Classification and Prediction for Accurate Sensor-Based Assembly to Moving Objects

Abstract

Typical industrial assembly tasks require an accuracy that cannot be realized by only feedback control if a minimum speed is given by a conveyor. Feed-forward has proven to be advantageous, using predictions of the desired trajectory which will be computed from sensor values. These predictions are improved by a model based classification of the sensor data to typical scenarios. In contrast to linear controllers this assures the fastest possible response to external disturbances, in spite of large dynamical delays. The method is demonstrated by assembling wheels to a car body that is moved by a conveyor, fusing sensor data using an extended Kalman filter.