MIT Researchers' New Algorithm Slashes Aerocapture Fuel by 15% for Mars and Uranus
A robust sampling-based guidance method cuts delta-V needs by 5–15% in planetary aerocapture scenarios.
Deep Dive
A robust sampling-based covariance steering algorithm for aerocapture guidance was developed. It leverages sampled nonlinear system trajectories to improve evaluation of delta-V and address nonlinearities from dynamics and atmospheric disturbances. In Monte Carlo simulations for Mars and Uranus, the algorithm reduced the 99th‑percentile, 99.7th‑percentile, and worst‑case delta‑V by 5–15% compared to a state‑of‑the‑art covariance steering algorithm.
Key Points
- Uses sampled nonlinear trajectories to improve delta‑V estimation under atmospheric uncertainty
- Achieves 5–15% reduction in worst‑case and high‑percentile delta‑V for Mars and Uranus aerocapture
- Outperforms state‑of‑the‑art covariance steering in Monte Carlo simulations with dispersed entry conditions
Why It Matters
Cutting fuel for orbital insertion enables faster interplanetary travel and heavier payloads for deep‑space missions.