SCPL-TD3: An Intelligent Evasion Strategy for High-Speed UAVs in Coordinated Pursuit-Evasion

Highlights: What are the main findings? Proposes the SCPL-TD3 strategy to enable effective evasion for High-Speed UAVs against coordinated pursuers. Analyzes and classifies the impact of pursuer spacing on evasion difficulty levels. What is the implication of the main finding? Achieves superior evasion rates while minimizing resource costs, thereby preserving operational capability for subsequent missions. Provides a foundational framework and a critical decision-making metric for assessing evasion difficulty and optimizing vehicle trajectory in complex pursuit-evasion scenarios. The rapid advancement of kinetic pursuit technologies has significantly increased the difficulty of evasion for high-speed UAVs (HSUAVs), particularly in scenarios where two collaboratively operating pursuers approach from the same direction with optimized initial space intervals. This paper begins by deriving an optimal initial space interval to enhance cooperative pursuit effectiveness and introduces an evasion difficulty classification framework, thereby providing a structured approach for evaluating and optimizing evasion strategies. Based on this, an intelligent maneuver evasion strategy using semantic classification progressive learning with twin delayed deep deterministic policy gradient (SCPL-TD3) is proposed to address the challenging scenarios identified through the analysis. Training efficiency is enhanced by the proposed SCPL-TD3 algorithm through the employment of progressive learning to dynamically adjust training complexity and the integration of semantic classification to guide the learning process via meaningful state-action pattern recognition. Built upon the twin delayed deep deterministic policy gradient framework, the algorithm further enhances both stability and efficiency in complex environments. A specially designed reward function is incorporated to balance evasion performance with mission constraints, ensuring the fulfillment of HSUAV’s operational objectives. Simulation results demonstrate that the proposed approach significantly improves training stability and evasion effectiveness, achieving a 97.04% success rate and a 7.10–14.85% improvement in decision-making speed.