Collision-Free Trajectory Planning with Digital Twin Support for Robotic Bin Picking from Unstructured Bins

Abstract

Bin-picking tasks involve multiple steps, including object detection, 3D pose estimation, and trajectory planning. Handling unstructured and cluttered bins is challenging due to potential overlaps and part misidentification. We introduce a novel approach for achieving highly accurate 3D pose estimation at the millimeter level. Our method captures a point-cloud image of the bin parts and smooths it in a post processing step. We then align a CAD model of the desired part with the smoothed point-cloud surface. Finally, we identify gripping points to facilitate collision-free trajectory planning with compromising K-d-Tree (k dimensional-Tree) and RRT (Rapidly-exploring Random Trees) method. After the digital twin tool verifies that the planned trajectory is collision-free, the robotic arm performs the assembly task. This concept is validated through a use case involving Lego assembly and is compared with an industrial tool for performance evaluation.