Shovel point optimization for unmanned loader based on pile reconstruction

This study details an advanced shovel point optimization system for unmanned loaders, crucial for efficient shovelling operations. First, the shovel point evaluation index is established with reference to the driver's experience. Second, a novel method for pile profile reconstruction is propose...

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Bibliographic Details
Published inComputer-aided civil and infrastructure engineering Vol. 39; no. 14; pp. 2187 - 2203
Main Authors Chen, Guanlong, Wang, Yakun, Li, Xue, Bi, Qiushi, Li, Xuefei
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc 01.07.2024
Subjects
Algorithms
Construction equipment
Loaders
Neural networks
Optimization
Reconstruction
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Summary:This study details an advanced shovel point optimization system for unmanned loaders, crucial for efficient shovelling operations. First, the shovel point evaluation index is established with reference to the driver's experience. Second, a novel method for pile profile reconstruction is proposed, utilizing a trained neural network to detect piles and extracting the point cloud using LiDAR and camera fusion. Subsequently, the system employs optimization algorithm to identify the best shovel point. Finally, 62 consecutive working experiments are successfully conducted. The system's performance closely approximates the driver's choices and achieves an average bucket fill factor of 97.7% for four materials. Results demonstrate the proposed method is reliable and efficient and contributes to the development of automated construction machinery.
ISSN:1093-9687
1467-8667
DOI:10.1111/mice.13190