A control apparatus is a control apparatus that controls a first manipulator including a detection acquisition unit that acquires information from a first detection unit detecting that at least one of a living organism and an object is located within a first range, a velocity acquisition unit that acquires a velocity of a second manipulator different from the first manipulator, and a control unit that controls a velocity of the first manipulator to be equal to or less than a first velocity, wherein the control unit controls the velocity of the first manipulator so that a relative velocity between the first manipulator and the second manipulator may be equal to or less than a second velocity when the detection acquisition unit acquires the information.

A robot controller (2) configured to control a robot (1) including a plurality of joints (J.sub.1-J.sub.6) each rotatable around a rotation axis, the robot controller (2) including: an acquisition unit (21) configured to acquire a rotation angle of each of the plurality of joints (J.sub.1-J.sub.6); a determination unit (22) configured to determine whether or not the robot (1) has been in proximity to a singular configuration, based on the rotation angle of each of the plurality of joints (J.sub.1-J.sub.6); and a control unit (23) configured to control the plurality of joints (J.sub.1-J.sub.6) to be rotated not to rotate simultaneously, when the determination unit (22) determines that the robot (1) has been in proximity to the singular configuration.

The parallel link mechanism is applied to a work device in which a link actuation device and a combined-side actuator are combined. A control device includes a storage that stores a plurality of work coordinates as well as a work-point movement velocity as a target velocity of an end effector and a posture change velocity as a target angular velocity to be set for changing the posture of the end effector. A controller includes a switching function unit that switches the target velocity used for calculating movement velocities of the respective posture control actuators and a movement velocity of the combined-side actuator, to the work-point movement velocity and to the posture change velocity.

Various embodiments for enforcing safe operation of machinery performing an activity in a three-dimensional (3D) workspace includes computationally generating a 3D spatial representation of the workspace; computationally mapping 3D regions of the workspace corresponding to space occupied by the machinery and a human; and based thereon, restricting operation of the machinery in accordance with a safety protocol during physical performance of the activity.

A tool control system may include: a tactile sensor configured to, when a tool holds a target object and slides the target object downward across the tool, obtain tactile sensing data from the tool; one or more memories configured to store a target velocity and computer-readable instructions; and one or more processors configured execute the computer-readable instructions to: receive the tactile sensing data from the tactile sensor; estimate a velocity of the target object based on the tactile sensing data, by using one or more neural networks that are trained based on a training image of an sample object captured while the sample object is sliding down; and generate a control parameter of the tool based on the estimated velocity and the target velocity.

A multipurpose robot arm having a controller configured to control the motion hereof during an operation process according to a plurality of basic operation commands Wherein the robot controller is configured to control the multipurpose robot arm in a standard mode of operation according to a first subset of the basic operation commands and in an application specific operation mode during part of the robot arm operation process according to a second subset of the basic operation commands. Wherein basic operation commands of the second subset are at least partly comprised by the first subset and wherein at least one of the operation parameters of the second subset is limited by a application operation value. Wherein the application operation value is defined by a desired property of the operation of the multipurpose robot arm in the application specific operation mode.

A method for determining a dynamic friction torque of a frictional brake device of a joint of an industrial robot, the method including performing a disengaged brake movement of an electric motor of the joint while the brake device is disengaged; determining a disengaged brake torque value based on a torque reference of a control loop of the electric motor during the disengaged brake movement; performing an engaged brake movement of the electric motor while the brake device is engaged; determining an engaged brake torque value based on a torque reference of the control loop during the engaged brake movement; and determining the dynamic friction torque of the brake device based on a difference between the engaged brake torque value and the disengaged brake torque value. A control system and an industrial robot are also provided.

A modular robotic vehicle (MRV) having a modular chassis configured for a vehicle utilizing two-wheel steering, four-wheel steering, six-wheel steering, eight-wheel steering controlled by a semiautonomous system or an autonomous driving system, either system is associated with operating modes which may include a two-wheel steering mode, an all-wheel steering mode, a traverse steering mode, a park mode, or an omni-directional mode utilized for steering sideways, driving diagonally or move crab like. Accordingly, during semiautonomous control a driver of the modular robotic vehicle may utilize smart I/O devices including a smartphone, tablet like devices, or a control panel to select a preferred driving mode. The driver may communicate navigation instructions via smart I/O devices to control steering, speed and placement of the MRV in respect to the operating mode. Accordingly, GPS and a wireless network provides navigation instructions during an autonomous operation involving driving, parking, docking or connecting to another MRV.

Various approaches to solve for inverse kinematics may be used for teleoperation of a surgical robotic system. In one approach, an iterative solver solves for the linear component of motion independently from solving for the angular component of motion. One solver may be used to solve for both together. In another approach, all limits (e.g., position, velocity, and acceleration) are handled in one solution. Where a limit is reached, the limit is used as a bound in the intermediate solution, allowing solution even where a bound is reached. In another approach, a ratio of limits of position are used to create a slow-down region near the bounds to more naturally control motion. In yet another approach, the medical-based teleoperation uses a bounded Gauss-Siedel solver, such as with successive-over-relaxation.

Spatial regions potentially occupied by a robot (or other machinery) or portion thereof and a human operator during performance of all or a defined portion of a task or an application are computationally estimated. These “potential occupancy envelopes” (POEs) may be based on the states (e.g., the current and expected positions, velocities, accelerations, geometry and/or kinematics) of the robot and the human operator. Once the POEs of human operators in the workspace are established, they can be used to guide or revise motion planning for task execution.