Cooperative Path Planning for Heterogeneous UAV Swarms: A Stackelberg Game Approach
Zhe Zhang, Ju Jiang, Keck Voon Ling, Xinhua Wang, Wen‐An Zhang · IEEE Transactions on Automation Science and Engineering · 2025
The coordinated operations of Stealth Unmanned Aerial Vehicle (SUAV) and Swarming Drones (SD) have demonstrated formidable power in the military domain. Efficient path planning is a critical technology that enhances combat effectiveness. This paper proposes a game-theoretic optimization approach to achieve cooperative penetration and target search path planning for swarm UAVs. Multi-task and multi-objective optimization models are developed for complex scenarios whose optimal solutions are NP-hard. Consequently, SUAV and SD are defined as leader and followers, respectively. The formulated Stackelberg game model enables distributed intelligent decision-making for SUAV and SD. We theoretically prove that by selecting an appropriate potential function, subgames within the leader-level and followers-level become an Ordinal Potential Game (OPG) with a Nash equilibrium, thereby ensuring the existence of a Stackelberg Equilibrium (SE) through leader-follower interactions. We propose a Gradient-based Hierarchical Learning and Optimization Algorithm (GHLOA) to achieve SE. At the leader-level, a Stochastic Gradient Ascent (SGA) algorithm optimizes the penetration path for SUAV, while in the followers-level, we demonstrate that the designed Hybrid Learning-based Multimodal Adaptive Pigeon-Inspired Optimization (HLMAPIO) algorithm converges with probability one to the suboptimal solution for each SD. Numerical results indicate that our approach is suboptimal, scalable, and fast adaptable to dynamical scenarios, and it outperforms the state-of-the-art techniques.