MRI: Development of an Integrated Instrument for Testing Safety and Robustness of Robotic Co-Workers in Dynamic Environments

MRI：开发用于测试动态环境中机器人同事的安全性和鲁棒性的集成仪器

• 批准号: 2018905
• 负责人: Herbert Tanner
• 金额: $ 55.17万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018905&HistoricalAwards=false
• 关键词: MRI; Development; Integrated; Instrument; Testing

Merging human living spaces with robot workspaces sparks the development of novel control, estimation and perception algorithms with theoretically provable performance and safety guarantees. However, to date, rigorous experimental protocols and equipment for validating the theoretically predicted performance of the proposed algorithms does not exist. A major reason for this is the difficulty associated with reproducing—in a systematic fashion—the dynamic conditions encountered by robots operating in typical human-centric and natural environments. The ultimate goal of this project is to promote human-robot co-existence by developing a unique new fully integrated sensing and control instrument, which will enable researchers to rigorously evaluate the performance of robotic algorithms in response to systematically induced dynamic perturbations that emulate real-world conditions. This award enables the initiation of the effort to realize the envisioned instrument. In this project, a component of the instrument will be realized to support research on legged locomotion using a bipedal robot integrated into a controllable environment realized by a novel instrumented variable impedance treadmill capable of emulating a wide variety of terrain realities. The unique configuration of this component will allow researchers to evaluate robot locomotion control and planning methodologies and will be used to generate sufficiently rich and expressive datasets, which will be widely shared through the internet to assist research in training models of locomotion through machine learning algorithms. The proposed effort will pave the way toward the development of similar instruments for experimentally testing and verifying the strengths and limitations of robotic algorithms. This award will result in the development of a stand-alone component of a unique shared-use research instrument, which will make possible—for the first time—measurements that will contribute to the creation of novel experimental protocols for the rigorous assessment of robotic locomotion algorithms. This project will involve assessment of the robustness of locomotion controllers to terrain variability and uncertainty by performing synchronous measurements of the (i) motion/kinematics of a novel bipedal robot walking on a unique variable stiffness treadmill, (ii) the interaction forces between the robot’s feet and the treadmill, and (iii) the deflection of the treadmill belt when a compliant environment is simulated. Data of this kind are invaluable to studying and assessing the performance of locomotion control strategies on a wide variety of terrain realities. Perturbations like sudden changes in the stiffness of the terrain underneath a leg—including the case where a leg encounters terrain disruption in the form of step-down or step-up disturbances—can be systematically induced and their effect can be measured by the developed instrument. Such capabilities have never been available in the context of human-sized robotic co-workers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

将人类的生活空间与机器人工作区合并，这引发了新的控制，估计和感知算法的发展，并提供了理论上的证明性能和安全保证。但是，迄今为止，尚不存在严格的实验协议和用于验证所提出算法的设备。造成这种情况的一个主要原因是与在系统的方式中复制典型的以人为中心和自然环境的机器人遇到的动态条件相关的困难。该项目的最终目标是通过开发一种独特的全新整合灵敏度和控制仪器来促进人类机器人共存，这将使研究人员能够严格评估机器人算法的性能，以响应系统诱发的动态扰动，以模仿现实世界中的情况。该奖项使努力实现了设想的工具的努力。在这个项目中，将实现该仪器的一个组成部分，以支持对腿部机车的研究，该机器人使用的两足机器人集成了由新型仪器可变的阻抗跑步机实现的受控环境，能够模仿各种地形现实。该组件的独特配置将使研究人员能够评估机器人机车控制和计划方法，并将用于生成足够丰富和表现力的数据集，这些数据集将通过Internet广泛共享，以帮助通过机器学习算法进行机车培训模型的研究。拟议的努力将为开发类似工具的发展铺平道路，以实验测试和验证机器人算法的优势和局限性。该奖项将导致开发独特的共享研究工具的独立组成部分，这将使这将是首次实现的测量，从而有助于创建新型实验协议，以严格评估机器人的locotive算法。该项目将涉及评估机车控制器对地形可变性和不确定性的鲁棒性，通过对（i）的运动/运动学进行同步测量，该运动/运动学的新型双皮亚机器人在独特的可变刚度踩踏行走上行走，（ii）机器人的脚和跑步机之间的相互作用，并在环境之间进行了启动，并在III中进行了模拟。这种数据对于研究和评估各种地形现实的运动控制策略的性能是无价的。扰动诸如腿下面的地形刚度的突然变化（包括腿以踩踏或加速灾害形式遇到地形破坏的情况）可以系统地诱导，并且可以通过开发的仪器来测量其效果。在人类大小的机器人同事的背景下，这种能力从未获得。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被视为通过评估而被视为珍贵的支持。

项目成果

On Intuitive Control of Ankle-Foot Prostheses: A Sensor Fusion-based Algorithm for Real-Time Prediction of Transitions to Compliant Surfaces

关于踝足假肢的直观控制：一种基于传感器融合的算法，用于实时预测到顺应表面的过渡

• 发表时间: 2023
• 期刊: IEEE
• 作者: Angelidou, Charikleia;Artemiadis, Panagiotis
• 通讯作者: Artemiadis, Panagiotis

Adjusting the Quasi-Stiffness of an Ankle-Foot Prosthesis Improves Walking Stability during Locomotion over Compliant Terrain

调整踝足假体的准刚度可提高在顺应地形上运动时的行走稳定性

• 发表时间: 2023
• 期刊: IEEE
• 作者: Karakasis, Chrysostomos;Salati, Robert;Artemiadis, Panagiotis
• 通讯作者: Artemiadis, Panagiotis

On Predicting Transitions to Compliant Surfaces in Human Gait via Neural and Kinematic Signals

• DOI: 10.1109/tnsre.2023.3272355
• 发表时间: 2023-01-01
• 期刊: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
• 影响因子: 4.9
• 作者: Angelidou，Charikleia;Artemiadis，Panagiotis
• 通讯作者: Artemiadis，Panagiotis

A Model-Based Analysis of The Effect of Repeated Unilateral Low Stiffness Perturbations on Human Gait: Toward Robot-Assisted Rehabilitation

基于模型的重复单侧低刚度扰动对人类步态影响的分析：走向机器人辅助康复

• DOI: 10.1109/icra48891.2023.10160224
• 发表时间: 2023
• 期刊: IEEE
• 作者: Chambers, Vaughn;Artemiadis, Panagiotis
• 通讯作者: Artemiadis, Panagiotis

Repeated Robot-Assisted Unilateral Stiffness Perturbations Result in Significant Aftereffects Relevant to Post-Stroke Gait Rehabilitation

• DOI: 10.1109/icra46639.2022.9812323
• 发表时间: 2022-01-01
• 期刊: 2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022)
• 作者: Chambers, Vaughn;Artemiadis, Panagiotis
• 通讯作者: Artemiadis, Panagiotis