A method to benchmark the balance resilience of robots.
benchmarking method
performance assessment
robots balance
robustness
self-stabilizing robots
Journal
Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350
Informations de publication
Date de publication:
2022
2022
Historique:
received:
18
11
2021
accepted:
14
12
2022
entrez:
6
2
2023
pubmed:
7
2
2023
medline:
7
2
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36743293
doi: 10.3389/frobt.2022.817870
pii: 817870
pmc: PMC9895781
doi:
Types de publication
Journal Article
Langues
eng
Pagination
817870Informations de copyright
Copyright © 2023 Monteleone, Negrello, Grioli, Catalano, Bicchi and Garabini.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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