Action Planning for Packing Long Linear Elastic Objects into Compact Boxes with Bimanual Robotic Manipulation
This addresses a specific robotic manipulation challenge for industrial or logistics applications, representing an incremental advance in packing automation.
The paper tackles the problem of automatically packing long linear elastic objects into boxes using bimanual robotic manipulation, developing a hybrid geometric model and action planner that successfully accomplished packing tasks in experiments with various objects and boxes.
In this paper, we propose a new action planning approach to automatically pack long linear elastic objects into common-size boxes with a bimanual robotic system. For that, we developed a hybrid geometric model to handle large-scale occlusions combining an online vision-based method and an offline reference template. Then, a reference point generator is introduced to automatically plan the reference poses for the predesigned action primitives. Finally, an action planner integrates these components enabling the execution of high-level behaviors and the accomplishment of packing manipulation tasks. To validate the proposed approach, we conducted a detailed experimental study with multiple types and lengths of objects and packing boxes.