Impedance Variation and Learning Strategies in Human-Robot Interaction.
IEEE Trans Cybern
; 52(7): 6462-6475, 2022 Jul.
Article
en En
| MEDLINE
| ID: mdl-33449901
In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for the online adjustment of impedance models and controllers for physical human-robot interaction (HRI) are categorized and compared. In this systematic review, studies on: 1) variation and 2) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), and signal requirements (including position, HRI force, and electromyography activity). Different methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and the Gaussian approximation algorithms (e.g., Gaussian mixture model-based and dynamic movement primitives-based strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Robótica
Tipo de estudio:
Systematic_reviews
Límite:
Humans
Idioma:
En
Revista:
IEEE Trans Cybern
Año:
2022
Tipo del documento:
Article
Pais de publicación:
Estados Unidos