Multi-Objective Trajectory Planning Method for Air–Ground Collaborative Logistics UAVs Under Preemptive Scheduling

Abstract

To effectively address the challenges of complex spatiotemporal conflicts, dynamic obstacle avoidance, and coordinated multi-objective optimization in preemptive multi-UAV logistics delivery within complex airspace, this study proposes a Hybrid Improved Multi-Objective Cuckoo Search algorithm (HI-MOCS) for preemptive multi-UAV cooperative logistics scheduling and planning. To overcome the limitations of conventional MOCS, including a low proportion of feasible solutions under complex constraints, susceptibility to local optima, and uneven distribution of multi-objective solution sets, a multi-constraint physical model and a multidimensional evaluation framework are established for preemptive scheduling. A positive knowledge-transfer mechanism based on the co-evolution of primary and auxiliary populations is developed, in which constraint-violation information is used to guide infeasible solutions toward the feasible region. A hybrid heuristic population initialization strategy combining emergency-order priority and spatial scanning rules is introduced to increase the proportion of high-quality feasible solutions in the initial population. In addition, a nonlinear dynamic adaptive parameter-adjustment strategy is designed to balance global exploration and local exploitation, while an iterative truncation-based environmental selection mechanism using the shortest-distance criterion is employed to improve the distribution quality of the Pareto solution set. The experimental results show that, in the benchmark scenario, HI-MOCS achieves an average increase of 33.26% in the total order completion rate and an average reduction of 15.34% in emergency response time compared with 11 multi-objective optimization algorithms, while also exhibiting favorable performance in terms of flight distance per completed order. The fleet-size analysis shows that the 15-UAV configuration achieves the lowest best mean fitness. The safety-distance analysis indicates that, compared with the other safety-distance settings, the 30 m setting increases the total order completion rate by an average of 26.55%, while reducing emergency response time and flight distance per completed order by 27.36% and 33.72%, respectively. The task-scale analysis shows that the 50-order scenario achieves the lowest best mean fitness. Further ablation experiments demonstrate that, compared with the average performance of MOCS and the four single-strategy variants, the complete HI-MOCS improves the total order completion rate by 20.27%, while reducing emergency response time and flight distance per completed order by 20.71% and 36.18%, respectively. The HV, IGD, and Pareto-front results further confirm that the synergistic effects of the four improvement mechanisms effectively enhance the multi-objective optimization performance and the quality of the nondominated solution set. The current study is still validated under simulation conditions assuming reliable GNSS positioning and communication links, without explicitly considering communication delays.