发表机构
University of Utah（犹他大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出首个完整工作流，分两阶段辨识大型真实世界对象（重型门、手推车）的导纳模型，无需关节扭矩传感器，模型物理一致，手柄与主体估计误差满足要求，提升力反馈接口保真度。
AI 中文摘要
导纳型模型的仿真需要物理一致性的动态模型，而这类模型很少能从现成的日常物体中获取，这限制了依赖此类仿真的力反馈接口的保真度。本文提出了首个完整工作流，用于生成带有各种约束和机构的大型真实世界对象的物理一致性模型，保证了惯性和摩擦参数的物理一致性。该工作流将每个对象分离，分两个阶段辨识手柄和主体，无需在铰链、轴或其他约束关节处安装扭矩传感器。针对重型近距驱动门和手推车生成了模型，分别代表不同约束类型和模型复杂度的对象。门采用四杆连杆运动学模型和基于流体动力学的集总参数模型，包含开启、回位、摆动和锁闭区域；手推车建模为带有球形轮的刚体，滚动时无滑动。两个对象的手柄估计均方根误差分别低于0.64 N和0.042 Nm；门主体估计均方根误差为2.19 Nm，手推车主体估计均方根误差为6.77 Nm。
英文摘要
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.
Comments8 pages, 7 figures,1 table
