# Research on the Trajectory and Operational Performance of Wheel Loader Automatic Shoveling
## 1 Introduction  
The automated operation of loaders [1] can reduce labor and increase the safety of people working in harsh environments, which plays a crucial role in modern construction [2]. Thus, the automatic shoveling operation performance of loaders has received much attention. Wheel loaders have many operational performances [3], such as full bucket rate [4], operational resistance [5], fuel consumption [6,7], etc. These are essential criteria to evaluate the loader in the automatic shoveling operation process. Many factors affect the operational performance of the loader, such as material type, trajectory, the resting angle of the pile, etc., of which the operational trajectory has a more significant impact. Since the whole process of automatic shoveling of the loader depends on the operation trajectory of the import controller [8], it is a critical technology for the loader to achieve automatic shoveling operation. Therefore, it is urgent to investigate the influence of shovel trajectory on the operational performance of wheel loaders. At present, the research on the automatic shoveling of loaders has achieved fruitful results. [PERSON] et al. [9] found four solutions to fuel consumption that can be applied to wheel loaders from HMPST, then found the optimal fuel-saving solution from the four solutions, and finally built a simulation model of the loader power system to verify the effectiveness of the solution. [PERSON] et al. [10] studied the buck's forces at various stages ofthe loader shoveling process, modeled the forces acting on the bucket, and experimentally validated the model. [PERSON] et al. [11] developed a general and reproducible efficiency test method for the reproducible evaluation of the fuel and handling performance of wheel loaders, which can be tested for acceleration, and resting angle-induced drag by physical models and by developing a real-time test program. [PERSON] et al. [12] proposed a reinforcement learning method to improve a loader's automatic shoveling performance, built a statistical model to simulate the natural environment, and then trained the proposed algorithm on the statistical model. The final results proved that the algorithm has good adaptability and fuel consumption performance. [PERSON] et al. [13] developed a method for repeated evaluation of the fuel performance and handling performance of various wheel loaders to measure the loader's overall efficiency. Other scholars have also optimized these aspects by reducing operating resistance, improving practical work performed by structural optimization, and simplifying the loader operation process. Since most of them still use methods such as modeling [14], simulation design [15], and prediction [16], it is evident that the final result is also only a theoretical optimization. There are still considerable differences from the actual, and it is also difficult to have a practical improvement in the optimization of loader-shovel operation performance. Others have used machine learning [17; 18; 19] methods to improve the operational performance of loaders. [PERSON] et al. [20] studied reinforcement learning control of loaders in a simulated environment using a deep neural network approach. At the start of shoveling, one agent selects the digging position via the depth camera, and another agent is responsible for filling the bucket with the material. The agent can continuously learn strategies for efficient shoveling during the shoveling process and shovel up to approximately 75% of the maximum capacity. However, the method cannot cope with situations that are significantly distinct from those included in the training. [PERSON] et al. [21] proposed a neural network reinforcement learning method to enhance the shovel weight of a loader. The shovel weight could be improved by 5-10% after automatic shoveling testing and reinforcement learning of the loader using this method. Nevertheless, the method was only validated on one material, leading to the fact that this method is only sometimes applied to other materials.