Loading of hanging trolleys on overhead conveyor with industrial robots

Handling moving objects with robot manipulators is a challenging task as it involves tracking of objects with high accuracy. An industrial application of this type is the loading and unloading of objects on an overhead conveyor. A robotic solution to this problem is presented in this paper, where we describe a method for the interaction of an industrial robot and a free swinging object. Our approach is based on visual tracking using particle filtering where the equations of motion of the object are included in the filtering algorithm. The first contribution of this paper is that the Fisher information matrix is used to quantify the information content from each image feature. In particular, the Fisher information matrix is used to construct a weighted likelihood function. This improves the robustness of tracking algorithm significantly compared to the standard approach based on an unweighted likelihood function. The second contribution of this paper is that we detect occluded image features, and avoid the use of these features in the calculation of the likelihood function. This further improves the quality of the likelihood function. We demonstrate the improved performance of the proposed method in experiments involving the automatic loading of trolleys hanging from a moving overhead conveyor.