The growing volume of retired lithium-ion battery packs from electric vehicles and portable electronics calls for automated disassembly that is safe, flexible, and selective down to the individual cell. Existing robotic systems, however, mostly assume known pack poses, external fixtures, or specialised tooling, leaving fixture-free cell-level disassembly under pose uncertainty largely unsolved. This paper presents a vision-guided dual-arm pipeline that disassembles a 21-cell 18650 pack from an arbitrary initial pose using only general-purpose parallel-jaw grippers, RGB-D sensing, and a pre-trained grasp detector. Pose uncertainty is absorbed by a learn-and-filter perception stack with discrete look-and-move wrist-camera corrections, while a mid-task support transfer between the two arms extends the effective workspace without any external clamp. The pipeline achieves an 8/10 end-to-end success rate, a cell-localisation root-mean-square error of $2.4$\,mm, and a mean cycle time of 6.0\,minutes per pack, providing a practical, fixture-free building block for industrial battery recycling.

翻译：随着电动汽车和便携式电子设备产生的废旧锂离子电池组数量日益增长，亟需能够实现安全、灵活且精细到单体电池级别的自动化拆解技术。然而，现有机器人系统大多依赖已知的电池组姿态、外部夹具或专用工具，尚未解决在姿态不确定情况下无夹具的电池级拆解问题。本文提出一种视觉引导的双臂拆解流程，该流程仅使用通用平行夹爪、RGB-D传感器和预训练的抓取检测器，即可从任意初始姿态拆解一个包含21节18650电池的电池组。通过整合学习与滤波的感知栈，并辅以离散的注视-移动腕部相机校正，系统可消除姿态不确定性；同时，双臂间任务中段的支撑转移操作可在无需外部夹具的情况下扩展有效工作空间。该流程实现了8/10的端到端成功率、2.4毫米的电池定位均方根误差以及每电池组6.0分钟的平均周期时间，为工业电池回收提供了一种实用且无需夹具的基础技术方案。