Tetsuyou Watanabe

Tetsuyou Watanabe, Kota Morino, Yoshitatsu Asama et al.

This letter presents a novel small gripper capable of grasping various types of small-sized items from flat surfaces for the assembly of small devices. Using a single actuator, the proposed gripper realizes two grasping modes: parallel-grip and turn-over modes. The gripper's mode can be switched via contact with a flat surface, such as a table. Handling thin thicknesses and light weights are the key challenges faced in attempts to grasp small-sized items. Although parallel grippers are effective in handling small items, there is a limit to the thinness of objects that can be grasped by parallel grippers. Accordingly, the turn-over mode was adopted to grasp items that exceeded this threshold. In the turn-over mode, one finger lifts the item, while another finger holds the item from above to keep it from flicking out. The proposed gripper is capable of picking up several types of items from a table, including thin (0.05 mm) and lightweight (0.007 g) items.

Felix von Drigalski, Christian Schlette, Martin Rudorfer et al.

The Industrial Assembly Challenge at the World Robot Summit was held in 2018 to showcase the state-of-the-art of autonomous manufacturing systems. The challenge included various tasks, such as bin picking, kitting, and assembly of standard industrial parts into 2D and 3D assemblies. Some of the tasks were only revealed at the competition itself, representing the challenge of "level 5" automation, i. e., programming and setting up an autonomous assembly system in less than one day. We conducted a survey among the teams that participated in the challenge and investigated aspects such as team composition, development costs, system setups as well as the teams' strategies and approaches. An analysis of the survey results reveals that the competitors have been in two camps: those constructing conventional robotic work cells with off-the-shelf tools, and teams who mostly relied on custom-made end effectors and novel software approaches in combination with collaborative robots. While both camps performed reasonably well, the winning team chose a middle ground in between, combining the efficiency of established play-back programming with the autonomy gained by CAD-based object detection and force control for assembly operations.