Cartesian coordinate robot

Abstract: The authors describe a novel technique for computing position and orientation of a camera relative to the last joint of a robot manipulator in an eye-on-hand configuration. It takes only about 100+64N arithmetic operations to compute the hand/eye relationship after the robot finishes the movement, and incurs only additional 64 arithmetic operations for each additional station. The robot makes a series of automatically planned movements with a camera rigidly mounted at the gripper. At the end of each move, it takes a total of 90 ms to grab an image, extract image feature coordinates, and perform camera extrinsic calibration. After the robot finishes all the movements, it takes only a few milliseconds to do the calibration. A series of generic geometric properties or lemmas are presented, leading to the derivation of the final algorithms, which are aimed at simplicity, efficiency, and accuracy while giving ample geometric and algebraic insights. Critical factors influencing the accuracy are analyzed, and procedures for improving accuracy are introduced. Test results of both simulation and real experiments on an IBM Cartesian robot are reported and analyzed. >

Abstract: The authors describe a novel technique for computing position and orientation of a camera relative to the last joint of a robot manipulator in an eye-on-hand configuration. It takes only about 100+64N arithmetic operations to compute the hand/eye relationship after the robot finishes the movement, and incurs only additional 64 arithmetic operations for each additional station. The robot makes a series of automatically planned movements with a camera rigidly mounted at the gripper. At the end of each move, it takes a total of 90 ms to grab an image, extract image feature coordinates, and perform camera extrinsic calibration. After the robot finishes all the movements, it takes only a few milliseconds to do the calibration. A series of generic geometric properties or lemmas are presented, leading to the derivation of the final algorithms, which are aimed at simplicity, efficiency, and accuracy while giving ample geometric and algebraic insights. Critical factors influencing the accuracy are analyzed, and procedures for improving accuracy are introduced. Test results of both simulation and real experiments on an IBM Cartesian robot are reported and analyzed. >

Abstract: To use a wrist-mounted sensor (such as a camera) for a robot task, the position and orientation of the sensor with respect to the robot wrist frame must be known. The sensor mounting position can be found by moving the robot and observing the resulting motion of the sensor. This yields a homogeneous transform equation of the form AX=XB, where A is the change in the robot wrist position, B is the resulting sensor displacement, and X is the sensor position relative to the robot wrist. The solution to an equation of this form has one degree of rotational freedom and one degree of translation freedom if the angle of rotation of A is neither 0 nor pi radians. To solve for X uniquely, it is necessary to make two arm movements and form a system of two equations of the form: A/sub 1/X=XB/sub 1/ and A/sub 2/X=XB/sub 2/. A closed-form solution to this system of equations is developed and the necessary conditions for uniqueness are stated. >

Abstract: An apparatus and method for controlling a robot may scale a motion of a surgical robot based on a type of object gripped by the surgical robot. In the robot controlling method, by scaling the motion of the surgical robot based on the type of object gripped by the surgical robot, the surgical robot may automatically perform the motion on objects using an optimized force although a user does not control a force minutely based on the type of object gripped by the surgical robot.