Sep 14, 2024 · This research proposes a deep reinforcement learning (DRL) method considering spatial-temporal dynamics to perform automated real-time TBM operations.

Adaptive reinforcement learning-based control using proximal policy optimization and slime mould algorithm with experimental tower crane system validation.

Automated position control of tunnel boring machine during excavation using deep reinforcement learning. https://doi.org/10.1016/j.asoc.2024.112234 ·.

Automated position control of tunnel boring machine during excavation using deep reinforcement learning considering spatial-temporal dynamics.

为了实现TBM 位置控制的自动化，使其能够以更高效、更可靠的方式按计划路线行驶，本研究提出了一种考虑时空动态的深度强化学习（DRL）方法，以执行TBM 的自动化实时操作。

This study proposes a hybrid deep learning approach for dynamic attitude and position prediction of the tunnel boring machine (TBM) with high accuracy. By ...

Article in Automation in Construction (December 2024) · Automated position control of tunnel boring machine during excavation using deep reinforcement learning.

Automated position control of tunnel boring machine during excavation using deep reinforcement learning considering spatial-temporal dynamics. Article. Sep ...

Sep 5, 2023 · This paper proposed a real-time optimal control framework of TBM attitude based on reinforcement learning, which contains the geological information predictive ...

In this study, a novel autonomous optimal excavation approach that integrates deep reinforcement learning and optimal control is proposed for shield machines.