## 5 Validation Experiment and Discussion
### Discussion  
This paper is based on the constructed automatic operation platform of the wheel loader. The mathematical model of fuel consumption is established by SVM. And the optimized shoveling trajectory with three different engine speeds in the gravel pile and the sand pile is obtained by combining IPSO. By comparing with the fuel consumption of manually controlled shoveling, the fuel consumption with the optimized shoveling trajectory has been significantly reduced. In this study, only the effect of different engine speeds and different shoveling trajectories on fuel consumption has been considered. There are other influencing factors for fuel consumption. The main factors include the driver's operating ability and the age of the wheel loader. The driver's operating ability will directly affect the fuel consumption, varying from two to three times for different drivers [42]. The influence of manual operation on fuel consumption mainly includes the selection of insertion depth [43], the bucket attitude control during shoveling [44], and the cooperation between the gas pedal and the brake pedal [45]. The power possessed by the wheel loader is different at different engine speeds. When the engine speed is low, if the insertion is too deep, the bucket will get stuck in the pile and cause the tires to slip, increasing fuel consumption. Moreover, if the bucket is turned too much during the shoveling process, the resistance to the bucket will surge, which requires additional fuel consumption. It is worth noting that the shoveling process is not a uniform speed process, which needs a moderate increase and decrease, that is, better cooperation between the gas pedal and the brake pedal. For the age of the wheel loader, it is sure that with the increase in use time, there will be different degrees of wear between the mechanisms, and fuel efficiency will be reduced. In particular, the bucket tip of the bucket, as the part that contacts the pile, suffers the most wear during the long-term shoveling process [46]. Timely replacement or repair of bucket tips is an effective way to reduce fuel consumption. Automatic shoveling of wheel loaders has undoubtedly become the mainstream development trend in the future [47; 48], and automatic shoveling means a more standard operation mode, higher fuel economy, and a corresponding reduction in wear for wheel loaders. At the same time, human safety is ensured to a large extent. Most importantly, automatic shoveling is an effective way to reduce fuel consumption. [PERSON] et al. [49] developed a controller based on deep reinforcement learning. They conducted experiments on a shoveling robot with three degrees of freedom, which included lift, turn, and speed, and the shoveling efficiency of the robot with this controller was significantly higher than that of the robot with manual control. [PERSON] et al. [50] proposed a time-lag neural network-based shoveling operation method for wheel loaders was conducted, and several experiments were successful, with only 26% longer operation time and improved fuel economy for the same shoveling weight. [PERSON] et al. [51] established an automatic shoveling model for wheel loaders by Q-learning, which could reduce fuel consumption by 8.0% and 10.6%, respectively, compared to manual operation by two drivers. Our study promotes the development of automatic shoveling by planning a reasonable shoveling trajectory for it. Moreover, it demonstrates excellent advantages and potential compared to manually controlled shoveling. In this study, we consider only the fuel consumption with an optimized shoveling trajectory. However, the shoveling weight is the critical indicator for evaluating the efficiency of wheel loaders. The importance of shoveling weight and fuel consumption varies for different shoveling environments. It is essential to recognize that higher shoveling weights are not better. If the wheel loader is overloaded for a long time, it may lead to greater wear on the wheel loader and reduce its service life.