Original title: Robot Aware Computational Design of Object Specific Passive Grippers for Additive Manufacturing
Abdullah Yahya Abdullah Omaisan, Ibrahim Sheikh Mohamed · September 2026
Robots often struggle to grab uniquely shaped items without using motors or complex moving parts. Researchers built an automated system that designs custom, unpowered grippers made for 3D printing. The user inputs a 3D model of an object, its scanned position, and a chosen six-axis robot arm. The software checks the object's shape, finds contact points, and picks one of six passive latching styles. It plans the robot's movement path, tests whether the plastic will break under stress, and carves away excess material while protecting key functional areas. Every exported design links directly to its original input data with a traceable code. When tested on four items—a rabbit, camera flange, 3DBenchy boat, and faceted bust—all met the baseline stress, movement, and fit checks. In tougher alignment tests, 29-134 of 160 trials held up. Physical prints succeeded, though they remain digital screening prototypes.
Imagine packing a fragile glass ornament in a suitcase with other items. You need to arrange everything so the ornament doesn't break, but each item has a different shape, and the suitcase has limited space. In this paper, the system is like packing the gripper design around the object, considering the robot's ability to move and the forces it must withstand, to create a perfect fit for 3D printing.
For people building robots that handle specific objects, this automates gripper design, saving time and improving grip. It could speed up prototyping in manufacturing or lab automation.
The designs are validated only through digital simulations and not with physical tests under real operating conditions. The method relies on nominal material properties without calibration for specific 3D-printing processes or materials.
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Robot Aware Computational Design of Object Specific Passive Grippers for Additive Manufacturing — Abdullah Yahya Abdullah Omaisan, Ibrahim Sheikh Mohamed
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.