Novel Sweeping Methods for Robotic Rearrangement of Object Piles

In this paper, we investigate robotic pile rearrangement algorithms to redistribute objects in cluttered scenes for improving the performance of object recognition and picking systems. In particular, we focus on identifying the best pile sweeping actions in fast-paced industrial sorting applications where the robot would only have limited time to react, i.e., has one chance to sweep. The robot’s sweep aims to minimize occlusions and improve visibility of the objects in the scene. We first study the performance of three methods that utilize dimensionality reduction techniques (i.e., clustering and PCA) as baselines, discussing their strengths and weaknesses. We then present a novel sampling-based approach for determining the best start and end points for the sweeping motions based on an action evaluation metric. Additionally, we adapted the best performing method to prioritize minimizing occlusion for specific objects. Our results show that the sampling-based method performs more consistently and successfully across different initial pile configurations.