Abstract
To realize the skill migration and generalization of robotic arm in industrial production. We extracts 7 basic movement primitives from industrial tasks. Parameterize each primitive and build a library of movement primitives, and give the connection mode between primitives. Then, the robotic arm Behavior Tree (BT) is constructed according to the execution logic of the task. When faced with a new task, use Dynamic Movement Prmitives (DMP) to generalize the movement primitives according to the target pose. When faced with an unknown environment, control the action by selecting a specific BT. Finally, the effectiveness of the framework is verified through experiments.
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Acknowledgment
This work was supported by the “New Generation Artificial Intelligence” Key Special Project of the Guangdong Key Area Research and Development Program, “Multiple Degrees of Freedom Intelligent Body Complex Skill Autonomous Learning, Key Components and 3C Manufacturing Demonstration Application” (2021B010410002). Guangdong Provincial Key Area R&D Program (2020B0404030001) and National Natural Science Foundation of China - Youth Project “Study on Adaptive Problems and Update Mechanisms of Online Learning of Visual Ash Data Stream” micron-level real-time vision inspection technology and system research and development Floating Measurement of Tailings” (62106048).
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Liu, M. et al. (2023). Robotic Arm Movement Primitives Assembly Planning Method Based on BT and DMP. In: Sun, F., Cangelosi, A., Zhang, J., Yu, Y., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2022. Communications in Computer and Information Science, vol 1787. Springer, Singapore. https://doi.org/10.1007/978-981-99-0617-8_27
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DOI: https://doi.org/10.1007/978-981-99-0617-8_27
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