Grasping-Based Robotic Threaded Assembly for Nonstandard Objects Under Uncertainty

Robotic threaded assembly is crucial in automation, yet existing methods struggle with nonstandard and structurally diverse objects. To address this issue, we develop a grasping-based robotic threaded assembly system. The system is built around a custom-designed grasp-based screwing tool, and leverages point cloud input to generate grasping poses and motions. The system also allows autonomous fingertip replacement, enabling continuous assembly of various objects. This article mainly addresses two key challenges caused by uncertainty in the assembly process: Feeding and screwing. Notably, the feeding process is essentially a handover task. First, we propose an object handover method that integrates gradient-descent search with taboo search, ensuring robust transfer of the object to the tool’s grasping center, while aligning the screwing axis with the object’s threaded axis. Second, we introduce a synchronized position and orientation search strategy, followed by fine-tuning of the orientation, to enable precise and stable threaded assembly. The experimental results validate the effectiveness of the proposed methods. The system successfully achieves continuous assembly of objects with diverse shapes, demonstrating its adaptability and robustness.