Modeling and Verification of an Acquisition Strategy for Wheel Loader’s Working Trajectories and Resistance

Abstract

To overcome the difficulty of collecting the working resistance and working trajectory of a wheel loader, this paper constructs a statics model of the bucket working resistance and a kinematics model of the working trajectory in the shoveling process and analyzes the key parameters of measuring the working resistance and working trajectory. Based on this, a working resistance and working trajectory acquisition strategy is proposed. To verify the effectiveness of the acquisition strategy, the in-service operation data of fine sand and loose soil shoveled by the wheel loader are collected and analyzed. Then, the test-fitted working resistance and working trajectory are obtained, and the working trajectory is input into the RecurDyn–EDEM co-simulation model to obtain the simulation-fitted working resistance. Considering the complex working conditions of the wheel loader, it is difficult to obtain accurate working resistance, and the actual working resistance is also a relative value. Therefore, a strong correlation between the two curves indicates that the acquisition strategy of the wheel loader’s working trajectory and working resistance proposed in this paper is feasible.

Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption Reduction

“…However, in these studies, the optimization of shoveling trajectory is mainly carried out by theoretical analysis and simulation analysis. It ignores the high randomness and complexity of the shoveling operation process, and there is a specific error between the dynamics or mathematical model and the actual shoveling operation [18][19][20]. As a result, it is difficult to achieve the desired effect in the actual shoveling operation.…”