There is provided a mobile robot that performs the obstacle avoidance, positioning and object recognition according to image frames captured by the same optical sensor. The mobile robot includes an optical sensor, a light emitting diode, a laser diode and a processor. The processor identifies an obstacle and a distance thereof according to image frames captured by the optical sensor when the laser diode is emitting light. The processor further performs the positioning and object recognition according to image frames captured by the optical sensor when the light emitting diode is emitting light.

A friction compensation device of the present disclosure includes a drive torque calculation unit that calculates output torque of a transmission mechanism from a motor's position, velocity, and acceleration, the transmission mechanism being connected to a motor via a shaft to transmit the driving force of the motor, and a friction estimate value calculation unit that calculates a friction estimate value that is an estimate value of a friction force on the shaft. The friction estimate value calculation unit includes a friction correction value calculation unit that calculates a friction correction value to correct the friction force on the shaft, in accordance with the output of the drive torque calculation unit.

A robot cleaner according to the present invention includes a body provided with a driving unit for movement, a position recognition unit provided in the body to recognize a position of the body, a storage unit configured to store, on a map, a region cleaned while the body is moving by the driving unit, and a control unit configured to control the driving unit, wherein the control unit determines whether a charging stand exists in a cleaning completed region on the map stored in the storage unit when a return condition that the body returns to the charging stand is satisfied, searches for an uncleaned region when the charging stand is not located in the cleaning completed region, and controls the driving unit such that the body moves from a current position to a point in a found uncleaned region or a point around the found uncleaned region.

A robotic arm comprising an operation end, a base, a sensor unit and a control unit is provided. The operation end is connected to the base, and the operation end is configured to reach an operational area. The sensor unit provides a sensor signal according to the force applied by or the motion of an operator. When the operation end reaches the operational area, the control unit sets a fixed position on the robotic arm between the base and the operation end. When the sensor signal from the operator fulfills a default condition, the control unit moves the robotic arm away from the operator, without moving the fixed position on the robotic arm.

Control systems for industrial machinery (e.g., robots) or other devices such as medical devices utilize a safety processor (SP) designed for integration into safety applications and computational components that are not necessarily safety-rated. The SP monitors performance of the non-safety computational components, including latency checks and verification of identical outputs. One or more sensors send data to the non-safety computational components for sophisticated processing and analysis that the SP cannot not perform, but the results of this processing are sent to the SP, which then generates safety-rated signals to the machinery or device being controlled by the SP. As a result, the system may qualify for a safety rating despite the ability to perform complex operations beyond the scope of safety-rated components.

Systems and methods for determining safe and unsafe zones in a workspace—where safe actions are calculated in real time based on all relevant objects (e.g., some observed by sensors and others computationally generated based on analysis of the sensed workspace) and on the current state of the machinery (e.g., a robot) in the workspace—may utilize a variety of workspace-monitoring approaches as well as dynamic modeling of the robot geometry. The future trajectory of the robot(s) and/or the human(s) may be forecast using, e.g., a model of human movement and other forms of control. Modeling and forecasting of the robot may, in some embodiments, make use of data provided by the robot controller that may or may not include safety guarantees.

As a preferred embodiment of the present invention, a device for controlling a collaborative robot includes a collision detection unit configured to sense a collision of the collaborative robot; a control unit configured to control an operation mode of the collaborative robot when the collision detection unit senses the collision; and a collision countermeasure unit configured to apply, when the collision detection unit senses the collision, a collision compensation value to each of a plurality of joints in the collaborative robot so as to change a proceeding direction of a force applied to the each of the plurality of joints.

A robot teaching system includes a teaching unit and a robot including a robotic arm and a robot controller. In the robot teaching system, a workpiece includes an internal space having an opening, and a target object of a work by the end effector exists in the internal space. The robot controller determines a possibility that the arm part interferes with an edge of the opening while the robotic arm is jogging or inching.

A technique for analyzing Quality of Experience, QoE, of a delay critical robotic application in a cloud robotics system is disclosed, the robotic application involving use of a cloud based service by a robot, the service being provided to the robot from a cloud using a connection over a mobile communication network. A method implementation of the technique comprises triggering (S202) determining whether a QoE measure associated with the robotic application exceeds a threshold to assess whether QoE degradation is present, and, when it is determined that QoE degradation is present, triggering (S204) identifying a root cause for the QoE degradation based on one or more performance indicators observed within the mobile communication network and associated with the connection.

A system and method for determining performance of a robot. In one form the robot is constructed as you assembling automotive workpieces onto an automobile assembly. In one form the robot accomplishes the task of assembling an automotive workpiece onto the automotive assembly by using vision feedback and force feedback. The vision feedback can use any number of features perform its function. Such features can include an artificial feature such as but not limited to a QR code, as well as a natural feature such as a portion of the workpiece or automotive assembly. In one embodiment the robot is capable of detecting a collision event and assessing the severity of the collision event. In another embodiment the robot is capable of evaluating its performance by attracting a performance metric against a performance threshold, and comparing a sensor fusion output with a sensor fusion output reference.