| Autor: |
Daewoo Choi, Tuyen P. Le, DongHyun Lee |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 18th International Conference on Ubiquitous Robots (UR). |
| DOI: |
10.1109/ur52253.2021.9494631 |
| Popis: |
Automated pick-and-place systems are seen as some of the most sensible commercial applications. However, developing such a system is still challenging because it requires not only high quality connected components but also that these connected components be synchronized to meet uncertainty and real-time constraints. This paper introduces a simulated conveyor belt-based pick-and-place system and proposes a joint learning algorithm to control manipulators for performing a sequence of pick and place tasks. The framework uses a deep reinforcement learning algorithm to teach an agent to pick objects that uncertainly and partial-observably appear on the conveyor. Moreover, this framework uses an Robot operating system (ROS) service to generate trajectories that navigate manipulators between objects and placements. Using an industrial-grade platform like ROS allows the generation of reliable path motions that meet various hardware and real-time constraints. We evaluate the effectiveness of our framework over a wide range of simulations against rule-based methods. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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