Abstract
In order to solve the problem of low-efficiency in the manual operation process of nameplate pasting for sand mold, an intelligent simulation system based on visual sensing and industrial robot is designed to paste nameplate on sand molds, and a deep reinforcement learning control method is proposed. The simulation system including the robot, visual sensor and sand mold is established in ROS combined with the physical simulation engine Gazebo. Then the task of nameplate pasting for sand molds is expressed as a markov process and the robot is trained by DQN method to learn a strategy to complete the task of pasting the nameplate of sand mold. A multi-level reward function algorithm based on multi-distances and collision information is proposed to improve the train success rate. Finally, the method is verified in the simulation system. The results show that the nameplate can be quickly attached to the sand mold cavity by the industrial robot.
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Acknowledgements
This work is partially supported by the National Science Foundation for Young Scientists of China (Grant No. 51805071), the Fundamental Research Funds for the Central Universities (Grant No. DUT18RC(3)073) and Changjiang Scholar Program of Chinese Ministry of Education (No. T2017030).
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Tuo, G., Li, T., Qin, H., Huang, B., Liu, K., Wang, Y. (2019). Control of Nameplate Pasting Robot for Sand Mold Based on Deep Reinforcement Learning. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11745. Springer, Cham. https://doi.org/10.1007/978-3-030-27529-7_32
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DOI: https://doi.org/10.1007/978-3-030-27529-7_32
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