智能电动汽车拆解机器人平台
Robotic Agentic Platform for Intelligent Electric Vehicle Disassembly
AI总结：
本文提出RAPID平台，通过感知驱动操作、灵活自动化和AI辅助机器人编程，解决电动汽车电池包拆解的自动化问题，实验评估了三种快拆策略并引入代理AI规范以提升人机协作效率。
摘要：
电动汽车（EV）创造了一种迫切需要可扩展的电池回收需求，但电池包拆解仍主要依赖人工，由于设计多样性高。我们提出了智能电动汽车拆解机器人平台（RAPID），旨在研究感知驱动操作、灵活自动化和AI辅助机器人编程在真实回收场景中的应用。系统集成了悬臂安装的工业机械臂、RGB-D感知和自动化螺栓运行工具，用于全尺寸电动汽车电池包的快拆。开放词汇目标检测管道实现了0.9757 mAP50，能够可靠识别螺丝、螺栓、母线等组件。我们实验评估（n=204）三种一次性快拆策略：教定姿态（97%成功率，24分钟）、一次性视觉执行（57%，29分钟）和视觉伺服（83%，36分钟），比较电池顶盖快拆的成功率和拆解时间。为支持灵活交互，我们引入代理AI规范用于机器人拆解任务，允许LLM代理通过结构化工具接口和ROS服务将高级指令转化为机器人动作。我们评估了SmolAgents在边缘硬件上的表现，工具接口实现100%任务完成，而自动ROS服务发现显示43.3%的失败率，突显了结构化机器人API在可靠LLM驱动控制中的重要性。该开源平台使系统研究人机协作、代理机器人编程和日益自主的拆解流程成为可能，为研究可扩展的机器人电池回收提供了实用基础。
英文摘要：
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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