A control device includes a machine learning unit that performs machine learning of control for an operation of a control target device, an avoidance command value calculation unit that obtains an avoidance command value that is a control command value for the control target device, the control command value which satisfies constraint conditions including a condition for the control target device not to come into contact with an obstacle, and the control command value that an evaluation value obtained by applying the control command value to an evaluation function satisfies a prescribed end condition, and a device control unit that controls the control target device on the basis of the avoidance command value, in which a parameter value obtained through the machine learning in the machine learning unit is reflected in at least one of the evaluation function and the constraint condition.

An obstacle avoidance control device includes an avoidance command value calculation unit that obtains an avoidance command value that is a control command value for control target equipment, the control command value which satisfies constraint conditions including a condition sufficient for the control target equipment not to come into contact with an obstacle, and the control command value that an evaluation value obtained by applying the control command value to an evaluation function satisfies a prescribed end condition, and an equipment control unit that controls the control target equipment on the basis of a processing result of the avoidance command value calculation unit.

A robot motion planning technique using an external computer communicating with a robot controller. A camera or sensor system provides input scene information including start and goal points and obstacle data to the computer. The computer plans a robot tool motion based on the start and goal points and the obstacle environment, where the robot motion is planned using either a serial or parallel combination of sampling-based and optimization-based planning algorithms. In the serial combination, the sampling method first finds a feasible path, and the optimization method then improves the path quality. In the parallel combination, both sampling and optimization methods are used, and a path is selected based on computation time, path quality and other factors. The computer converts dense planned waypoints to sparse command points for transfer to the robot controller, and the controller computes robot kinematics and interpolation points and controls the movement of the robot.

Various approaches to ensuring safe operation of industrial machinery in a workcell include disposing multiple image sensors proximate to the workcell and acquiring, with at least some of the image sensors, the first set of images of the workcell; registering the sensors to each other based at least in part on the first set of images and, based at least in part on the registration, converting the first set of images to a common reference frame of the sensors; determining a transformation matrix for transforming the common reference frame of the sensors to a global frame of the workcell; registering the sensors to the industrial machinery; acquiring the second set of images during operation of the industrial machinery; and monitoring the industrial machinery during operation thereof based at least in part on the acquired second plurality of images, transformation, and registration of the sensors to the industrial machinery.

A human-robot collaboration system, including at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to: predict a human atomic action based on a probability density function of possible human atomic actions for performing a predefined task; and plan a motion of the robot based on the predicted human atomic action.

Systems and a method are provided for programming a cobot for a plurality of cells of an industrial environment. A physical cobot is provided within a lab cell comprising physical lab objects. A virtual simulation system receives information inputs on a virtual cobot representing the physical cobot, regarding a virtual lab cell comprising virtual lab objects, and on a plurality of virtual industrial cells comprising virtual industrial objects. Inputs are received from the physical cobot's movement during teaching whereby the physical cobot is moved in the lab cell to the desired position(s) while providing, via a user interface, a visualization of the virtual cobot's movement within a meta cell generated by superimposing the plurality of virtual industrial cells with the virtual lab cell, so that collisions with any object are minimized. A robotic program is generated based on the received inputs of the physical cobot's movement.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning a path of motion for a robot. In some implementations, a candidate path of movement is determined for each of multiple robots. A swept region, for each of the multiple robots, is determined that the robot would traverse through along its candidate path. At least some of the swept regions for the multiple robots is aggregated to determine amounts of overlap among the swept regions at different locations. Force vectors directed outward from the swept regions are assigned, wherein the force vectors have different magnitudes assigned according to the respective amounts of overlap of the swept regions at the different locations. A path for a particular robot to travel is determined based on the swept regions and the assigned magnitudes of the forces.

A method of stacking goods by a robot, a system of controlling the robot to stack the goods, and the robot are provided in the field of robot control. The method includes: acquiring a current pose and a target pose of the goods; obtaining a collision-free motion trajectory of the robot and/or an end effector of the robot based on the current pose and the target pose of the goods; and controlling the robot to place the goods in the target pose in accordance with the collision-free motion trajectory. The method of stacking goods by the robot, the system of controlling the robot to stack the goods, and the robot are configured to realize a process of fully automated stacking the goods in the scenario of logistically loading or unloading the goods. The efficiency of loading or unloading the goods can be improved, and the labor cost can be reduced.

A method for operating the X-ray device, which includes a detector, a radiation source, or a C-arm including the detector and the radiation source, and an articulated arm and a base. Initially, a starting position of the X-ray device is specified with respect to the detector, the radiation source, or the C-arm, and the articulated arm, and an end position of the X-ray device is specified at least with respect to the detector, the radiation source, or the C-arm. A plurality of paths that may be followed by the articulated arm and the detector, the radiation source, or the C-arm on movement from the starting position into the end position are automatically determined. One path of the plurality of paths for the movement of the X-ray device is selected, and the X-ray device is moved into the end position.

Automatic calculation of a trajectory by taking the interference with an obstacle into account may be performed by calculating trajectory information representing a trajectory on which (i) the picking hand picks, on the first position, the work with a posture associated with the first position included in the first combination; and (ii) the picking hand arranges, on the second position, the work with a posture associated with the second position included in the first combination, and determining whether an interference is present or absent on the trajectory represented by the trajectory information calculated in the calculating a trajectory.