用触觉与柔顺控制提升机器人在不确定环境下的抓取成功率。
Grasping in Uncertain Environments: A Case Study For Industrial Robotic Recycling
- 设计三种夹爪，结合触觉反馈实现力控抓取。
- 实验室与工厂测试中抓取成功率显著提升。
- 适合工业回收场景中复杂、未知物体的抓取任务。
自主机器人在不确定环境中抓取未知物体是未来工业的重要挑战。以电子废弃物（WEEE）回收为例，设备可能损坏、脏污且无法识别，导致传统基于视觉的模型失效。本文针对这一问题，提出三种夹爪及相应的触觉控制策略，通过力控与柔顺技术增强抓取鲁棒性。针对每种夹爪，开发适配不同任务的操作策略，充分发挥其结构优势。在四种典型WEEE设备上，于实验室与工厂环境下进行实验验证，结果表明，结合触觉感知与柔顺机制可有效应对物体不确定性，显著提高抓取成功率。
原文摘要 · Abstract (English)
Autonomous robotic grasping of uncertain objects in uncertain environments is an impactful open challenge for the industries of the future. One such industry is the recycling of Waste Electrical and Electronic Equipment (WEEE) materials, in which electric devices are disassembled and readied for the recovery of raw materials. Since devices may contain hazardous materials and their disassembly involves heavy manual labor, robotic disassembly is a promising venue. However, since devices may be damaged, dirty and unidentified, robotic disassembly is challenging since object models are unavailable or cannot be relied upon. This case study explores grasping strategies for industrial robotic disassembly of WEEE devices with uncertain vision data. We propose three grippers and appropriate tactile strategies for force-based manipulation that improves grasping robustness. For each proposed gripper, we develop corresponding strategies that can perform effectively in different grasping tasks and leverage the grippers design and unique strengths. Through experiments conducted in lab and factory settings for four different WEEE devices, we demonstrate how object uncertainty may be overcome by tactile sensing and compliant techniques, significantly increasing grasping success rates.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。