Sensor data fusion using perception net for a precise assembly task

Abstract

A sensor fusion method is presented for a peg-in-hole insertion task. Three kinds of sensor are fused for task execution. The vision and proximity sensors are mainly used for gross motion control, and the force/torque sensor is used for fine motion control of the robot. Covariance analysis is conducted for each sensor discussed in this paper, and the perception net is introduced to improve the usefulness of each sensor by optimally weighting its output.