Integrating a robotic system into the depalletizing process of a supermarket demands a high level of autonomy, based on strong perceptive capabilities. This letter presents a system for detection, recognition, and localization of heterogeneous cases in a depalletizing robotic cell, using a single RGB-D camera. Such a system integrates apriori information on the content of the pallet with data from the RGB-D camera, exploiting a sequence of 2D and 3D model-based computer-vision algorithms. The effectiveness of the proposed methodology is assessed in an experiment where multiple cases and pallet configurations are considered. Finally, a complete depalletizing process is shown.

Automatic depalletizing is becoming a practice widely applied in warehouses to automatize and speed-up logistics. On the other hand, the necessity to adapt the preexisting logistic lines to a custom automatic system can be a limit for the application of robotic solutions into smaller facilities like supermarkets. In this work, we tackle this issue by proposing a flexible and adaptive gripper for robotic depalletizing. The gripper is designed to be assembled on the end-tip of an industrial robotic arm. A novel patent-pending mechanism allows grasping boxes and products from both the upper and the lateral side enabling the depalletizing of boxes with complex shape. Moreover, the gripper is reconfigurable with five actuated degrees of freedom, that are automatically controlled using the embedded sensors to adapt grasping to different shapes and weights.

Robust manipulation of mechanical parts in different grasping configurations is a challenging problem in autonomous robotic assembly that can be overcome by adopting suitable mechatronic solutions. This article proposes a tactile-sensor-based approach that exploits in-hand pose estimation and contact perception to compensate for unavoidable picking, placing, and insertion errors that may occur during task assembly execution under uncertain/perturbed conditions. The main objective of this work is to demonstrate how the use of tactile data, together with both machine learning and model-based methods, allows us to obtain an advanced system able to successfully complete a task that requires the manipulation of boltlike fasteners with different shapes and grasped in different poses. Experiments carried out using the proposed robotic system are reported for a specific assembly task in order to evaluate the effectiveness of the proposed solution. By means of suitable calibration procedures exploiting the same methods proposed here, the system can be easily adapted to different objects and shapes.