A device for ensuring safe operation of a robot used for cosmetics applications, including the retrofitting of robots not originally design for such applications. In some embodiments, the robot is used for the automatic placement of eyelash extensions onto the natural eyelashes of a subject. In some embodiments, a safety barrier is provided by a physical barrier or light curtain. In other embodiments, readily deformable end effectors are used.

A surgical robotic system including a surgical table, a surgical robotic manipulator coupled to the surgical table and comprising a plurality of links coupled together by a plurality of joints that are operable to move with respect to one another to move the surgical robotic manipulator, at least one of the plurality of links or the plurality of joints having a portion that faces another of the plurality of links or the plurality of joints, a proximity sensing assembly coupled to the portion of the at least one of the plurality of links or the plurality of joints, the proximity sensing assembly operable to detect an object prior to the surgical robotic manipulator colliding with the object and to output a corresponding detection signal, and a processor operable to receive the corresponding detecting signal and cause the manipulator or the object to engage in a collision avoidance operation.

In various examples, an obstacle detector is capable of tracking a velocity state of detected objects or obstacles using LiDAR data. For example, using LiDAR data alone, an iterative closest point (ICP) algorithm may be used to determine a current state of detected objects for a current frame and a Kalman filter may be used to maintain a tracked state of the one or more objects detected over time. The obstacle detector may be configured to estimate velocity for one or more detected objects, compare the estimated velocity to one or more previous tracked states for previously detected objects, determine that the detected objects corresponds to a certain previously detected object, and update the tracked state for the previously detected object with the estimated velocity.

A control device according to one or more embodiments may control a robot that performs a collaborative work with a worker. The control device may include: a storage section storing an operation program to cause the robot to perform the collaborative work with the worker; a control section controlling the robot based on the operation program when the collaborative work is performed; a calculation section calculating a motion of the worker when the collaborative work is performed; and a correction section correcting the operation program based on the motion of the worker calculated by the calculation section.

A technique shortens the time taken to adjust a protection area. An area setting device includes a setting unit that sets a protection area in at least a part of a surrounding environment of a robot to detect an entry of an object, an obtainer that obtains surrounding information about the robot, and a storage prestoring a set value for the protection area and the surrounding information associated with each other. The setting unit sets the protection area based on the set value read from the storage.

A method of scaling a desired velocity of a tool of a surgical robot with a processing unit includes receiving an input signal, determining a position of the tool relative to a boundary of a surgical site, and scaling a desired velocity of movement of the tool when the tool is within a predetermined distance of the boundary of the surgical site. The input signal includes the desired velocity of movement of the tool.

A model prediction control part of a control device includes an obstacle avoidance control unit that operates when there are a plurality of actual obstacles to be avoided. The obstacle avoidance control unit decides the position of a virtual obstacle from the positions of the plurality of actual obstacles acquired by an acquisition part so as to be positioned between the plurality of actual obstacles, and performs model prediction control by using, as the stage cost, the addition result of a standard cost and a virtual obstacle evaluation term for which a prescribed function, which uses, as parameters, at least the position of the virtual obstacle and the position of a moving body, is multiplied by a virtual obstacle weight. Using this configuration, when the moving body is caused to follow with respect to a target trajectory by the model prediction control, a collision with an obstacle can be avoided suitably.

Methods of calibrating a position of a component include providing a robot with a gripper and crush and crash sensors, a calibration tool coupled to the gripper, and the component, which has a recess and a crush zone. The methods also include moving the gripper in a first direction to sense contact between the calibration tool and the crush zone, recording the contact position, and moving the gripper to insert the tool into the recess. The gripper is then moved in second directions to sense contact between the tool and the recess and moved in third directions to also sense contact between the tool and the recess. The methods further include recording and processing the contact positions to determine a surface location in the first direction and a physical center of the recess. Robot calibration apparatus for performing the method is also disclosed, as are other aspects.

Crosstalk mitigation among cameras in neighboring monitored workcells is achieved by computationally defining a noninterference scheme that respects the independent monitoring and operation of each workcell. The scheme may involve communication between adjacent cells to adjudicate non-interfering camera operation or system-wide mapping of interference risks and mitigation thereof. Mitigation strategies can involve time-division and/or frequency-division multiplexing.

A robot-connected IoT-based sleep-caring system includes a sleep-caring robot and an IoT system. The sleep-caring robot includes environment monitoring, physiology monitoring, sleep monitoring, sound, lighting and electricity control, a smart storage compartment, central data processing, and machine arms. The IoT system senses and executes instructions from the sleep-caring robot, thereby catering to bedroom activities of the user.