Aller Felix, Harant Monika, Mombaur Katja
Optimization, Robotics and Biomechanics, Institute of Computer Engineering, Heidelberg University, Heidelberg, Germany.
Department of Mathematics for the Digital Factory, Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany.
Front Robot AI. 2022 Jun 28;9:898696. doi: 10.3389/frobt.2022.898696. eCollection 2022.
To enable the application of humanoid robots outside of laboratory environments, the biped must meet certain requirements. These include, in particular, coping with dynamic motions such as climbing stairs or ramps or walking over irregular terrain. Sit-to-stand transitions also belong to this category. In addition to their actual application such as getting out of vehicles or standing up after sitting, for example, at a table, these motions also provide benefits in terms of performance assessment. Therefore, they have long been used as a sports medical and geriatric assessment for humans. Here, we develop optimized sit-to-stand trajectories using optimal control, which are characterized by their dynamic and humanlike nature. We implement these motions on the humanoid robot REEM-C. Based on the obtained sensor data, we present a unified benchmarking procedure based on two different experimental protocols. These protocols are characterized by their increasing level of difficulty for quantifying different aspects of lower limb performance. We report performance results obtained by REEM-C using two categories of indicators: primary, scenario-specific indicators that assess overall performance (chair height and ankle-to-chair distance) and subsidiary, general indicators that further describe performance. The latter provide a more detailed analysis of the applied motion and are based on metrics such as the angular momentum, zero moment point, capture point, or foot placement estimator. In the process, we identify performance deficiencies of the robot based on the collected data. Thus, this work is an important step toward a unified quantification of bipedal performance in the execution of humanlike and dynamically demanding motions.
为了使类人机器人能够在实验室环境之外得到应用，两足机器人必须满足一定的要求。这些要求尤其包括应对诸如爬楼梯、斜坡或在不规则地形上行走等动态动作。从坐姿到站立的转换也属于这一范畴。除了它们的实际应用，比如从车辆中出来或在桌子旁就座后起身，这些动作在性能评估方面也有好处。因此，它们长期以来一直被用作人类的运动医学和老年医学评估。在这里，我们使用最优控制来开发优化的从坐姿到站立的轨迹，其特点是具有动态性和类人性。我们在类人机器人REEM-C上实现这些动作。基于获得的传感器数据，我们提出了一种基于两种不同实验方案的统一基准测试程序。这些方案的特点是在量化下肢性能的不同方面时难度逐渐增加。我们报告了REEM-C使用两类指标获得的性能结果：主要的、特定场景指标，用于评估整体性能（椅子高度和脚踝到椅子的距离）以及辅助的、通用指标，用于进一步描述性能。后者对所应用的动作提供了更详细的分析，并基于诸如角动量、零力矩点、捕获点或足部位置估计器等指标。在此过程中，我们根据收集到的数据识别机器人的性能缺陷。因此，这项工作是朝着在执行类人且动态要求高的动作时对双足性能进行统一量化迈出的重要一步。
