A multi-objective trajectory planning method of the excavator based on the modified multi-objective particle swarm algorithm

With the vigorous promotion of new infrastructure construction, the construction environment has become increasingly complex, leading to heightened demands for the accuracy, efficiency, and energy consumption of excavator operations. To enhance the efficiency and stability of excavation trajectories, this paper proposes a multi-objective trajectory planning method for excavator work devices. First, a kinematic model of the excavator’s working device is established, and a fifth-order non-uniform B-spline curve is employed to plan the bucket’s end trajectory. Building on the traditional multi-objective particle swarm optimization algorithm, we construct an improved multi-objective particle swarm algorithm by incorporating adaptive inertia weights and learning factors, introducing a differential evolution strategy, and maintaining an external archive based on crowding distance. Finally, simulations are conducted to compare the performance of MOPSO, NSGA-II, MOPSO-DE, and the multi-objective optimization algorithm proposed in this paper, demonstrating the superiority of the proposed method. The approach presented herein effectively facilitates the planning and optimization of excavator trajectories and holds significant research implications.