通过仿真模型评估下肢外骨骼连接设计的透明性,减少对用户的干扰力。
Transparency evaluation for the Kinematic Design of the Harnesses through Human-Exoskeleton Interaction Modeling
- 构建人-外骨骼交互动力学模型,优化接口阻抗参数与运动轨迹匹配度。
- 仿真结果与实验测量的接触力一致,验证了方法的有效性。
- 适合外骨骼设计者快速评估不同连接方案的性能。
下肢外骨骼(LLEs)是为用户提供机械助力的可穿戴机器人。人-外骨骼(HE)连接必须在交互中保持用户自然行为,避免产生不必要的作用力。因此,许多研究聚焦于最小化这些力。由于反复原型制作和实验测试成本高,建立外骨骼及其与使用者物理交互的模型成为评估设计效果的有价值方法。本文提出一种新方法,利用灵活的仿真工具比较不同外骨骼配置。该方法模拟设备动力学及与穿戴者的交互,评估多种连接机制设计,以及LLE的运动学与驱动特性。评估基于优化过程,以最小化交互力矩为目标,将接口阻抗参数作为优化变量,并确保外骨骼关节变量轨迹与穿戴者关节运动相似。通过穿戴式步行器LLE在不同配置下的探索性测试,测量交互力,实验数据与优化结果对比显示,所提方法能准确估计接触力,与文献已有结果一致。
原文摘要 · Abstract (English)
Lower Limb Exoskeletons (LLEs) are wearable robots that provide mechanical power to the user. Human-exoskeleton (HE) connections must preserve the user's natural behavior during the interaction, avoiding undesired forces. Therefore, numerous works focus on their minimization. Given the inherent complications of repeatedly prototyping and experimentally testing a device, modeling the exoskeleton and its physical interaction with the user emerges as a valuable approach for assessing the design effects. This paper proposes a novel method to compare different exoskeleton configurations with a flexible simulation tool. This approach contemplates simulating the dynamics of the device, including its interaction with the wearer, to evaluate multiple connection mechanism designs along with the kinematics and actuation of the LLE. This evaluation is based on the minimization of the interaction wrenches through an optimization process that includes the impedance parameters at the interfaces as optimization variables and the similarity of the LLE's joint variables trajectories with the motion of the wearer's articulations. Exploratory tests are conducted using the Wearable Walker LLE in different configurations and measuring the interaction forces. Experimental data are then compared to the optimization outcomes, proving that the proposed method provides contact wrench estimations consistent with the collected measurements and previous outcomes from the literature. Copyright 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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