E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
Source abstract
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: crossref · Quality: baseline
Research context
OpenAlex topics: 3D Surveying and Cultural Heritage · Robotics and Sensor-Based Localization · Image and Object Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China
Sany Heavy Machinery Co., Ltd., Kunshan, Suzhou 215300, China
Funding
National Natural Science Foundation of China · Ministry of Education “Human Factors and Ergonomics” University Industry Collaborative Education Project · Xie Youbai Design Scientific Research Foundatio
References (19)
Show source-provided references
1. Hemami, A., and Hassani, F. (2009, January 24–27). An overview of autonomous loading of bulk material. Proceedings of the 26th International Symposium on Automation and Robotics in Construction, Austin, TX, USA.10.22260/ISARC2009/0013 ↗
2. Key challenges in automation of earth-moving machines10.1016/j.autcon.2016.05.009 ↗
3. An autonomous excavator system for material loading tasks10.1126/scirobotics.abc3164 ↗
4. IndustryResearch (2024, March 30). Global Excavator Market Report. Available online: https://www.industryresearch.co/global-excavator-market-18836753.
5. Afshar, R.R., Zhang, Y., Vanschoren, J., and Kaymak, U. (2022). Automated reinforcement learning: An overview. arXiv.
6. A robotic excavator for autonomous truck loading10.1023/A:1008914201877 ↗
7. Development of the autonomous hydraulic excavator prototype using 3-D information for motion planning and control10.9746/sicetr.48.488 ↗
8. A 3D surface modeling system for intelligent excavation system10.1016/j.autcon.2011.02.003 ↗
9. Shariati, H., Yeraliyev, A., Terai, B., Tafazoli, S., and Ramezani, M. (2019, January 16–17). Towards autonomous mining via intelligent excavators. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.
10. Forkel, B., Kallwies, J., and Wuensche, H.J. (June, January 30). Probabilistic terrain estimation for autonomous off-road driving. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China.10.1109/ICRA48506.2021.9561689 ↗
11. Foley, T., and Sugerman, J. (2005, January 30–31). KD-tree acceleration structures for a GPU raytracer. Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Conference on Graphics Hardware, Los Angeles, CA, USA.10.1145/1071866.1071869 ↗
12. Rusu, R.B., Blodow, N., and Beetz, M. (2009, January 12–17). Fast point feature histograms (FPFH) for 3D registration. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.10.1109/ROBOT.2009.5152473 ↗
13. The trimmed iterative closest point algorithm
14. Kazhdan, M., Bolitho, M., and Hoppe, H. (2006, January 26–28). Poisson surface reconstruction. Proceedings of the Fourth Eurographics Symposium on Geometry Processing, Cagliari, Italy.
15. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–22). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
16. Parkes, D.C., and Singh, S. (2003). An MDP-based approach to online mechanism design. Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference, MIT Press.
17. An introduction to deep reinforcement learning10.1561/2200000071 ↗
18. (2024, March 30). SY750H | Large Excavator. Available online: https://www.sanyglobal.com/product/excavator/large_excavator/115/847/.
19. Decision transformer: Reinforcement learning via sequence modeling