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.

中文翻译：

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基于桩重构的无人装载机铲点优化

这项研究详细介绍了一种先进的无人装载机铲点优化系统，这对于高效铲运作业至关重要。首先，参考驾驶员经验建立铲点评价指标。其次，提出了一种桩轮廓重建的新方法，利用经过训练的神经网络来检测桩并使用激光雷达和相机融合提取点云。随后，系统采用优化算法来识别最佳铲点。最终成功进行了62次连续工作实验。该系统的性能非常接近驾驶员的选择，四种物料的平均铲斗填充系数达到 97.7%。结果表明，该方法可靠、高效，有助于自动化工程机械的发展。