A Spatiotemporal Uncertainty-Aware Task Planning Framework for Cooperative Vehicle–UAV Remote Sensing Monitoring and Verification in Complex Terrain

Abstract

Unmanned aerial vehicles (UAVs) have been increasingly used as flexible sensing platforms for remote sensing applications due to their rapid deployment and efficient data acquisition capabilities. Cooperative vehicle–UAV systems have shown great potential for large-scale remote sensing monitoring and field verification. However, existing task planning methods often overlook the characteristics of remote sensing verification missions, including fragmented target parcels and spatiotemporal uncertainties caused by complex terrain, which limits scheduling efficiency and robustness. To address these challenges, this paper proposes a spatiotemporal uncertainty-aware task planning framework for vehicle–UAV cooperative remote sensing verification. The framework integrates UAV capability-constrained task region generation, terrain-driven spatial uncertainty risk classification, a dual-channel genetic algorithm (DC-GA), and an uncertainty-aware two-stage scheduling framework (UATSF). Experiments in two real-world study areas validate the effectiveness of the proposed framework. The region-merging strategy reduces total travel distance and travel time while improving UAV utilization, and DC-GA consistently reduces the system makespan across different vehicle configurations. Moreover, the two-stage strategy, which combines deterministic optimization with Monte Carlo robustness assessment, reduces planned completion time by 6.80–11.09% compared with worst-case scheduling while achieving 86.20–98.40% reliability under the modeled uncertainty and assumed simulation settings. The results demonstrate that the proposed framework improves task planning efficiency and robustness for vehicle–UAV cooperative operations in complex terrain environments.