The present invention corrects errors made by a machining tool having a discretionary machine configuration, by using a graph in which constituent elements serve as nodes. This control device comprises: a control point coordinate system insertion unit that inserts, as nodes, a control point and a coordinate system, for each node of a machine configuration graph; an identifier allocation unit that allocates an identifier to the inserted control points and coordinate systems; an error information storage unit that stores information relating to a machine error on a control subject, and the identifier allocated to the coordinate system in which the machine error was observed; an error node generation unit that converts the machine error to an equivalent error node; and an error node addition unit that adds the error node to the machine configuration graph.

Robotic platforms and methods of use are disclosed that include: at least one robot or robotic device, at least one computer-based control system, wherein the system is at least in part located on the at least one robot, at least one communications system, wherein the communications system is designed to communicate between the computer-based control system and the at least one robot, and at least one evaluation system that is designed to implement and process at least one nondestructive testing method.

A robot programming device 1 is provided with a model layout unit 112 that lays out a workpiece model of a workpiece, a robot model of a robot, and a tool model of a tool in the virtual space, a machining site designation unit 113 that designates a machining site on the workpiece model, a stereoscopic shape layout unit 115 that lays out a predetermined stereoscopic shape such that a surface of the stereoscopic shape is filled in with a predetermined operation pattern and that the operation pattern is projected to at least one surface of the workpiece model, a machining path creation unit 116 that projects the operation pattern to at least one surface of the workpiece model to create a machining path for the tool, and a change unit 117 that changes the machining path and/or an operation program on the basis of the machining site.

A control device for moving a robot and causing the robot to perform a work onto a work target portion includes a reference position acquisition section that acquires a position of the work target portion as a reference position, based on detection data of a sensor that detects the work target portion, a movement direction acquisition section that acquires a movement direction of the robot, a direction determination section that determines a shift direction in which a working position of the robot onto the work target portion is to be shifted from the reference position, based on the movement direction acquired by the movement direction acquisition section, and a robot control section that positions the robot at a target position shifted from the reference position toward the shift direction by a predetermined shift amount, when carrying out the work onto the work target portion.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using template robotic control plans. One of the methods comprises obtaining a template robotic control plan that is configurable for a plurality of different robotics applications, wherein the template robotic control plan comprises data defining (i) an adaptation procedure and (ii) a set of one or more open parameters; obtaining a user input defining a respective value or range of values for each open parameter in the set of open parameters, wherein the user input characterizes a specific robotics application for which the template robotic control plan can be configured; and executing, using the obtained values for the set of open parameters, the adaptation procedure to generate a specific robotic control plan from the template robotic control plan.

A method is provided. The method includes obtaining an enhanced state graph. The enhanced state graph represents a set of objects within an environment and a set of positions of the set of objects. The enhanced state graph includes a set of object nodes, a set of property nodes and a set of goal nodes to represent a set of objectives. The method also includes generating a set of instructions for a set of mechanical systems based on the enhanced state graph. The set of mechanical systems is configured to interact with one or more of the set of objects within the environment. The method further includes operating the set of mechanical systems to achieve the set of objectives based on the set of instructions.

A computing system including a processing circuit in communication with a robot and a camera having a field of view. The processing circuit obtains image information based on the objects in the field of view and a loading environment, the loading environment which includes loading areas, an object queue, and a buffer zone. The computing system is configured to use the obtained image information in motion planning operations for the retrieval and placement of objects from the object queue into the loading environment. Pallets provided within the loading environment (i.e., within the loading areas) are dedicated to receiving objects having corresponding object type identifiers. The computer system further uses the image information to determine the fill status of pallets existing within the loading environment, and whether new pallets need to be brought into the loading environment and/or swapped out with existing pallets to account for future planning and placement operations.

This application relates to an autonomously navigating robot. In one aspect, the robot includes an end effector configured to measure a person's body temperature and, when the body temperature exceeds a standard fever temperature, activate a chatbot to check symptoms of Covid-19. The robot may also include a manipulator configured to align the end effector with the person's forehead. The robot may further include a mobile robot configured to detect the person and move the end effector and the manipulator to a position where the person is located by performing autonomous navigation.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using learnable robotic control plans. One of the methods comprises obtaining a learnable robotic control plan comprising data defining a state machine that includes a plurality of states and a plurality of transitions between states, wherein: one or more states are learnable states, and each learnable state comprises data defining (i) one or more learnable parameters of the learnable state and (ii) a machine learning procedure for automatically learning a respective value for each learnable parameter of the learnable state; and processing the learnable robotic control plan to generate a specific robotic control plan, comprising: obtaining data characterizing a robotic execution environment; and for each learnable state, executing, using the obtained data, the respective machine learning procedures defined by the learnable state to generate a respective value for each learnable parameter of the learnable state.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing a plan for one or more robots using a process definition graph. One of the methods includes receiving a process definition graph for a robot, the process definition graph having a plurality of action nodes. One or more of the action nodes are motion nodes that represent a motion to be taken by the robot from a respective start location to an end location. It is determined that a motion node satisfies one or more splitting criteria, and in response to determining that the motion node satisfies the one or more splitting criteria, the process definition graph is modified. Modifying the process definition graph includes splitting the motion node into two or more separate motion nodes whose respective paths can be scheduled independently.