# Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles
## 1 Introduction  
Loaders are widely used in construction sites, mining sites, and other places, and they are important tools in the field of modern construction and engineering. With the rise of the digital revolution, the construction machinery industry is accelerating its transformation to intelligence [1]. However, at present, loaders mainly rely on manual operation for shoveling operations, and there are many problems in the entire shoveling process: First, the operating environment of the loader is relatively complex, potentially posing a threat to the driver's personal safety. Secondly, the shoveling operation process heavily relies on the driver's experience and lacks unified standards, which can significantly impact the efficiency and quality of the project [2]. Realizing unmanned operation of loaders has become an important research topic in order to improve the operating efficiency of these machines. Currently, both XCMG and Doosan Corporation in South Korea have successfully implemented unmanned loader operations. However, during the operation, the loader can only repeatedly operate at the same position and cannot independently determine the shoveling position. Shoving at the same position blindly will inevitably create a large holethere, or excessive shoveling resistance will lift the rear wheel off the ground. It can be seen that correctly judging the shoveling position has an enormous impact. The correct shoveling position not only ensures that the loader collects and fills materials effectively, but also ensures the safety of loading operations and prevents accidents. Therefore, correctly identifying the shoveling position is crucial for unmanned loaders to reduce safety accidents and improve operating efficiency. Currently, extensive research is underway in the field of autonomous operation of unmanned loaders, specifically focusing on the material shoveling process. To achieve autonomous material shoveling, unmanned loaders must first obtain the location information and shape characteristics of the material pile in real time. Therefore, identifying the material pile and reconstructing its morphological characteristics are crucial for selecting the appropriate shoveling material position. In the field of 3D reconstruction of material piles, the literature [3] discretized the map and adopted a grid-based method. An algorithm used the relative angle between the two columnar models on the target grid and the adjacent grid, along with the height information in each grid, to estimate the shape of the material pile, leading to the determination of multiple shoveling positions. However, discretizing the map into a grid will inevitably result in information loss, as the actual material shape is a continuous curve and surface, and the grid method only provides an approximate representation. Given the limitations of this approximate method, collecting point cloud data of the material pile using LiDAR is a viable option for achieving 3D reconstruction. Reference [4] has installed multiple LiDAR in the working environment to achieve distance measurement and 3D reconstruction of the pile. This solution allows the loader to obtain the shape changes in the pile in real time during the shoveling operation. However, this solution has certain usage restrictions. The majority of loaders' actual application scenarios are unstructured, and their fixed working locations limit the loader's working environment range. Given the common usage scenarios of loaders, the literature [5] recommends installing a laser radar on the loader. This allows the loader to obtain point clouds by scanning the surface of the pile during the operation phase. The loader can then use the fitted quadratic surface to estimate the local convex area and distance features that are suitable for the shoveling position.