Risk-Aware Aerocapture Guidance Through a Probabilistic Indicator Function

Abstract: 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 performance. A risk-aware aerocapture guidance algorithm is proposed that uses a generative-modeling-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, 69% to 100% of recoverable cases are saved in near-escape and near-impact scenarios. In addition, the probabilistic indicator is compared to a first-order fading memory filter for density estimation, showing improvements in apoapsis error even when a fading filter is included. The probabilistic indicator function can also accurately predict failure probability for dispersions outside its training data, showing generalizability. The proposed risk-aware aerocapture guidance algorithm improves capture performance and robustness to entry interface state dispersions, especially for missions with high navigation uncertainty.