多机器人协作用视觉触觉感知，实现柔性线状物自动装配
Multi-Robot Assembly of Deformable Linear Objects Using Multi-Modal Perception
- 以物体为中心融合视觉与触觉信息，实时追踪线状物形状与接触状态
- 完成从杂乱堆叠中抓取到多机协同安装的全流程装配任务
- 适用于工业场景下的柔性电缆等线状物自动化装配
工业中柔性线状物（如电缆）的自动化装配具有广泛应用潜力，但其变形特性带来行为预测难题。现有研究多聚焦于形状追踪、抓取或形状控制等单一问题，缺乏集成化流程。为此，本文提出一种面向物体的感知与规划框架，贯穿工业全链条实现柔性线状物的完整装配。该框架利用视觉与触觉信息，在不同阶段同步追踪线状物形状及接触状态，支持机器人动作的有效规划。方法包括：机器人从杂乱环境中抓取线状物，随后由两台额外机器人协同完成装配至指定工装。真实实验在多机器人系统上验证了该方法的有效性与工业适用性。
原文摘要 · Abstract (English)
Industrial assembly of deformable linear objects (DLOs) such as cables offers great potential for many industries. However, DLOs pose several challenges for robot-based automation due to the inherent complexity of deformation and, consequentially, the difficulties in anticipating the behavior of DLOs in dynamic situations. Although existing studies have addressed isolated subproblems like shape tracking, grasping, and shape control, there has been limited exploration of integrated workflows that combine these individual processes. To address this gap, we propose an object-centric perception and planning framework to achieve a comprehensive DLO assembly process throughout the industrial value chain. The framework utilizes visual and tactile information to track the DLO's shape as well as contact state across different stages, which facilitates effective planning of robot actions. Our approach encompasses robot-based bin picking of DLOs from cluttered environments, followed by a coordinated handover to two additional robots that mount the DLOs onto designated fixtures. Real-world experiments employing a setup with multiple robots demonstrate the effectiveness of the approach and its relevance to industrial scenarios.
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。