Zhao, Q.; Gao, L.; Wu, D.; Meng, X.; Qi, J.; Jie, H. E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators. Preprints2024, 2024071871. https://doi.org/10.20944/preprints202407.1871.v1
APA Style
Zhao, Q., Gao, L., Wu, D., Meng, X., Qi, J., & Jie, H. (2024). E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators. Preprints. https://doi.org/10.20944/preprints202407.1871.v1
Chicago/Turabian Style
Zhao, Q., Jin Qi and Hu Jie. 2024 "E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators" Preprints. https://doi.org/10.20944/preprints202407.1871.v1
Abstract
The shift towards autonomous excavators in construction and mining is a significant leap towards enhancing operational efficiency and ensuring worker safety. However, it presents challenges such as the need for sophisticated decision-making and environmental perception due to complex terrains and diverse conditions. Our study introduces the E-GTN framework, a novel approach tailored for autonomous excavation that leverages advanced multisensor fusion and a custom-designed convolutional neural network to address these challenges. Results demonstrate that GridNet effectively processes grid data, enabling the reinforcement learning algorithm to make informed decisions, thereby ensuring efficient and intelligent autonomous excavator performance. The study concludes that the E-GTN framework offers a robust solution for the challenges in unmanned excavator operations, providing a valuable platform for future advancements in the field.
Keywords
Excavator Automation; Terrain Feature Extraction; Point Cloud; Reinforcement Learning
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
