ED-MOEA: An Event-Driven Multi-Objective Evolutionary Algorithm for Cooperative Planning of Heterogeneous UAV-UGV Systems in Dynamic Post-Disaster Environments

Post-disaster rescue operations require rapid and efficient coordination of heterogeneous 2 unmanned systems to locate survivors and deliver supplies in dynamic, uncertain environ- 3 ments. Existing approaches predominantly adopt a static task allocation paradigm, which 4 fails to adapt to scenarios where new survivors are continually discovered and obstacles 5 appear unexpectedly. This paper proposes ED-MOEA (Event-Driven Multi-Objective Evolu- 6 tionary Algorithm), establishing a closed-loop cooperative sensing-planning mechanism for 7 heterogeneous UAV-UGV systems. The core innovation lies in an event-triggered dynamic 8 replanning strategy: when UAVs detect new task points or obstacles during flight, the 9 system immediately broadcasts information to UGVs and triggers a warm-start replanning 10 process. The problem is formulated as a three-objective optimization considering makespan 11 minimization, energy consumption minimization, and task coverage maximization, while 12 accounting for heterogeneous mobility constraints—UAVs can fly directly over obstacles 13 while UGVs must navigate around them. Experiments on five benchmark scenarios and 14 a real case based on the 2024 Hualien earthquake demonstrate that ED-MOEA achieves 15 the highest hypervolume (HV) across all scenarios, outperforming existing multi-objective 16 algorithms including NSGA-III, MOEA/D-DE, RVEA, and LMEA. The heterogeneous 17 coordination achieves 16.7% higher task coverage compared to UAV-only systems (0.910 18 vs. 0.780), while the warm-start strategy significantly accelerates replanning convergence 19 (over 35% reduction in generations required), ensuring real-time response capability under 20 6 seconds for scenarios with up to 70 tasks.