Work

E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators

Year: 2024

Type: article

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.

Source: Applied Sciences

DOI: 10.3390/app14166974

Authors: Qianyou Zhao, Le Gao, Duidi Wu, Xinyao Meng, Jin Qi, Hu Jie

Institutions: Shanghai Jiao Tong University, Sany (China)

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728