# Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles
## 6 Conclusions  
Because the shape of the material pile changes during the cyclic operation of the unmanned loader, the unmanned loader cannot identify a suitable scooping position, and the current recognition network model is costly to deploy on mobile devices. Therefore, to address these issues, this paper proposes a method that combines edge curvature-based position detection features with a lightweight deep learning network model. We select gravel, a common material in loader operations, as the research object. We first analyze the positional characteristics of the loader's scooping operation, primarily using the EDEM-Adams coupled simulation to examine the impact of the edge curvature of different piles on the scooping operation process. We conclude that the area with a convex gravel pile edge and a small degree of curvature is suitable as a scooping position, after comprehensive evaluation using radar charts and quantifying the impact. Then, based on the analysis results, we combined the proposed lightweight YOLOv5s improved model to realize the recognition and detection of the gravel material pile shoveling position. We set up a scene in a laboratory environment and used the improved model to detect the rubble pile. The results show that the model can achieve an average detection accuracy of 98% for the location of suitable material for shoveling. Compared to the original YOLOv5s model, we have reduced its parameters and GFlops by 32.5% and 55.2%, respectively. From the results, we can see that our proposed method of combining a location detection feature based on edge curvature with a lightweight model of a deep learning network is feasible. However, this paper still has certain limitations. On the one hand, we used the EDEM-Adams coupling simulation to analyze the effect of the curvature of the material pile edge on the scooping operation. This method simplifies the intricate process of carrying out actual loader operations. In a real scooping scenario, it is difficult to accurately simulate the complex dynamic processes such as system vibration, impact, and dynamic response, which leads to differences between the simulation results and the actual dynamic process. On the other hand, although we have made lightweight improvements to the deep learning model, we have not embedded it in a mobile device. Theoretically, this has reduced the computing requirements, but in real-world mobile devices, it is unfeasible to assess the model's optimal adaptation to hardware resources like CPU, GPU, and memory, thereby impacting the model's overall operational efficiency and performance. In future research, we aim to address these limitations by introducing a multi-physics model for the EDEM-Adams coupled simulation problem. We will calibrate and optimize this model using actual operational data, thereby improving the simulation accuracy of complex dynamic processes in the loader operation simulation and reducing any discrepancies with the actual situation. For the problem of embedding deep learning models in mobile devices, we will conduct experiments on a variety of mobile devices, analyze the hardware characteristics, adjust the model, and continuously improve it with the help of performance monitoring to ensure that the model adapts to the hardware and improve the operating efficiency and performance of the model on mobile devices. Methodology, B.L. and S.J. ; software, H.Z. ; resources, B.X. and Y.X. ; data curation, W.Z. ; writing--original draft preparation, H.Z. ; writing--review and editing, B.L. All authors have read and agreed to the published version of the manuscript. This research is supported by the Guangxi Science and Technology Major Project, grant number AA22068064; Liuzhou Science and Technology Plan Project, grant number 2022 AAB0101; Central Government-Guided coal Science and Technology Development Fund Program, grant number 2023 PRJ0101. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request.