Target-mass Grasping of Entangled Food using Pre-grasping & Post-grasping
This addresses automation challenges for food packing industries dealing with entangled ingredients, though it appears incremental as it builds on existing grasping techniques.
The study tackled the problem of accurately grasping target masses of entangled food pieces in packing industries by proposing methods combining pre-grasping to reduce entanglement, post-grasping to adjust mass with a novel gripper, and selecting grasping points. It showed significant improvement in grasp accuracy for user-specified target masses on various foods.
Food packing industries typically use seasonal ingredients with immense variety that factory workers manually pack. For small pieces of food picked by volume or weight that tend to get entangled, stick or clump together, it is difficult to predict how intertwined they are from a visual examination, making it a challenge to grasp the requisite target mass accurately. Workers rely on a combination of weighing scales and a sequence of complex maneuvers to separate out the food and reach the target mass. This makes automation of the process a non-trivial affair. In this study, we propose methods that combines 1) pre-grasping to reduce the degree of the entanglement, 2) post-grasping to adjust the grasped mass using a novel gripper mechanism to carefully discard excess food when the grasped amount is larger than the target mass, and 3) selecting the grasping point to grasp an amount likely to be reasonably higher than target grasping mass with confidence. We evaluate the methods on a variety of foods that entangle, stick and clump, each of which has a different size, shape, and material properties such as volumetric mass density. We show significant improvement in grasp accuracy of user-specified target masses using our proposed methods.