Game-Theoretical MPC for Quadrotor Pursuit: Strategic Anticipation and Efficient Capture

This paper proposes a pursuit-centric Game-Theoretical MPC (GT-MPC) framework for quadrotors, lever-aging Nash equilibrium to anticipate evader maneuvers and optimize pursuit strategies. Unlike conventional MPC or reinforcement learning (RL) methods, GT-MPC explicitly models bidirectional adversarial interactions, enabling 63.03% faster average capture times in symmetric pursuit scenarios. By focusing on strategic parity—where pursuer and evader share identical dynamics—we demonstrate that superior decision-making, not hardware advantages, can also drive capture efficiency. Simulations across 50 randomized trials validate GT-MPC's robustness, with a 93% success rate under perfect information, outperforming state-of-the-art baseline.