Manipulation of Boltlike Fasteners Through Fingertip Tactile Perception in Robotic Assembly

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.