# Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles
## 1 Introduction
This solution not only meets the real-time requirements of the operation process but also estimates the local convex area by fitting the quadratic surface using the laser radar, enabling the loader to dynamically adjust during the shoveling process. It is undeniable that this solution improves the automation and intelligence level of the loader operation. However, during actual operation, dust will inevitably appear in the environment, hindering the laser radar's ability to penetrate effectively and resulting in information loss. Since a single sensor cannot effectively solve the problem, the literature [6] adopted a multi-sensor fusion method of LiDAR and vision. This method uses visual sensors to locate the pile, while LiDAR performs three-dimensional reconstruction of the pile surface. This method enhances the flexibility and real-time performance of the loader's shoveling process. However, while this brings about efficient operation, it also increases equipment costs, and the loader can only shovel one position at a time during the shoveling operation. When dealing with a pile of materials that has an irregular surface height and uneven ground contact edges, the loader may find multiple positions that meet the required shoveling conditions. At this time, how the loader should determine the appropriate shoveling position has an important impact on the overall operation's efficiency and safety. Among these challenges, the development of visual detection technology has presented a novel solution. Due to its high image resolution and rich pixel information, it can solve the problem of detecting targets in complex environments. Therefore, the engineering field has widely used visual target detection based on deep learning [7]. For example, reference [8] used the YOLOv5 network to solve the problem that low light, low contrast, and objects with similar crack features in tunnel environments lead to less feature information and thus affect the crack recognition effect; reference [9] used deep learning methods to solve the problem that complex scenes and overlapping vehicles in mining environments affect tracking detection, recognition, and counting. In reference [10],YOLOv5s were employed to investigate the identification of bucket working angles, with the aim of reducing energy consumption during loader shoveling operations. The loader bucket posture can adjust in time to achieve energy savings when material resistance causes the angle to change. Reference [11] used a neural network to identify the change curve of the engine power when the loader shovels different materials, thereby controlling the extension of the loader boom cylinder and the bucket angle, thereby achieving intelligent energy saving. In reference [12], a YOLOv5-based method processed multi-sensor data to achieve obstacle recognition, enabling the excavator to identify and detect objects in a dark environment at night and ensuring safe operation. This paper proposes a method that combines position feature analysis based on edge curvature with an improved deep learning network lightweight recognition model. We select gravel particles as the operation object for two reasons. On the one hand, in engineering operations, gravel is an important raw material for many infrastructure constructions, and it is common for loaders to shovel gravel piles; on the other hand, the irregular shape of gravel makes it difficult for unmanned loaders to find a stable force position when shoveling, which makes it complicated to determine the characteristics and recognition of the shoveling position. This solution employs the coupled simulation of EDEM (engineering discrete element method) and Adams (automatic dynamic analysis of mechanical systems) to initially determine the shape of the shoveling area. It then uses the coupled simulation to find out how the different curves of the gravel pile's edge affect the choice of the shoveling area.