A teleoperation assist device includes: a motion acquiring unit configured to acquire information on a motion of an operator who operates an end effector; an intention estimating unit configured to estimate a target object which is a target operated by the end effector and a task which is an operation method of the target object using the information acquired by the motion acquiring unit; an environmental status determining unit configured to acquire environment information of an environment in which the end effector is operated; a parameter setting unit configured to acquire information from the intention estimating unit and the environmental status determining unit and to set parameters of an operation type of the end effector from the acquired information; and an end effector operation determining unit configured to determine an amount of operation of the end effector on the basis of taxonomy which is the set parameters and the information acquired by the motion acquiring unit.

Systems, methods, and computer-readable media for robotic joining of components, parts, and structures are disclosed. A method in accordance with an aspect of the present disclosure comprises determining a target first position and a target second position in a reference frame, controlling robotic arms to move a first part to the target first position and a second part to the target second position, measuring the parts at the target first and second positions to obtain a measured first and second positions, performing a first operation to determine differences between the measured positions and the target positions, and when the differences exceeds desired tolerances, controlling the robotic arms to move the parts to compensate for the differences, and controlling at least the first or second robotic arm to join the first and second parts after the first and second operations are concluded.

A method includes: (a) causing a robotic arm to position a contacting structure of the arm laterally in a horizontal direction in relation to a first subject on a target object; (b) causing the arrn to bring the contacting structure into contact with at least three locations on the first subject; (c) detecting positions of the contacting structure in relation to the robot when contacting the locations; (d) detecting a position of the first subject in relation to the robot by using the detected positions of the contacting structure; (e) performing same steps as the steps (a) to (d) for a second subject on the target object; and (f) detecting a position of the robot in relation to the target object by using the positions of the subjects in relation to the robot and using positions of the subjects in relation to the target object.

A control device includes a processor that calculates one or more predetermined command values for one or more robots to undergo synchronous control in predetermined control cycles, an output unit that outputs the one or more predetermined command values in each of the predetermined control cycles, and a generator that generates an output signal for a virtual robot. The virtual robot is virtually defined in relation to the synchronous control. The processor calculates the one or more predetermined command values using the output signal for the virtual robot generated by the generator.

A hand device is attached to a robot arm, grips a workpiece extending helically around a helical axis, and includes a base attached to the robot arm and a gripping part that is supported by the base in a rotatable manner around a predetermined rotation axis and that grips the workpiece. The gripping part grips the workpiece on the predetermined rotation axis such that the helical axis of the workpiece substantially extends along the predetermined rotation axis, and rotates in a helical direction of the workpiece in accordance with an external force acting on the workpiece in a tangential direction around the predetermined rotation axis.

Methods and systems to remotely operate robotic devices are provided. A number of embodiments allow users to remotely operate robotic devices using generalized consumer devices (e.g., cell phones). Additional embodiments provide for a platform to allow communication between consumer devices and the robotic devices. Further embodiments allow for training robotic devices to operate autonomously by training the robotic device with machine learning algorithms using data collected from scalable methods of controlling robotic devices.

A system 100 for sheet coil packaging is provided. The system 100 preferably comprises: a sheet coil rotating arrangement 120, arranged to rotate a sheet coil 116 to enable it to be wrapped; first 112 and second 113 industrial robots, having first 108 and second 109 robot arms, arranged to wrap the sheet coil 116 using a wrapping tool 110, using sequences of the first robot arm 108 inserting the wrapping tool 110 into a central hole 118 of the sheet coil 116 and handing over the wrapping tool 110 to the second robot arm 109, and the second robot arm 109 transporting the wrapping tool 110 along the outside of the sheet coil 116 and handing it back to the first robot arm 108, as the sheet coil 116 is rotated by the sheet coil rotating arrangement 120; and two outer edge protection mounting devices 210, 220, arranged at opposite ends of the sheet coil 116 to feed out edge protection material 250 along an outer edge of the sheet coil 116 as the sheet coil 116 is rotated by the sheet coil rotating arrangement 120. The first 112 and second 113 industrial robots are preferably arranged to wrap the sheet coil 116 in synchronization with the feeding out of the edge protection material 250, thereby fixing the edge protection material 250 to the outer edges of the sheet coil 116 by the wrapping as the sheet coil 116 is rotated by the sheet coil rotating arrangement 120.

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.

A robot-assisted surgical system has a user interface operable by a user, a first robotic manipulator having a first surgical instrument, and a second robotic manipulator having a second surgical instrument. The system receives user input in response to movement of the input device by a user and causes the manipulator to move the first surgical instrument in response to the user input, determines a vector defined by the position of the first surgical instrument relative to the second surgical instrument, generates dynamic control signals based on the determined vector, and causes the manipulator to move the second surgical instrument in response to said dynamic control signals.

Systems and methods for collision detection and avoidance are provided. In one aspect, a robotic medical system including a first set of links, a second set of links, a console configured to receive input commanding motion of the first set of links and the second set of links, a processor, and at least one computer-readable memory in communication with the processor. The processor is configured to access the model of the first set of links and the second set of links, control movement of the first set of links and the second set of links based on the input received by the console, determine a distance between the first set of links and the second set of links based on the model, and prevent a collision between the first set of links and the second set of links based on the determined distance.