无需扭矩传感器，可为大型物体建立物理一致的动力学模型。
System Identification of Admittance Models for Large Real-World Objects
- 分两阶段识别物体手柄与主体，无需关节处安装扭矩传感器。
- 门和手推车模型的误差分别低于0.64N和6.77Nm，符合物理一致性要求。
- 适用于需高保真力反馈的虚拟现实、机器人交互等场景。
阻抗型模型的仿真需要物理上一致的动力学模型，但大多数日常大件物品缺乏此类模型，限制了依赖此类仿真的触觉接口精度。本文提出首个完整的大型真实物体建模工作流程，适用于不同约束类型与复杂度的物体，确保惯性与摩擦参数的物理一致性。该流程将物体分离处理，分两阶段识别手柄与主体，无需在铰链、轴心或其他约束关节处安装扭矩传感器。对一扇重的自闭门和一辆手推车进行了建模：门采用四连杆运动学与基于流体动力学的集总参数模型，涵盖开启、回弹、摆动与锁闭区域；手推车建模为刚体，带球形轮且滚动无滑移。手柄估计的均方根误差低于0.64 N和0.042 Nm，门体估计误差为2.19 Nm，手推车体为6.77 Nm。
原文摘要 · Abstract (English)
Simulation of admittance-type models requires physically consistent dynamic models that are rarely available for off-the-shelf, everyday objects, limiting the fidelity of haptic interfaces that rely on such simulations. This paper presents the first complete workflow for producing physically consistent models of large real-world objects with various constraints and mechanisms, guaranteeing physical consistency of inertia and friction parameters. The workflow separates each object and identifies the handle and body in two stages, requiring no torque sensors at hinges, axles, or other constrained joints. Models are produced for a heavy, closer-actuated door and a wheelbarrow, representing objects of differing constraint types and model complexity. The door is modeled using four-bar linkage kinematics and a fluid dynamics-based lumped parameter model including opening, backcheck, swing, and latch zones. The wheelbarrow is modeled as a rigid body with a spherical wheel and no slip during rolling. Handle estimation RMS errors were below 0.64 N and 0.042 Nm across both objects. Door body estimation had RMS error of 2.19 Nm and wheelbarrow body estimation had RMS error of 6.77 Nm.
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。