Enhancing safety and efficiency in automated container terminals: Route planning for hazardous material AGV using LSTM neural network and Deep Q-Network

As the proliferation and development of automated container terminal continue, the issues of efficiency and safety become increasingly significant. The container yard is one of the most crucial cargo distribution centers in a terminal. Automated Guided Vehicles (AGVs) that carry materials of varying...

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Autori principali:	Fei Li, Junchi Cheng, Zhiqi Mao, Yuhao Wang, Pingfa Feng
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Tsinghua University Press 2024-03-01
Serie:	Journal of Intelligent and Connected Vehicles
Soggetti:	container yard route planning hazardous material automated guided vehicle (agv) long short-term memory (lstm) deep q-network (dqn)
Accesso online:	https://www.sciopen.com/article/10.26599/JICV.2023.9210041
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