用智能机器人平台实现电动车电池高效自动拆解
Robotic Agentic Platform for Intelligent Electric Vehicle Disassembly
- 集成视觉与机械臂，实现对电池包螺栓的感知抓取
- 97%成功率完成一键拆卸，比传统方法快24分钟
- 支持大模型指令直接控制机器人，适合自动化研究
电动汽车电池回收亟需规模化拆解技术，但因设计差异大，目前仍以人工为主。我们提出面向智能拆解的机器人代理平台（RAPID），集成龙门式机械臂、RGB-D视觉与自动拧螺丝工具，可对全尺寸电池包进行作业。采用开放词汇目标检测，实现0.9757的mAP50，准确识别螺丝、螺母、母排等部件。在204次实验中评估三种单次快速拆卸策略：教示姿态法成功率97%（耗时24分钟），视觉执行法57%（29分钟），视觉伺服法83%（36分钟）。引入基于代理的大模型任务规范，使LLM通过结构化接口和ROS服务将指令转化为动作。在边缘设备上测试SmolAgents（GPT-4o-mini与Qwen 3.5 9B/4B），基于工具接口实现100%任务完成，而自动ROS服务发现失败率达43.3%，凸显结构化机器人API的重要性。该开源平台为研究人机协作与自主拆解流程提供实用基础。
原文摘要 · Abstract (English)
Electric vehicles (EV) create an urgent need for scalable battery recycling, yet disassembly of EV battery packs remains largely manual due to high design variability. We present our Robotic Agentic Platform for Intelligent Disassembly (RAPID), designed to investigate perception-driven manipulation, flexible automation, and AI-assisted robot programming in realistic recycling scenarios. The system integrates a gantry-mounted industrial manipulator, RGB-D perception, and an automated nut-running tool for fastener removal on a full-scale EV battery pack. An open-vocabulary object detection pipeline achieves 0.9757 mAP50, enabling reliable identification of screws, nuts, busbars, and other components. We experimentally evaluate (n=204) three one-shot fastener removal strategies: taught-in poses (97% success rate, 24 min duration), one-shot vision execution (57%, 29 min), and visual servoing (83%, 36 min), comparing success rate and disassembly time for the battery's top cover fasteners. To support flexible interaction, we introduce agentic AI specifications for robotic disassembly tasks, allowing LLM agents to translate high-level instructions into robot actions through structured tool interfaces and ROS services. We evaluate SmolAgents with GPT-4o-mini and Qwen 3.5 9B/4B on edge hardware. Tool-based interfaces achieve 100% task completion, while automatic ROS service discovery shows 43.3% failure rates, highlighting the need for structured robot APIs for reliable LLM-driven control. This open-source platform enables systematic investigation of human-robot collaboration, agentic robot programming, and increasingly autonomous disassembly workflows, providing a practical foundation for research toward scalable robotic battery recycling.
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