Provided is a method wherein a movement or a manipulation of an object by a robot, observing constraints, from a starting condition to the manipulation target, is generated, wherein the manipulation is divided into different manipulation modes or sections, which include different constraints, wherein a plurality of manipulation mode-specific controllers for controlling partial manipulations in the different manipulation modes for sections are randomly generated and an optimized sequence of the controllers is randomly generated, wherein the controllers specify a vector field or a directional field, wherein a simulation module simulates the manipulation or movement for each of the controller sequences and determines an expense value or a cost value quantifying a reaching of the manipulation target, and wherein the controller sequence and the expense value are supplied to a machine learning module as training data in order to indicate an expense-optimized controller, which optimizes or minimizes the expense value.

A trajectory generating method includes a first generating process of generating a plurality of trajectories between a start teaching point and a target teaching point, an evaluation process of evaluating a motion of the robot arm on each trajectory to calculate an evaluation value of each trajectory, a selection process of selecting one of the plurality of trajectories based on calculated evaluation values, and an update process of updating the trajectory by repeating the processes of generating a plurality of new trajectories by changing a selected trajectory in the selection process, of calculating an evaluation value of a motion of the robot arm on each changed trajectory and of selecting a trajectory based on calculated evaluation values.

A control device, for controlling a robot that performs a task, according to one or more embodiments may be configured to cause the robot to perform the task. When a distance between the robot and a person is less than a predetermined value, the control device may be configured to change a movement path of the robot and to adjust the predetermined value in accordance with a workpiece held by the robot.

A virtualization system implemented within a cloud server enables the simulation of robot structure and behavior in a virtual environment. The simulated robots are controlled by clients remote from the cloud server, enabling human operators or autonomous robot control programs running on the clients to control the movement and behavior of the simulated robots within the virtual environment. Data describing interactions between robots, the virtual environment, and objects can be recorded for use in future robot design. The virtualization system can include robot templates, enabling users to quickly select and customize a robot to be simulated, and further enabling users to update and re-customize the robot in real-time during the simulation. The virtualization system can re-simulate a portion of the robot simulation when an intervention by a human operator is detected, positioning robots, people, and objects within the virtual environment based on the detected intervention.

A robot collision detection device and a method thereof are provided. The robot collision detection device includes a buffer that periodically stores a driving command for allowing a robot to move to a destination and a sensor that detects a behavior of the robot. A controller monitors the driving command and a behavior of the robot corresponding to the driving command, and determines whether there is a robot collision based on the driving command and the behavior of the robot.

A method for operating at least one robot includes determining the minimum distance of the robot from an obstacle, in particular the closest obstacle to the robot, in particular excluding at least one previously known, in particular temporary, obstacle; reducing the maximum speed of the robot if this minimum distance is below a first minimum distance; and reducing this maximum speed of the robot more if the minimum distance is below a second minimum distance which is smaller than the first minimum distance.

A control device controlling a robot including a robot arm, a drive section causing the robot arm to pivot around a pivot axis, a shaft that is provided at a position of the robot arm different from the pivot axis and that moves parallel to the pivot axis, and an angular velocity sensor that is provided in the robot arm and that detects angular velocity around an axis orthogonal to an axial direction of the pivot axis and parallel to a plane including the pivot axis and an axis of the shaft, the control device includes a processor that is configured to control the robot, wherein the processor is configured to perform feedback control on the drive section based on the angular velocity.

The automatic machine includes a movable member driven by a motor, a tool that is attached to the movable member and includes a movable mechanism that operates independently of the movable member, a sensor that is attached to the movable mechanism and measures a location of the movable mechanism, and a processor configured to detect, based on a location of the movable mechanism measured by the sensor when the motor is controlled in accordance with a command to move the tool to a target location and on an amount of movement of the movable mechanism from a reference point, detects a first location in a case in which the movable mechanism is virtually fixed to the reference point, and calculate a correction amount for a control amount of the motor in such a way as to decrease a difference between the target location and the first location.

In a robot stopping method, if a shaft that moves at least a part of the robot in the gravity direction is defined as a Z shaft and a time at which the occurrence of the a power failure has been detected is set as a reference time, a first control is performed if the occurrence of the power failure has been detected when the Z shaft is in a state of ascent, in which based on an ascending speed of the Z shaft at the reference time, a time until a speed of the Z shaft reaches zero as a result of deceleration by a gravitational acceleration is calculated as a deceleration time, the Z shaft is driven so as to decelerate and stop ascent of the Z shaft by spending the deceleration time.

Systems and methods related to intelligent grippers with individual cup control are disclosed. One aspect of the disclosure provides a method of determining grip quality between a robotic gripper and an object. The method comprises applying a vacuum to two or more cup assemblies of the robotic gripper in contact with the object, moving the object with the robotic gripper after applying the vacuum to the two or more cup assemblies, and determining, using at least one pressure sensor associated with each of the two or more cup assemblies, a grip quality between the robotic gripper and the object.