Using deep reinforcement learning to guide PCBS welding robot to solve multi-objective optimization tasks

Fan Liu; Wanfeng Shang; Xizhang Chen; Yang Wang; Xiangdong Kong

doi:10.1117/12.3004933

9 October 2023 Using deep reinforcement learning to guide PCBS welding robot to solve multi-objective optimization tasks

Fan Liu, Wanfeng Shang, Xizhang Chen, Yang Wang, Xiangdong Kong

Author Affiliations +

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127912A (2023) https://doi.org/10.1117/12.3004933
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

An optimized PCB welding sequence is important to improve both the welding rate and the safety of the robot. Both problems are essentially combinatorial optimization problems, and both are NP problems. In this paper, we use DRL for solving the end trajectory smoothing when the tin welding robot performs path planning on PCBs degree and distance problems for path planning on PCBs. In our approach, we use the concept of combinatorial optimization reward function to redefine the loss function of the PN. The better performance of using the proposed method compared to the manual heuristic algorithm is demonstrated through numerical experiments, in real PCBs scenario and in a simulation environment.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fan Liu, Wanfeng Shang, Xizhang Chen, Yang Wang, and Xiangdong Kong "Using deep reinforcement learning to guide PCBS welding robot to solve multi-objective optimization tasks", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127912A (9 October 2023); https://doi.org/10.1117/12.3004933

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