Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall

نویسندگان

چکیده

A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve human-robot cooperation performance for a curtain wall installation task. This breaks the traditional idea that robot regarded as follower or only adjusts leader and in cooperation. In this paper, self-learning which can dynamically adapt continuously adjust initiative weight of according change Firstly, physical model, including role factor built. Then, model real time established, reward action designed. The be adjusted comprehensive interaction force robot’s Jerk during repeated installation. Finally, adjustment rule established above improves performance. Experiments dynamic effect weighting coefficient result have been verified. results show realize adaptation achieve dual optimization goal reducing sum cooperator Jerk.

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ژورنال

عنوان ژورنال: Cmes-computer Modeling in Engineering & Sciences

سال: 2024

ISSN: ['1526-1492', '1526-1506']

DOI: https://doi.org/10.32604/cmes.2023.029729