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

人类生活空间与机器人工作空间的融合引发了新的控制、估计和感知算法的发展，这些算法具有理论上可证明的性能和安全保证。然而，到目前为止，还没有严格的实验协议和设备来验证所提出的算法的理论预测性能。造成这种情况的一个主要原因是，以系统的方式复制在典型的以人为中心的自然环境中运行的机器人所遇到的动态条件是相关的困难。该项目的最终目标是通过开发一种独特的新的完全集成的传感和控制仪器来促进人与机器人的共存，这将使研究人员能够严格评估机器人算法在响应模拟真实世界条件的系统诱导的动态扰动时的性能。这项裁决使人们能够开始努力实现所设想的文书。在这个项目中，该仪器的一个部件将被实现，以支持使用两足机器人进行腿部运动的研究，该机器人集成到由新型仪表式可变阻抗跑步机实现的可控环境中，该跑步机能够模拟各种地形现实。该组件的独特配置将使研究人员能够评估机器人运动控制和规划方法，并将用于生成足够丰富和富有表现力的数据集，这些数据集将通过互联网广泛共享，以帮助通过机器学习算法训练运动模型的研究。这项拟议的努力将为开发类似的仪器铺平道路，用于实验测试和验证机器人算法的优势和局限性。该奖项将导致开发一种独特的共享研究仪器的独立组件，这将首次使测量成为可能，这将有助于创建新的实验协议，用于严格评估机器人运动算法。该项目将通过同步测量(I)在独特的可变刚度跑步机上行走的新型两足机器人的运动/运动学，(Ii)机器人脚与跑步机之间的相互作用力，以及(Iii)模拟顺应环境时跑步机皮带的偏转，来评估运动控制器对地形变化和不确定性的稳健性。这类数据对于研究和评估运动控制策略在各种地形现实中的性能是非常有价值的。可以系统地诱导扰动，如腿部下方地形刚度的突然变化--包括腿部遇到以降级或升级扰动形式的地形干扰的情况--并且其影响可以通过所开发的仪器来测量。这种能力在人类大小的机器人合作者的背景下从未获得过。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.