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.

翻译：电子废弃物快速增长而回收率仍然较低。我们提出一种基于电子设备图的自适应规划(eGRAP)方法，该方法集成视觉感知、动态规划与双臂执行以实现自主拆解。配备相机的机械臂识别部件并估计其位姿，有向图编码了部件必须被优先移除的顺序。调度器利用该图的拓扑排序选择有效的后续步骤并将其分配给两个机器人手臂，允许独立任务并行执行。一个手臂携带螺丝刀（配备手眼深度相机），另一个手臂则夹持或操作组件。我们在3.5英寸硬盘驱动器上验证了eGRAP系统：随着部件被拧松和移除，系统在线更新其图结构与规划方案。实验表明系统能稳定完成每个硬盘的完整拆解，具有高成功率和高效作业周期，证明了该方法实时自适应协调双臂任务的能力。