounting structures, the one or more mounting arms having a length that allows the table surface to be mounted to the robot or the carriage at a distance from a base of the robot that enables the robot to reach a plurality of locations on the table surface each with a desired pose.

An information processing system according to an embodiment includes processing circuitry. The processing circuitry determines whether or not processing related to an object disposed in an environment is appropriate based on information related to the object. When determining that the processing is not appropriate, the processing circuitry adds label information designated by a user to data on the object.

A robot system includes a robot and a controller that controls the robot. The robot includes a wheeled platform and a manipulator mounted on the wheeled platform. The manipulator includes a sensor that detects a force or a moment that acts on at least one joint. The controller controls at least one of the manipulator and the wheeled platform on the basis of the force or moment detected by the sensor so that a moment acting on the wheeled platform does not exceed a tip-over moment.

An automatic wheel changer robot has a includes a drive assembly, a torque gun, a sensor assembly, and a controller. The drive assembly has a mobile base and two wheel-clamping assemblies, each configured to engage a wheel. The controller generates a set of instructions based, at least in part, on information obtained from the sensor assembly. The drive assembly uses the set of instructions to cooperatively remove respective wheels from respective hubs on a vehicle and/or attach respective wheels to respective hubs on a vehicle. The device may have lidar sensors and Mecanum wheels that the controller is programmed to use to move between respective hubs and wheel storage locations install wheels, replace wheels, rotate tires, and perform similar operations.

Described is a system (and method) for locating a center point of a lug nut for an automated vehicle wheel removal system. To improve the accuracy of the center point, the system may perform machine learning inferences using two-dimensional (2D) and three-dimensional (3D) image data. The system may process a 2D image to infer an initial center point, and potentially improve the accuracy by leveraging a 3D image. More particularly, the system may process a 3D image to infer a location of one or more edges (or edge points) around the perimeter of the lug nut and measure a set of distances between the initial center point and the located set of edges. The system may then refine (or adjust) the center point based on such measurements.

A conveyance robot system according to the present disclosure includes a conveyance robot, and a robot control unit configured to control an operation of picking up an object performed by the conveyance robot, wherein the robot control unit determines that a movable range area, which is an area outside a safety cover where a robot arm is operated, satisfies a safety ensuring condition that can regard safety of the movable range area as equivalent to the safety inside the safety cover and allow the robot arm to perform a work while projecting toward the shelf.

A mobile robot system for automated operation of a data center or telecommunications office, includes a moveable robotic platform with a multiplicity of tools integrated therein, to operate on a network element within a bay, with integrated RFID (radio-frequency identification) tags and visual alignment markers attached to fiber optic connectors and ports of the network elements. The mobile robot system positions a robot probe arm with an RFID probe for proximity detection to identify a cable and associated fiber optic connector based on a unique RF identifier of a tag on the fiber optic connector. The robot probe arm has a connector gripper to engage and unplug the associated fiber optic connector.