Estimation of Optimal Gripper Configuration Through an Embedded Array of Proximity Sensors

The task of picking up and handling objects is a great robotic challenge. Estimating the best point where the gripper fingers should come into contact with the object before performing the pick-up task is essential to avoid failures. This study presents a new approach to estimating the grasping pose of objects using a database generated by a gripper through its proximity sensors. The grasping pose estimation simulates the points where the fingers should be positioned to obtain the best grasp of the object. In this study, we used a database generated by a reconfigurable gripper with three fingers that can scan different objects through distance sensors attached to the fingers and palm of the gripper. The grasping pose of 13 objects was estimated, which were classified according to their geometries. The analysis of the grasping pose estimates considered the versatility of the gripper used. These object grasping pose estimates were validated using the CoppeliaSim software, where it was possible to configure the gripper according to the estimates generated and pick up the objects using just two or three fingers of the reconfigurable gripper.