仅用单目图像和驱动信号实时估算软机器人三维接触力
A three-dimensional force estimation method for the cable-driven soft robot based on monocular images
- 直接输入单目图像与多维驱动信息，免去3D重建预处理
- 均方相对误差仅0.84%，优于已有方法的2.2%
- 适合需高安全交互的医疗或助老场景
软机械臂在需高安全性的交互任务中表现优异，如机器人辅助手术、老人照护等。然而实时接触反馈的挑战制约了其在精确操作中的应用。本文提出一种端到端网络，用于估算软机器人的三维接触力，以提升其交互能力。该方法直接使用单目图像与多维驱动信息作为网络输入，相比依赖3D形状信息的研究，简化了原始数据预处理，有效降低了配置重建误差。设计统一特征表示模块，将系统驱动信号的低维特征提升至与图像特征同尺度，促进多模态信息融合。该方法在软机器人实验平台上验证，三维力估计精度良好（均方相对误差为0.84%），优于相关工作中最佳结果（2.2%）。
原文摘要 · Abstract (English)
Soft manipulators are known for their superiority in coping with high-safety-demanding interaction tasks, e.g., robot-assisted surgeries, elderly caring, etc. Yet the challenges residing in real-time contact feedback have hindered further applications in precise manipulation. This paper proposes an end-to-end network to estimate the 3D contact force of the soft robot, with the aim of enhancing its capabilities in interactive tasks. The presented method features directly utilizing monocular images fused with multidimensional actuation information as the network inputs. This approach simplifies the preprocessing of raw data compared to related studies that utilize 3D shape information for network inputs, consequently reducing configuration reconstruction errors. The unified feature representation module is devised to elevate low-dimensional features from the system's actuation signals to the same level as image features, facilitating smoother integration of multimodal information. The proposed method has been experimentally validated in the soft robot testbed, achieving satisfying accuracy in 3D force estimation (with a mean relative error of 0.84% compared to the best-reported result of 2.2% in the related works).
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