Sensing Planning of Calibration Measurements for Intelligent Robots

Abstract

1.1 Overview of the problem Improvement of the accuracy and performance of robot systems implies both external sensors and intelligence in the robot controller. Sensors enable a robot to observe its environment and, using its intelligence, a robot can process the observed data and make decisions and changes to control its movements and other operations. The term intelligent robotics, or sensor-based robotics, is used for an approach of this kind. Such a robot system includes a manipulator (arm), a controller, internal and external sensors and software for controlling the whole system. The principal motions of the robot are controlled using a closed loop control system. For this to be successful, the bandwidth of the internal sensors has to be much greater than that of the actuators of the joints. Usually the external sensors are still much less accurate than the internal sensors of the robot. The types of sensors that the robot uses for observing its environment include vision, laser-range, ultrasonic or touch sensors. The availability, resolution and quality of data varies between different sensors, and it is important when designing a robot system to consider what its requirements will be. The combining of information from several measurements or sensors is called sensor fusion. Industrial robots have high repeatable accuracy but they suffer from high absolute accuracy (Mooring et. al. 1991). To improve absolute accuracy, the kinematic parameters, typically Denavit – Hartenberg (DH) parameters or related variants can be calibrated more efficiently, or the robot can be equipped with external sensors to observe the robot’s environment and provide feedback information to correct robot motions. With improved kinematic calibration the robot’s global absolute accuracy is improved. While using external sensors the local absolute accuracy is brought to the accuracy level of the external sensors. The latter can also be called workcell calibration. For kinematic calibration several methods have been developed to fulfil the requirements of several applications. There are two main approaches for calibration (Gatla et. al. 2007): open loop and closed loop. Open loop methods use special equipment such as coordinate measuring machines or laser sensors to measure position and orientation of the robot end-effector. These methods are relatively expensive and timeconsuming. The best accuracy will be achieved when using these machines as Visual Servoing tools where they guide the end-effector of the robot on-line (Blank et. al. 2007). Closed loop methods use robot joint measurements and end-effector state to form closed loop equations for calculating the calibration parameters. The state of the end effector can be