Adaptive Entry Guidance Under Complex Geographical Constraints via Modular RL Strategy and Model-Based Rules
Gaoxiang Peng, Bo Yuan Wang, Lei Liu, Huijin Fan · IEEE Transactions on Aerospace and Electronic Systems · 2025
Normal reinforcement learning (RL) methods for entry guidance face challenges in environmental design and generalization due to the uncertainty of geographic constraint types and distributions. This paper presents an adaptive guidance algorithm that integrates modular RL with model-based rules to address entry guidance problems subject to complex geographical constraints such as no-fly zones and waypoints. Firstly, a normal trajectory module is developed based on modular RL to generate reference commands online. Then, focusing on geographical constraints, model-based rules are designed by employing both the virtual target method and the geometric trajectory planning. The virtual target method guides the normal trajectory module to meet the requirements for maneuverability of geographical constraints and terminal target. The geometric trajectory planning designs arc trajectories to ensure precise navigation to waypoints and avoidance of no-fly zones. Moreover, a maximum lateral module is designed to constrain the control commands derived from arc trajectories, ensuring their feasibility, such as satisfaction of path constraints. By integrating model-based rules with RL modules, the proposed algorithm does not require training for specific geographic constraint scenarios while avoiding computationally intensive iterations, enabling direct, real-time adaptation to varying geographical constraints. Extensive simulations demonstrate the adaptability and robustness of the proposed algorithm under various geographical constraints with different types and distributions.