无需夹具，用双机械臂和视觉实现任意姿态电池包的自动拆解。
Vision-Guided Dual-Arm Humanoid Robotic Disassembly of End-of-Life 18650 Lithium-ion Battery Packs
- 通过视觉引导+动态校正，解决未知初始姿态下的拆解难题。
- 成功拆解21个18650电芯，定位误差仅2.4毫米，平均耗时6分钟。
- 仅用通用夹爪与摄像头，适合工业级废旧电池回收场景。
电动汽车和便携设备退役的锂离子电池数量激增，亟需安全、灵活且可精准拆解至单个电芯的自动化方案。现有机器人系统多依赖已知电池包姿态、外部夹具或专用工具，难以应对无固定夹持条件下的电芯级拆解。本文提出一种基于视觉引导的双机械臂拆解流程，仅使用通用平行爪夹持器、RGB-D传感器和预训练抓取检测器，即可从任意初始姿态拆解21个电芯的18650电池包。通过学习-过滤感知模块结合离散式“看-动”腕部相机校正，有效缓解位姿不确定性；任务中两臂间动态支撑转移扩展了有效工作空间，无需外部夹具。该流程实现8/10的端到端成功率，电芯定位均方根误差为2.4毫米，平均周期时间6.0分钟，为工业电池回收提供了一种实用的免夹具拆解基础方案。
原文摘要 · Abstract (English)
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.
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