用图结构动态规划双臂拆解电子设备，实时自适应调整任务顺序。
Graph-Based Adaptive Planning for Coordinated Dual-Arm Robotic Disassembly of Electronic Devices (eGRAP)
- 构建零件依赖图，按拓扑序生成可并行执行的拆解步骤
- 在3.5英寸硬盘上实现100%完整拆解，成功率高且耗时高效
- 适合需要高鲁棒性自动拆解的电子废弃物回收场景
电子垃圾增长迅速但回收率仍低。本文提出电子设备图结构自适应规划（eGRAP），融合视觉感知、动态规划与双臂协同执行，实现自主拆解。配备摄像头的机械臂识别部件并估计位姿，有向图编码部件间的拆除依赖关系。调度器基于该图的拓扑排序选择有效下一步，并分配给两台机械臂，支持独立任务并行。一台臂持螺丝刀（带眼在手深度相机），另一台负责抓取或支撑部件。系统在3.5英寸硬盘上实测：随着螺钉被拧下和部件移除，实时更新图与计划。实验表明，每台硬盘均能一致完成全拆解，成功率高，周期短，验证了方法在实时自适应协调双臂任务中的能力。
原文摘要 · Abstract (English)
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.
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