Optimization of dynamic sit-to-stand trajectories to assess whole-body motion performance of the humanoid Robot REEM-C

• Verfasst von: Aller, Felix [VerfasserIn]
• Verfasst von: Harant, Monika [VerfasserIn]
• Verfasst von: Mombaur, Katja [VerfasserIn]
• Titel: Optimization of dynamic sit-to-stand trajectories to assess whole-body motion performance of the humanoid Robot REEM-C
• Verf.angabe: Felix Aller, Monika Harant and Katja Mombaur
• E-Jahr: 2022
• Jahr: 28 June 2022
• Umfang: 17 S.
• Fussnoten: Gesehen am 03.11.2022
• Titel Quelle: Enthalten in: Frontiers in robotics and AI
• Ort Quelle: Lausanne : [Verlag nicht ermittelbar], 2014
• Jahr Quelle: 2022
• Band/Heft Quelle: 9(2022), Artikel-ID 898696, Seite 1-17
• ISSN Quelle: 2296-9144
• DOI: doi:10.3389/frobt.2022.898696
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1820703401
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

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.