Entry guidance for spatial no-fly zones avoidance via model-based reinforcement learning

This paper proposes a novel guidance law for hypersonic entry vehicles, considering no-fly zones with height limits. Traditional planar assumptions restrict the flexibility of trajectory design for scenarios like radar avoidance. Besides, the numerical integration proves inefficient for long-term prediction and avoidance. Therefore, a model-based reinforcement learning policy is designed. It offline learns entry dynamics in advance and onboard plans a feasible trajectory. The planner's state includes flight status and no-fly zones; action presents waypoints; and reward ensures constraint while maximizing terminal precision. Then, analytical prediction converts spatial no-fly zone constraints to flight-path angle constraints, improving precision compared to traditional one-step estimates. Finally, the two parts are assembled into the predictor-corrector framework, which gives the augmented guidance commands. While retaining its robustness to bias, our method reduces online optimization calculations and outperforms constraint satisfaction. Experiments show that the model-based method reduces 60% training in offline training compared with proximal policy optimization. Besides, our method is 80% faster than conventional predictor-corrector guidance regarding online computation speed.