Monteleone Simone, Negrello Francesca, Grioli Giorgio, Catalano Manuel G, Bicchi Antonio, Garabini Manolo
Centro di Ricerca E. Piaggio e Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, Italy.
Istituto Italiano di Tecnologia, Genova, Italy.
Front Robot AI. 2023 Jan 20;9:817870. doi: 10.3389/frobt.2022.817870. eCollection 2022.
Robots that work in unstructured scenarios are often subjected to collisions with the environment or external agents. Accordingly, recently, researchers focused on designing robust and resilient systems. This work presents a framework that quantitatively assesses the balancing resilience of self-stabilizing robots subjected to external perturbations. Our proposed framework consists of a set of novel Performance Indicators (PIs), experimental protocols for the reliable and repeatable measurement of the PIs, and a novel testbed to execute the protocols. The design of the testbed, the control structure, the post-processing software, and all the documentation related to the performance indicators and protocols are provided as open-source material so that other institutions can replicate the system. As an example of the application of our method, we report a set of experimental tests on a two-wheeled humanoid robot, with an experimental campaign of more than 1100 tests. The investigation demonstrates high repeatability and efficacy in executing reliable and precise perturbations.
在非结构化场景中工作的机器人经常会与环境或外部物体发生碰撞。因此，近年来，研究人员致力于设计强大且有弹性的系统。这项工作提出了一个框架，用于定量评估受外部干扰的自稳定机器人的平衡弹性。我们提出的框架包括一组新颖的性能指标（PI）、用于可靠且可重复测量这些性能指标的实验方案，以及一个用于执行这些方案的新型测试平台。测试平台的设计、控制结构、后处理软件以及所有与性能指标和方案相关的文档都作为开源材料提供，以便其他机构能够复制该系统。作为我们方法应用的一个例子，我们报告了在一个两轮人形机器人上进行的一组实验测试，实验活动超过1100次测试。调查表明，在执行可靠且精确的干扰方面具有高度的可重复性和有效性。
