Stop and start detection block (63) determines whether or not a joint portion is in a stopped state before a rotation direction of the joint portion is inverted based on stop flag signal (Stop_Flg). When it is determined that the joint portion is in the stopped state, filter processing block (65) changes a frequency component of a correction amount for correcting backlash to a low frequency lower than a predetermined threshold value.

When workpiece (W) is brought into a non-gripping state after deflection compensation of robot arm (10) is performed in a gripping state of workpiece (W), the deflection compensation of robot arm (10) is performed in a non-gripping state of workpiece (W). Here, the deflection compensation of robot arm (10) in the non-gripping state of workpiece (W) is performed while a compensation amount is changed to gradually decrease, while hand (18) is moved from a first teaching point to a second teaching point.

The present disclosure generally relates to the field of robotics and computer animation, more particularly, method and apparatus to solve the inverse kinematics problem to control a kinematic chain such as a robot arm or an animation character's skeleton to reach a target position. The new method simulates a kinematic chain whose links and joints are elastic and can be distorted. The method distorts the kinematic chain to move its end to the target position, calculates distortions, and iteratively adjusts link and joint configurations of the kinematic chain to reduce distortions while keeping its end at the target position until a solution with near zero distortions is found. The resulting link and joint configurations of the simulated kinematic chain then can be used for the actual kinematic chain to reach the same target position.

A method, system and computer program product for training a control input system involve taking an integral of an output value from a Motion Decision Neural Network for one or more movable joints to generate an integrated output value and comparing the integrated output value to a backlash threshold. A subsequent output value is generated using a machine learning algorithm that includes a sensor value and a previous joint position if the integrated output value does not at least meet the threshold. A position of the one or more movable joints is simulated based on an integral of the subsequent output value; and the Motion Decision Neural Network is trained with the machine learning algorithm based upon at least a result of the simulation of the position of the one or more movable joints.

Disclosed is an electronic device including a robot arm configured to include at least one coupling portion configured to be coupled to a force sensor to which a specified object is attached, at least one actuator configured to drive the robot arm such that a position of the at least one coupling portion is changed, and a processor electrically connected to the actuator, wherein the processor is configured to: receive a first measurement value of the force sensor due to a weight of the specified object with respect to a first position of the at least one coupling portion, receive a second measurement value of the force sensor due to the weight of the specified object with respect to a second position of the at least one coupling portion, receive a third measurement value of the force sensor due to the weight of the specified object with respect to a third position of the at least one coupling portion, and estimate a relationship between a first coordinate system relative to the at least one coupling portion and a second coordinate system relative to the force sensor based at least on at least the first measurement value, the second measurement value, and the third measurement value to calculate a magnitude of an external force acting on the specified object.

In one embodiment, a method includes accessing a target value for a gear system, where the target value includes a target backlash or a target preload, and where the gear system includes a driving gear, a driven gear, a preloading actuator coupled to the driving gear, and a preloading actuator controller, determining a measured value for the gear system, where the measured value includes a measured backlash or a measured preload, determining that an error value between the measured value and the target value exceeds a threshold error, and sending, by the preloading actuator controller, instructions to the preloading actuator to adjust the driving gear in response to determining the error value between the measured value and the target value exceeds the threshold error.

A system comprises a robotic manipulator for control of motion of a medical tool. The robotic manipulator including a joint and a link connected to the joint. The link is configured to connect to the medical tool. A processing unit of the system is configured to receive first data from an encoder of the joint. A first tool tip estimate of a first parameter of a tool tip coupled at a distal end of the medical tool is generated using the first data. The first parameter of the tool tip is a position or a velocity of the tool tip. Second data is received from a sensor system located at a sensor portion of the link or the medical tool. The joint is controlled based on a first difference between the first tool tip estimate and a second tool tip estimate generated using the first and second data.

In an angular velocity calculation block, an angular velocity component is calculated based on a position command for a joint portion. In a kinetic calculation block, a kinetic torque is calculated based on the position command for the joint portion. In a command velocity component reversal detection block, a reversal timing is calculated based on the angular velocity component. In a reverse torque detection block, a reverse torque is calculated based on the kinetic torque and the reversal timing. In a backlash correction amount calculation block, the correction amount of the joint portion is calculated based on the kinetic torque, the reverse torque, and the reversal timing.

A computing system and method are presented. The computing system may store sensor data which includes: (i) a set of movement data, and (ii) a set of actuation data. The computing system may divide the sensor data into training data and test data by: (i) selecting, as the training data, movement training data and corresponding actuation training data, and (ii) selecting, as the test data, movement test data and corresponding actuation test data. The computing system may determine, based on the movement training data and the actuation training data, at least one of: (i) a friction parameter estimate or (ii) a center of mass (CoM) estimate, and may determine actuation prediction data based on the movement test data and based on the at least one of the friction parameter estimate or the CoM estimate. The computing system may further determine residual data, and determine a value for an error parameter.

A method for characterising the performance of a joint in a surgical robotic arm, the joint being driven by a drivetrain which transfers power from a drive source to the joint, the method comprising: sending a first command signal to position the robot arm into an initial configuration; sending a second command signal to apply a force to the joint to displace the joint from a steady state; for a plurality of predefined time intervals: receiving a first measurement indicating the configuration of the drive source at a first end of the drivetrain; receiving a second measurement indicating the configuration of the joint at a second end of the drivetrain; calculating a value of elongation using the first and second measurements; and receiving a third measurement indicating the torque experienced by the joint at the second end of the drivetrain; comparing the values of elongation with corresponding values of torque at each of the predefined time intervals; and generating an output from the comparison indicating the performance of the joint.