Mission Planning for Multi-Base-Station Rendezvous-Guided UAV Swarm Return Under Communication Denial: From Static to Rolling Horizon Dynamic Optimization

Abstract

The mission planning problem of using ground-fixed communication base stations to guide Unmanned Aerial Vehicle (UAV) swarms back under communication denial is addressed. The core challenge is to optimally match limited resources with massive UAV demands under constraints such as time windows, base station exclusivity, and relay continuity, which we formulate as an NP-hard combinatorial optimization problem. We first build a static model maximizing comprehensive benefits, incorporating base station heterogeneity and a super-linear congestion penalty for load balancing. We then extend it to a rolling horizon dynamic framework. Through task state partitioning and frozen resource inheritance, this extension decomposes the long-term optimization into sequential finite-horizon subproblems, enabling online decisions as UAV information is gradually revealed. To solve these models, we propose CMSA-MSWOA, which integrates elite opposition-based learning and Lévy flights to navigate the fragmented feasible solution space. Simulation results show 100% guidance coverage across scales from 100 to 500 UAVs in static scenarios, with the benefit advantage over the best benchmark growing from 10.0% to 65.5% as scale increases. In dynamic scenarios, the rolling framework satisfies all constraints and achieves full coverage. While our framework performs robustly in simulations, the current evaluation assumes idealized communication conditions; validation under more complex interference and external testing remains future work. Overall, our model and algorithm offer a useful simulation-based closed-loop framework for resource scheduling in denial environments, providing a foundation for further validation under more realistic field conditions.