| Autor: |
Xi Lan, Yuansong Qiao, Brian Lee |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 78988-79002 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3409076 |
| Popis: |
Recent breakthroughs in hierarchical multi-agent deep reinforcement learning (HMADRL) are propelling the development of sophisticated multi-robot systems, particularly in the realm of complex coordination tasks. These advancements hold significant potential for addressing the intricate challenges inherent in fast-evolving sectors such as intelligent manufacturing. In this study, we introduce an innovative simulator tailored for a multi-robot pick-and-place (PnP) operation, built upon the OpenAI Gym framework. Our aim is to demonstrate the efficacy of HMADRL algorithms for multi robot coordination in a manufacturing setting, concentrating on their influence on the gripping rate, a crucial indicator for gauging system performance and operational efficiency. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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