Optimization of Unmanned Excavator Operation Trajectory Based on Improved Particle Swarm Optimization

To realize the autonomous operation of unmanned excavators, this study takes the four-axis manipulator arm of an unmanned excavator as the research object, uses the five-order B-spline curve for operation trajectory planning, and proposes an improved particle swarm optimization algorithm for the continuous trajectory optimization problem of excavator single operation. The specific contents are as follows: based on the standard PSO algorithm, dynamic parameter update is used to enhance the global search ability in the early stage and improve the local search accuracy in the later stage; the diversity monitoring mechanism is enhanced to avoid premature maturity convergence; multi-particle SA perturbation is introduced, and the new solution is accepted according to the Metropolis criterion to enhance global search ability. The adaptive cooling rate flexibly responds to different search situations and improves the search efficiency and quality of the solution. To verify the effectiveness of the improved PSO–SA algorithm, this study compares it with the standard PSO algorithm, the standard PSO–SA algorithm, and the MPSO algorithm. The simulation results show that the improved PSO–SA algorithm can converge to the global optimal solution more quickly, has the shortest time in trajectory planning, and the generated trajectory has higher tracking accuracy, which ensures that the vibration and impact of the manipulator during motion are effectively suppressed.