Distributed joint trajectory optimization for search and relay UAVs under urban NLoS communication constraints

Abstract

Multi-UAV cooperative search in dense urban environments faces significant challenges due to non-line-of-sight (NLoS) conditions, which substantially degrades both mission efficiency and coordination reliability. To address these challenges, this paper proposes a joint trajectory optimization framework for a heterogeneous UAV team, in which dedicated relay UAVs are explicitly incorporated to support reliable search operations. A communication-aware objective function is formulated by coupling search rewards with realistic urban occlusion models, enabling the UAV team to autonomously balance exploration coverage and connectivity maintenance. The resulting optimization problem is solved online via a Distributed Model Predictive Control (DMPC) scheme, where the receding-horizon decision process is reformulated as a Continuous Distributed Constraint Optimization Problem (C-DCOP) to facilitate scalable coordination. To efficiently handle the associated high-dimensional problem, an Adaptive Differential Evolution–enhanced Distributed Stochastic Algorithm (ADE-DSA) is developed, which enhances global exploration capability while preserving the decentralized, local-update nature of classical DSA. Extensive simulations demonstrate that ADE-DSA consistently outperforms state-of-the-art distributed solvers on both standard C-DCOP benchmarks and the proposed joint search–relay problem formulation. Moreover, the results show that the proposed framework significantly improves search efficiency and communication robustness in dense urban scenarios.