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.

Within assembly lines, wheel assembly to continuously conveyed car bodies is still executed by human workers using a device that compensates the weight of the wheel. This paper presents a solution in which a robot autonomously assembles and fixes the wheels. The approach uses a sensor-driven control strategy that compensates a possible temporal or spatial offset. Three types of sensors are proposed for adequate perception of the wheel hub. Their signals are fused by a Kalman filter that allows predictions in the time domain. Finally, a feed-forward controller is used, that is designed to consider the predictions in order to minimize dynamical delays. The control is driven by a special task description that extents usual robot programming methods.