Risk-Aware Aerocapture Guidance Through a Probabilistic Indicator Function

摘要

Aerocapture is sensitive to trajectory errors, particularly for low-cost missions with imprecise navigation. For such missions, considering the probability of each failure mode when computing guidance commands can increase capture rate. A risk-aware aerocapture guidance algorithm is proposed that uses a generative model-based probabilistic indicator function to estimate escape, impact, or capture probabilities. The probability of each mode is incorporated into corrective guidance commands to increase the likelihood of successful capture. The proposed method is evaluated against state-of-the-art numeric predictor-corrector guidance algorithms in high-uncertainty scenarios where entry interface dispersions lead to nontrivial failure probabilities. When using a probabilistic indicator function in guidance, 71.43% to 100% of recoverable cases are saved for a variety of initial distributions and atmosphere models. The probabilistic indicator function is capable of predicting failure probability for dispersions and atmosphere models outside its training data, showing generalizability. In addition, the probabilistic indicator is compared to a fading memory filter for density estimation, demonstrating improvements in accuracy when both are used in conjunction. The proposed risk-aware aerocapture guidance algorithm improves capture performance and robustness to entry interface state dispersions, especially for missions with high navigation uncertainty.