Extended Kalman filter based sensor fusion for operational space control of a robot arm

Abstract

World modeling for achieving operational space motion control of robot arms requires accurate measurements of positions and velocities in both joint and operational space. Servomotors used for joint actuation are normally equipped with position sensors and optionally with velocity sensors for interlink motion measurements. Further improvements in measurement accuracy can be obtained by equipping the robot arm with accelerometers for absolute acceleration measurement. In this paper an Extended Kalman Filter is used for multi-sensor fusion. The real-time control algorithm was previously based on the assumption of a jerk represented as a processed white noise with the zero mean. In reality, the accelerations are varying in time during the arm motion and the zero mean assumption is not valid, particularly during fast accelerating periods. In this paper, a model predictive control approach is used for predetermining next-time-step jerk such that the remaining term can be modeled as Gaussian white noise. Experimental results illustrate the effectiveness of the proposed sensor fusion approach.