发表机构
Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种融合IMU与主动磁场的嵌入式姿态传感框架，实现连续体机器人无外部相机的实时三维姿态估计（16.7 Hz），并通过闭环控制实验验证其有效性。
AI 中文摘要
连续体机器人因其固有的柔顺性和适应复杂环境的能力，非常适合进行轻柔操作。然而，其连续可变形结构使得精确的构型估计具有挑战性，尤其是在外部视觉系统不可用或被遮挡的情况下。在这项工作中，我们提出了一种嵌入式姿态传感框架，该框架结合惯性测量单元（IMU）和主动磁场来估计机器人构型，无需依赖外部相机。来自IMU的角测量和磁场参考被融合，以改善局部姿态估计并减少操作过程中累积的姿态误差。该姿态传感方案实现了16.7 Hz的更新率，允许实时反馈。所提出的系统通过闭环控制进行了实验验证，其中估计的机器人构型用于在与物体交互时将末端执行器保持在期望位置。这些结果展示了分布式磁-惯性传感在连续体机器人实时姿态估计和闭环控制方面的潜力。
英文摘要
Continuum robots are well suited for gentle manipulation because of their inherent compliance and ability to adapt to complex environments. However, their continuously deformable structure makes accurate configuration estimation challenging, particularly when external vision systems are unavailable or obstructed. In this work, we present an embedded pose sensing framework that combines inertial measurement units (IMUs) and active magnetic fields to estimate the robot configuration without relying on external cameras. The angular measurements from the IMU and magnetic-field references are fused to improve local orientation estimation and reduce accumulated orientation error during operation. This pose sensing scheme achieves an update rate of 16.7~Hz, allowing real-time feedback. The proposed system is experimentally validated through closed-loop control, where the estimated robot configuration is used to maintain the end-effector at a desired position while interacting with an object. These results demonstrate the potential of distributed magnetic--inertial sensing for real-time pose estimation and closed-loop control of continuum robots.
