Action Planning for Packing Long Linear Elastic Objects into Compact Boxes with Bimanual Robotic Manipulation

TL;DR

This paper introduces a novel action planning method for bimanual robots to efficiently pack long elastic objects into boxes, combining geometric modeling, vision, and reference planning, validated through extensive experiments.

Contribution

It presents a hybrid geometric model and an integrated action planner for packing elastic objects, advancing automation in robotic packing tasks.

Findings

Successful packing of various elastic objects demonstrated

High accuracy in reference pose planning achieved

Effective handling of occlusions with hybrid modeling

Abstract

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.