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A Survey of High-Level Programming Languages for Operating Swarm Robots
Woosuk Kang, EunJin Jeong, Taejoon Yoon, Seokhaeng Heo, Soonhoi Ha
http://doi.org/10.5626/JOK.2022.49.11.936
Robots used for unmanned tasks in various fields have been developing in the direction of operating multiple robots rather than using only a single robot. Since swarm robotics aims to outperform a single robot through cooperative control of multiple robots, it can efficiently perform tasks such as building local maps and searching for survivors. Although developing and operating such swarm robot systems requires a skilled developer, high-level programming languages and frameworks that can support non-experts to easily specify and operate swarm robots are being developed. In this paper, we selected and introduced 11 swarm robot specification languages tested by simulations or using real robots. We compared the difference between selected languages from the viewpoint of software and swarm robot operation. Finally, limitations of swarm robotics languages developed so far and future tasks are discussed.
Korean Machine Reading Comprehension with S²-Net
Cheoneum Park, Changki Lee, Sulyn Hong, Yigyu Hwang, Taejoon Yoo, Hyunki Kim
http://doi.org/10.5626/JOK.2018.45.12.1260
Machine reading comprehension is the task of understanding a given context and identifying the right answer in context. Simple recurrent unit (SRU) solves the vanishing gradient problem in recurrent neural network (RNN) by using neural gate such as gated recurrent unit (GRU), and removes previous hidden state from gate input to improve speed. Self-matching network is used in r-net, and this has a similar effect as coreference resolution can show similar semantic context information by calculating attention weight for its RNN sequence. In this paper, we propose a S²-Net model that add self-matching layer to an encoder using stacked SRUs and constructs a Korean machine reading comprehension dataset. Experimental results reveal the proposed S²-Net model has EM 70.81% and F1 82.48% performance in Korean machine reading comprehension.
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