Graph-Based Adaptive Planning for Coordinated Dual-Arm Robotic Disassembly of Electronic Devices (eGRAP)

TL;DR

This paper introduces eGRAP, a graph-based adaptive planning system that enables autonomous, coordinated dual-arm robotic disassembly of electronic devices, improving efficiency and success rates in e-waste recycling.

Contribution

eGRAP integrates vision, dynamic planning, and dual-arm coordination using a graph-based approach for autonomous electronic device disassembly.

Findings

Achieved high success rates in full HDD disassembly

Demonstrated real-time adaptive coordination of dual-arm robots

Improved cycle times and efficiency in disassembly tasks

Abstract

E-waste is growing rapidly while recycling rates remain low. We propose an electronic-device Graph-based Adaptive Planning (eGRAP) that integrates vision, dynamic planning, and dual-arm execution for autonomous disassembly. A camera-equipped arm identifies parts and estimates their poses, and a directed graph encodes which parts must be removed first. A scheduler uses topological ordering of this graph to select valid next steps and assign them to two robot arms, allowing independent tasks to run in parallel. One arm carries a screwdriver (with an eye-in-hand depth camera) and the other holds or handles components. We demonstrate eGRAP on 3.5in hard drives: as parts are unscrewed and removed, the system updates its graph and plan online. Experiments show consistent full disassembly of each HDD, with high success rates and efficient cycle times, illustrating the method's ability to adaptively coordinate dual-arm tasks in real time.