Abstract: To address the need for efficient and energy-saving trajectory planning in autonomous loader excavation operations, a multi-objective shoveling trajectory optimization method is proposed based on non-uniform rational B-splines (NURBS). First, a soil-bucket coupling model is established using soil mechanics failure theory, which accurately characterizes the resistance encountered during the excavation process. Subsequently, the soil-bucket interaction is incorporated into the dynamic modeling of the loader’s working device, thereby improving the model’s representation of actual working conditions. Finally, a nonlinear multi-objective and multi-constraint trajectory optimization model is constructed, with both operation time and energy consumption considered as synergistic performance indices. This optimization problem is efficiently solved using a genetic algorithm. To validate the effectiveness and superiority of the proposed method, a high-fidelity mathematical-physical co-simulation platform is developed, and simulation experiments are conducted. The results demonstrate that the proposed approach can generate smooth and feasible excavation trajectories, with hydraulic cylinder velocities and output forces maintained within reasonable ranges during critical stages. The loading process is both efficient and stable, exhibiting good operational continuity.

Key words: unmanned loader, soil-bucket coupling model, non-uniform rational B-splines, trajectory optimization