التفاصيل البيبلوغرافية
| العنوان: |
Exploiting Robot Redundancy for Online Learning and Control |
| المؤلفون: |
Ficorilli, Marco, Modugno, Valerio, De Luca, Alessandro, Capotondi, Marco |
| بيانات النشر: |
Zenodo |
| سنة النشر: |
2022 |
| المجموعة: |
Zenodo |
| مصطلحات موضوعية: |
model learning, redundant robot, variance optimization |
| الوصف: |
Accurate trajectory tracking in the task space is crit- ical in many robotics applications. Model-based robot controllers are able to ensure very good tracking but lose effectiveness in the presence of model uncertainties. On the other hand, online learning-based control laws can handle poor dynamic modeling, as long as prediction errors are kept small and decrease over time. However, in the case of redundant robots directly controlled in the task space, this condition is not usually met. We present an online learning-based control framework that exploits robot redundancy so as to increase the overall performance and shorten the learning transient. The validity of the proposed approach is shown through a comparative study conducted in simulation on a KUKA LWR4+ robot. |
| نوع الوثيقة: |
conference object |
| اللغة: |
unknown |
| Relation: |
https://zenodo.org/communities/irim-2022; https://doi.org/10.5281/zenodo.7531279; https://doi.org/10.5281/zenodo.7531280; oai:zenodo.org:7531280 |
| DOI: |
10.5281/zenodo.7531280 |
| الاتاحة: |
https://doi.org/10.5281/zenodo.7531280 |
| Rights: |
info:eu-repo/semantics/openAccess ; Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/legalcode |
| رقم الانضمام: |
edsbas.D19FECFB |
| قاعدة البيانات: |
BASE |