E-GCDT: advanced reinforcement learning with GAN-enhanced data for continuous excavation system

E-GCDT: advanced reinforcement learning with GAN-enhanced data for continuous excavation system

연구 분야: Verification

학회: Applied Intelligence

The automation of excavator operations entails the development and implementation of systems that allow excavators to execute tasks autonomously, thereby significantly reducing the need for human intervention. By integrating advanced sensors and artificial intelligence algorithms, these systems aim to increase operational efficiency, safety, and precision in construction and mining. However, previously developed methods have two weaknesses. First, existing automated excavator systems struggle with adapting to diverse and complex environmental conditions and with precision in control mechanisms. Second, operating an excavator involves multiple, repeated decisions that need to be modeled, planned, and executed in real time. However, there is a significant lack of comprehensive datasets that reflect real-world excavation operations to support this process. In this paper, we present an innovative system named E-GCDT. This system integrates the DoppelGANger module, which generates action time series by emulating human-mined trajectories through a generative adversarial mechanism and replays them in a simulation environment, ultimately expanding the dataset to 155 continuous mining trajectories. Furthermore, E-GCDT integrates terrain features into the decision-making process with the contrastive language-image pre-training model (CLIP), in which the decision transformer optimizes trajectory planning for efficient and accurate continuous excavation tasks. E-GCDT uniquely integrates advanced data augmentation and terrain awareness, developing an advanced Markov decision framework (DT) for continuous excavation tasks. The experimental results of a bulldozer verify that the efficiency of E-GCDT surpasses human efficiency. This system sets a new standard for continuous autonomous mining, and this study provides a new perspective on the application of reinforcement learning in industrial environments.