Risk-Aware Aerocapture Guidance

• The paper introduces a risk-aware aerocapture guidance algorithm that explicitly accounts for uncertainty in atmospheric density, entry conditions, and control response to mitigate failure modes.
• It defines robust design strategies such as corridor structure and optimal entry-angle selection to maintain vehicle performance across variable planetary atmospheres.
• It compares online control architectures—including bank-angle and drag modulation, probabilistic methods, and covariance steering—to enhance capture reliability and minimize ΔV penalties.

Searching arXiv for papers on risk-aware aerocapture guidance and related aerocapture uncertainty/guidance methods. Risk-aware aerocapture guidance algorithms are guidance formulations for atmospheric orbit insertion that explicitly account for uncertainty in entry conditions, atmospheric density, aerodynamic response, and control authority while targeting a captured exit state. In aerocapture, atmospheric drag is used to reduce orbital energy during a single pass, after which the vehicle exits the atmosphere on a bound trajectory and typically performs a periapsis raise to the desired science orbit. The central technical problem is that aerodynamic deceleration, heating, dynamic pressure, and energy dissipation scale directly with atmospheric density, so uncertainty in the density profile can shift the vehicle toward skip-out, over-deceleration, overheating, or an incorrect exit apoapsis. Across recent work, risk-aware guidance appears in several complementary forms: robust entry flight-path angle selection under bounded density envelopes (Girija, 2023), continuous bank-angle or drag-modulation control adapted to atmospheric anomalies (Girija, 2023), probabilistic failure-mode-aware predictor-corrector guidance (Calkins et al., 7 Jul 2025), augmented bank-angle modulation with angle-of-attack control (Sonandres et al., 12 Mar 2025, Sonandres et al., 9 May 2025), guidance that incorporates attitude-kinematics and radiative-heating structure (Zucchelli et al., 2024), and covariance-steering formulations that optimize high-percentile post-pass $\Delta V$ under stochastic uncertainty (Rose et al., 12 Jun 2026).

1. Atmospheric uncertainty as the dominant risk driver

Aerocapture outcomes are governed primarily by atmospheric density at the altitudes relevant for the atmospheric pass because drag, deceleration, heating, and energy dissipation scale directly with density (Girija, 2023). The comparative uncertainty study reports the following $3\sigma$ density variations in aerocapture-relevant altitude bands: Venus at $100$–$120\ \mathrm{km}$, $\pm 30\%$; Mars at $50$–$80\ \mathrm{km}$, $\pm 50\%$; Titan at $300$–$450\ \mathrm{km}$, $3\sigma$0; Uranus at $3\sigma$1–$3\sigma$2 above $3\sigma$3-bar, GRAM $3\sigma$4; and Neptune at $3\sigma$5–$3\sigma$6 above $3\sigma$7-bar, GRAM $3\sigma$8 (Girija, 2023). For Uranus and Neptune, those GRAM values are identified as optimistic, and a more conservative envelope is recommended until in-situ probe data exist: $3\sigma$9 in the $100$0–$100$1 region (Girija, 2023).

The canonical relations used to map density uncertainty into trajectory risk are standard. Atmospheric density may be written as $100$2 or as $100$3 over a bounded uncertainty set (Girija, 2023). Dynamic pressure is $100$4, drag and lift are $100$5 and $100$6, ballistic coefficient is $100$7, and drag deceleration is $100$8 (Girija, 2023). Specific orbital energy is $100$9 with $120\ \mathrm{km}$0, so the total energy loss through the pass, $120\ \mathrm{km}$1, inherits the density dependence through $120\ \mathrm{km}$2 (Girija, 2023). For heating checks, a standard stagnation convective scaling such as Sutton–Graves, $120\ \mathrm{km}$3, is commonly invoked even when not explicitly part of the original guidance law (Girija, 2023).

These dependencies define the principal failure modes. If the atmosphere is thinner than expected or the entry flight-path angle is too shallow, integrated drag is insufficient and the vehicle exits hyperbolic, producing skip-out (Girija, 2023). If the atmosphere is thicker than expected or the entry is too steep, deceleration and heating rise, which can induce undershoot, failure to exit, or constraint violations (Girija, 2023). Even when capture occurs, density perturbations bias the drag integral and hence the post-exit apoapsis energy, producing exit-state targeting error unless the onboard guidance compensates (Girija, 2023). A plausible implication is that risk-aware guidance is not a single algorithmic family so much as a set of mechanisms for allocating margin against these density-driven mode transitions.

2. Corridor structure, entry-angle selection, and bounded-uncertainty robustness

A standard geometric framework for aerocapture robustness is the Theoretical Corridor Width (TCW), bounded by overshoot and undershoot limits and further restricted by maximum deceleration and maximum heat-rate constraints (Girija, 2023). Within this framing, the entry flight-path angle, $120\ \mathrm{km}$4, is selected so that the vehicle remains within a feasible capture corridor despite atmospheric uncertainty and delivery dispersion (Girija, 2023).

A practical graphical method is reported for selecting $120\ \mathrm{km}$5 under uncertainty (Girija, 2023). Three capture-corridor boxes are constructed on a $120\ \mathrm{km}$6 axis, one each for minimum, average, and maximum density profiles. Each box is bounded above by the overshoot limit and below by the undershoot limit. As the atmosphere becomes thicker, the corridor shifts shallower; as it becomes thinner, it shifts steeper (Girija, 2023). Delivery error is then overlaid; the cited Uranus example uses $120\ \mathrm{km}$7 (Girija, 2023). The selection rule is to choose $120\ \mathrm{km}$8 such that, when the density envelope is considered, both limiting cases—shallow entry with thin atmosphere and steep entry with thick atmosphere—remain within the union of the corridor boxes with additional margin (Girija, 2023). This can be formalized as

$120\ \mathrm{km}$9

where $\pm 30\%$0 is a margin function to the skip boundary, the heat-rate limit, and the deceleration limit (Girija, 2023).

The comparative study reports substantial planet-to-planet variation in corridor width (Girija, 2023). Mars has density uncertainty of $\pm 30\%$1 in the $\pm 30\%$2–$\pm 30\%$3 range, yet its low gravity and extended atmosphere yield a TCW about $\pm 30\%$4 that of Venus, making robustness more achievable (Girija, 2023). Titan has a very large TCW and $\pm 30\%$5 density uncertainty that is described as manageable (Girija, 2023). Venus combines $\pm 30\%$6 uncertainty with a higher-heating environment, and drag-modulation concepts are noted as favorable there to reduce heating risk (Girija, 2023). By contrast, the ice giants combine narrow TCW, entry speeds of about $\pm 30\%$7, and large possible density excursions, making robust $\pm 30\%$8 selection and onboard density estimation critical (Girija, 2023). In the Uranus lifting-aeroshell example with $\pm 30\%$9 and entry speed $50$0, the corridor width is only $50$1 (Girija, 2023).

When the corridor cannot span the full uncertainty set, especially for Uranus or Neptune, the recommended bias is toward the steep side to reduce skip-out risk, while accepting a small undershoot risk that onboard guidance can mitigate, provided that heating and deceleration constraints remain within limits (Girija, 2023). This steep-bias rule reappears in later ice-giant guidance studies as a structural risk-management choice rather than a purely nominal optimum.

3. Online guidance architectures: bank modulation, drag modulation, and density-aware targeting

Once a robust initial entry state is selected, online guidance uses the available control authority to regulate energy depletion and exit-state targeting. Two principal control architectures are compared for ice-giant aerocapture: lift modulation by bank-angle control and drag modulation by discrete jettison (Girija, 2023).

Lift modulation uses bank angle $50$2 as a continuous control variable. Lift-up, $50$3, reduces descent rate and penetration; lift-down, $50$4, increases descent and penetration; bank reversals adjust energy depletion and the exit state (Girija, 2023). The cited Uranus architecture is an MSL-like rigid aeroshell with $50$5, Uranus entry speed $50$6, and target apoapsis $50$7 (Girija, 2023). Drag modulation instead uses a single-event jettison to change ballistic coefficient; the Uranus example is a $50$8-m ADEPT vehicle with $50$9, $80\ \mathrm{km}$0-ratio $80\ \mathrm{km}$1, Uranus entry speed $80\ \mathrm{km}$2, and target apoapsis $80\ \mathrm{km}$3 (Girija, 2023).

The trade differs sharply in robustness structure. Lift modulation provides nearly twice the entry corridor width as drag modulation at Uranus: approximately $80\ \mathrm{km}$4 versus $80\ \mathrm{km}$5 (Girija, 2023). Continuous control throughout flight allows lift modulation to adjust the trajectory in response to the actual density profile encountered, including unexpected density pockets (Girija, 2023). Drag modulation offers much more benign aero-thermal conditions, with peak heat rate $80\ \mathrm{km}$6–$80\ \mathrm{km}$7 and heat load $80\ \mathrm{km}$8–$80\ \mathrm{km}$9, compared with lift modulation’s peak heat rate $\pm 50\%$0–$\pm 50\%$1 and heat load $\pm 50\%$2–$\pm 50\%$3 in the Uranus cases reported (Girija, 2023). However, drag modulation loses control authority after jettison, making the exit state more sensitive to density variations encountered after that event (Girija, 2023).

The same study recommends density-aware onboard estimation. States may include $\pm 50\%$4, where $\pm 50\%$5 is a multiplicative density-scale factor relative to the nominal atmospheric model (Girija, 2023). IMU accelerations provide along-track deceleration and normal acceleration, enabling estimation of $\pm 50\%$6 by an EKF or UKF with a small random-walk process model (Girija, 2023). Dynamic pressure, heating, and predicted exit energy can then be updated online using the estimated density scale (Girija, 2023). For lift modulation, a risk-aware PID form is proposed in which the bank angle command is

$\pm 50\%$7

with $\pm 50\%$8 defined against a dynamic-pressure, altitude-rate, or specific-energy reference, and with lift-up or lift-down polarity switched for corridor threading or energy shaping (Girija, 2023). For drag modulation, robust jettison policies are suggested using dynamic-pressure windows, integrated heat load, or estimated exit energy as trigger logic, explicitly to reduce post-jettison sensitivity to density anomalies (Girija, 2023).

At the mission-analysis level, these architectures support a general offline/online decomposition already articulated in the comparative uncertainty paper: precompute corridor and constraint boundaries for the uncertainty set, select a robust $\pm 50\%$9, define exit target bands, estimate density and scale height in flight, and modulate bank or drag state to track drag integral or energy rate toward the target exit energy (Girija, 2023). This suggests that “risk-aware” in classical aerocapture guidance primarily means coupling conservative offline corridor design with online density adaptation and control authority allocation.

4. Probabilistic and learning-based failure-mode-aware guidance

A more explicit probabilistic formulation appears in the probabilistic-indicator guidance method denoted $300$0PAG (Calkins et al., 7 Jul 2025). In that framework, guidance does not merely track an energy target; it estimates the probabilities of capture, escape, and impact and uses those probabilities to bias guidance commands away from failure modes (Calkins et al., 7 Jul 2025).

The truth model uses $300$1-DOF equations of motion over an oblate, rotating planet with bank angle as the only control and a first-order actuator with bank rate constrained to $300$2 (Calkins et al., 7 Jul 2025). Aerodynamic accelerations are

$300$3

with $300$4 and $300$5 (Calkins et al., 7 Jul 2025). The atmosphere is modeled using UranusGRAM for truth and a polynomial rational fit for the onboard model (Calkins et al., 7 Jul 2025). Entry-interface dispersions are applied in $300$6, mass, altitude, longitude, latitude, velocity, and flight-path angle, with scenario cases constructed near the escape boundary and near the impact boundary (Calkins et al., 7 Jul 2025).

The central probabilistic object is a Gaussian Mixture Variational Autoencoder (GMVAE) trained on downsampled and normalized specific-energy time histories (Calkins et al., 7 Jul 2025). Inputs are energy trajectories reduced to $300$7 time points; outputs are cluster-membership probabilities, which are mapped to mode probabilities $300$8, $300$9, and $450\ \mathrm{km}$0 (Calkins et al., 7 Jul 2025). The best reported GMVAE configurations are latent dimension $450\ \mathrm{km}$1, clusters $450\ \mathrm{km}$2 for the near-impact case with average misclassification $450\ \mathrm{km}$3, and $450\ \mathrm{km}$4, $450\ \mathrm{km}$5 for the near-escape case with average misclassification $450\ \mathrm{km}$6 (Calkins et al., 7 Jul 2025).

The baseline guidance is FNPAG, a fully numeric predictor-corrector aerocapture guidance that uses a bang-bang bank profile and root finding for apoapsis targeting (Calkins et al., 7 Jul 2025). The risk-aware augmentation applies threshold logic: if $450\ \mathrm{km}$7 or $450\ \mathrm{km}$8, then either the guidance is forced from Phase 1 to Phase 2 or the bank command is corrected by a fixed magnitude $450\ \mathrm{km}$9 in the direction that reduces the current failure risk (Calkins et al., 7 Jul 2025). A persistence window $3\sigma$00 prevents oscillatory bang-off-bang behavior (Calkins et al., 7 Jul 2025). Example tuned values are $3\sigma$01, $3\sigma$02, $3\sigma$03, $3\sigma$04 for the near-escape scenario, and $3\sigma$05, $3\sigma$06 for the near-impact scenario (Calkins et al., 7 Jul 2025).

In Monte Carlo with $3\sigma$07, the probabilistic indicator improved capture and recoverability in high-uncertainty cases (Calkins et al., 7 Jul 2025). In the near-escape scenario with a fading-memory density filter, FNPAG achieved capture $3\sigma$08, escape $3\sigma$09, mean capture apoapsis error $3\sigma$10, and standard deviation $3\sigma$11, while $3\sigma$12PAG achieved capture $3\sigma$13, escape $3\sigma$14, recoverable save $3\sigma$15, mean error $3\sigma$16, and standard deviation $3\sigma$17 (Calkins et al., 7 Jul 2025). In the near-impact scenario, FNPAG achieved capture $3\sigma$18 and impact $3\sigma$19, while $3\sigma$20PAG achieved capture $3\sigma$21, impact $3\sigma$22, and recoverable save $3\sigma$23 (Calkins et al., 7 Jul 2025). The method also improved performance relative to fading-memory density estimation alone, indicating that failure-probability-aware guidance and density estimation address different parts of the robustness problem (Calkins et al., 7 Jul 2025).

This probabilistic approach is distinct from bounded-corridor design. Rather than selecting margins against worst-case envelopes alone, it estimates mode probabilities directly from the evolving trajectory and then applies discrete corrective bias. A plausible implication is that such methods are particularly relevant for low-cost missions with imprecise navigation, where the local failure probability is nontrivial even when nominal tracking remains acceptable.

5. Multi-input and thermally informed risk-aware guidance

Risk-aware aerocapture guidance can also be formulated by enlarging the control space. Augmented bank angle modulation (ABAM) introduces angle of attack $3\sigma$24 alongside bank angle $3\sigma$25 as control inputs, allowing direct modulation of both lift and drag (Sonandres et al., 12 Mar 2025, Sonandres et al., 9 May 2025). The longitudinal dynamics used for the optimal-control derivation are

$3\sigma$26

with $3\sigma$27 (Sonandres et al., 9 May 2025). Both linear and quadratic aerodynamic coefficient models in $3\sigma$28 are developed, and Pontryagin’s Minimum Principle yields bang-bang or unsaturated optimal control profiles depending on the aerodynamic model (Sonandres et al., 9 May 2025).

ABAMGuid and its later extension ABAMGuid+ mimic the structure of these optimal solutions without solving the full optimal control problem online (Sonandres et al., 12 Mar 2025, Sonandres et al., 9 May 2025). ABAMGuid uses a four-phase predictor-corrector structure with up to three switching times for $3\sigma$29 and $3\sigma$30, estimated by Nelder–Mead and Newton–Raphson over an apoapsis- or exit-velocity-targeting objective (Sonandres et al., 12 Mar 2025). ABAMGuid+ retains the four-phase structure but introduces Continuous Alpha-Sigma Modulation (CASM) in the terminal phase, using line-search bracketing and Brent’s method to compute coupled unsaturated $3\sigma$31 commands for exit-velocity targeting (Sonandres et al., 9 May 2025). Exit-velocity targeting is used because it remains well-defined near hyperbolic or ballistic outcomes where apoapsis can become ill-conditioned or undefined (Sonandres et al., 9 May 2025).

The Uranus high-fidelity simulations reported for ABAMGuid+ use $3\sigma$32-DoF nonlinear dynamics at $3\sigma$33, guidance at $3\sigma$34, Uranus-GRAM atmosphere, $3\sigma$35 gravity, vehicle mass $3\sigma$36, $3\sigma$37 $3\sigma$38, bank-angle limits $3\sigma$39, angle-of-attack limits $3\sigma$40, and rate limits $3\sigma$41 and $3\sigma$42 (Sonandres et al., 9 May 2025). The target orbit has apoapsis altitude $3\sigma$43 and periapsis altitude $3\sigma$44 (Sonandres et al., 9 May 2025). Under dispersed atmosphere and perfect sensors, ABAMGuid+ achieved mean $3\sigma$45 $3\sigma$46, $3\sigma$47 capture, and $3\sigma$48th-percentile $3\sigma$49 $3\sigma$50 in the baseline entry set, compared with $3\sigma$51 and $3\sigma$52 for ABAMGuid and $3\sigma$53 and $3\sigma$54 for FNPAG (Sonandres et al., 9 May 2025). The same paper states that ABAMGuid+ reduces $3\sigma$55 $3\sigma$56 from $3\sigma$57 to $3\sigma$58 relative to ABAMGuid in that case, a reduction of about $3\sigma$59, and reduces the $3\sigma$60th percentile by about $3\sigma$61 (Sonandres et al., 9 May 2025). Under atmosphere dispersion and EKF navigation errors, ABAMGuid+ reduces mean $3\sigma$62 by up to about $3\sigma$63 versus FNPAG and reduces the $3\sigma$64th percentile by about $3\sigma$65 in the baseline set (Sonandres et al., 9 May 2025).

Thermal-risk-aware guidance appears in a different but related form in work on minimum radiative heat and propellant with attitude-kinematics constraints (Zucchelli et al., 2024). That study proves that the same single-switch bang-bang trajectory that minimizes final $3\sigma$66 also minimizes integrated shock-layer radiative heat load for a broad class of heating models, and that it starts with lift up (Zucchelli et al., 2024). It further shows that, for many convective heating formulations, the same trajectory instead maximizes convective heat load (Zucchelli et al., 2024). The practical guidance derived from that result, denoted OAK, plans a bang-bang trajectory while incorporating bank-rate and bank-acceleration constraints in prediction (Zucchelli et al., 2024). The cited constraints are $3\sigma$67 and $3\sigma$68, with a representative $3\sigma$69 bank rotation taking about $3\sigma$70 and dissipating more than $3\sigma$71–$3\sigma$72 of the total required energy change during aerocapture if executed during the pass (Zucchelli et al., 2024). This is a risk-aware result in a different sense: it shows that ignoring attitude kinematics can distort both propellant and thermal predictions enough to undermine robustness.

Taken together, ABAM-based guidance and thermally informed bang-bang guidance indicate that risk awareness need not be limited to atmosphere estimation; it can also arise from richer control authority, better-posed terminal objectives, and more faithful modeling of the attitude dynamics and heating physics that shape the feasible set.

6. Stochastic tail-risk optimization and performance propagation

A more formal stochastic risk formulation is provided by robust sampling-based covariance steering for aerocapture guidance (Rose et al., 12 Jun 2026). In that setting, risk is defined directly on the post-exit cleanup cost rather than only on capture feasibility. The total deterministic cleanup cost is $3\sigma$73, where the first burn raises periapsis and the second, if needed, corrects apoapsis to the mission target orbit (Rose et al., 12 Jun 2026). The objective is to minimize a high-percentile statistic of this $3\sigma$74, specifically the $3\sigma$75th or $3\sigma$76th percentile under uncertain entry-state dispersion and uncertain atmosphere (Rose et al., 12 Jun 2026).

The longitudinal dynamics are

$3\sigma$77

with control parameterized as $3\sigma$78 and bounded within a prescribed interval (Rose et al., 12 Jun 2026). Entry-state uncertainty is modeled as $3\sigma$79, while atmospheric density variability is modeled as a state-dependent Gaussian random field (Rose et al., 12 Jun 2026). The algorithm linearizes about a nominal trajectory, designs an affine state-feedback policy, and enforces chance constraints on the control via a convex second-order-cone reformulation (Rose et al., 12 Jun 2026).

The distinguishing feature is the robust sampling-based objective. Rather than relying only on a single linearized mean trajectory, the method propagates $3\sigma$80 sigma points on the $3\sigma$81 contours of the initial Gaussian, rolls them out under the nonlinear dynamics, fits a disturbance realization for each, and then builds a percentile surrogate for $3\sigma$82 about each sigma trajectory (Rose et al., 12 Jun 2026). The convex subproblem minimizes the maximum of these surrogates over the sigma set, thereby targeting the worst high-percentile behavior captured by the nonlinear samples (Rose et al., 12 Jun 2026).

Monte Carlo performance is reported for Mars and Uranus against a state-of-the-art covariance-steering baseline (Rose et al., 12 Jun 2026). For Mars with small initial dispersion, the proposed method reduced the $3\sigma$83th percentile from $3\sigma$84 to $3\sigma$85, the $3\sigma$86th percentile from $3\sigma$87 to $3\sigma$88, and the worst case from $3\sigma$89 to $3\sigma$90, corresponding to reductions of $3\sigma$91, $3\sigma$92, and $3\sigma$93 (Rose et al., 12 Jun 2026). For Mars with larger velocity dispersion, the reductions were $3\sigma$94 at the $3\sigma$95th percentile, $3\sigma$96 at the $3\sigma$97th percentile, and $3\sigma$98 in the worst case (Rose et al., 12 Jun 2026). For Uranus, the algorithm reduced the $3\sigma$99th percentile from $100$00 to $100$01, the $100$02th percentile from $100$03 to $100$04, and the maximum from $100$05 to $100$06, corresponding to $100$07, $100$08, and $100$09 reductions (Rose et al., 12 Jun 2026). The key observation is that mean or median $100$10 remains similar while the difficult tail cases improve, indicating an explicitly risk-focused benefit (Rose et al., 12 Jun 2026).

Risk-aware performance analysis can also be accelerated by directional state transition tensors (DSTTs) tailored to aerocapture (Calkins et al., 13 Dec 2025). That work develops time-varying, higher-order sensitive bases using augmented higher-order Cauchy–Green tensors, selective tensors for chosen state subsets, and quantity-of-interest tensors for energy or apoapsis (Calkins et al., 13 Dec 2025). For a seven-state aerocapture model, a second-order DSTT with latent dimension $100$11 reduces the higher-order representation from $100$12 coefficients to $100$13 while preserving useful accuracy, especially near peak dynamic pressure when higher-order effects matter most (Calkins et al., 13 Dec 2025). The reported result is that $100$14-qDSTTs and sDSTTs reduce terminal energy prediction error by approximately an order of magnitude relative to original DSTTs based on linear-CGT directions (Calkins et al., 13 Dec 2025). This suggests a pathway toward real-time semi-analytical risk assessment inside onboard guidance loops, although the paper is framed as performance analysis rather than a deployed flight algorithm.

7. Limitations, mission dependence, and open technical issues

Risk-aware aerocapture guidance remains strongly mission-dependent because the relevant uncertainties, thermal environment, and available control authority differ substantially across planets, vehicles, and architecture choices. For Mars, a deployable, aperture-modulated heat shield study shows that atmospheric density and ballistic-coefficient uncertainty strongly narrow the feasible solution space, yet viable solutions still exist even in worst conditions (Isoletta et al., 2021). That work uses drag-area modulation through $100$15, with $100$16 and a nominal Mars ballistic coefficient of $100$17 with uncertainty $100$18 (Isoletta et al., 2021). The combined density and $100$19 uncertainty case reported that capture remains achievable only for $100$20 up to about $100$21 and $100$22 under $100$23 density and $100$24 uncertainty, while under $100$25 density viable captures persist only for $100$26–$100$27 and $100$28 (Isoletta et al., 2021). This is consistent with the general principle that robust operating regions shrink quickly as bounded uncertainty grows.

Several limitations recur across the literature. Many guidance laws do not enforce thermal or structural path constraints directly in the optimal control problem, even when they monitor them or derive robust entry conditions against them (Sonandres et al., 12 Mar 2025, Sonandres et al., 9 May 2025). Some algorithms remain longitudinal only and rely on separate lateral logic, so interactions between risk correction and plane targeting can become pathological, especially when bank reversals occur during corrective maneuvers (Calkins et al., 7 Jul 2025). Some methods assume constant $100$29 or ballistic coefficient, neglect winds or horizontal density gradients, or linearize about a nominal trajectory sufficiently aggressively that good initialization and trust-region management are essential (Rose et al., 12 Jun 2026). For Uranus and Neptune, the largest unresolved issue remains the lack of in-situ atmospheric data; until probe data become available, the conservative FMINMAX envelope rather than the nominal GRAM $100$30 estimate is recommended for pre-flight design and robustness analysis (Girija, 2023).

There is also an unresolved methodological tension between bounded-uncertainty and probabilistic formulations. Corridor-based methods and conservative EFPA rules are transparent and directly tied to skip and undershoot margins (Girija, 2023). Probabilistic indicator methods can save recoverable failures in high-dispersion scenarios and improve apoapsis error even when density filters are already in use (Calkins et al., 7 Jul 2025). Covariance steering directly optimizes tail-risk in post-pass $100$31 (Rose et al., 12 Jun 2026). ABAM-based methods improve authority and empirical robustness without introducing explicit chance constraints (Sonandres et al., 9 May 2025). This suggests that the field does not yet converge on a single universal definition of “risk-aware”; rather, the term encompasses several strategies for shaping trajectories and controls around uncertain capture dynamics.

A plausible synthesis is that a modern risk-aware aerocapture guidance algorithm has four layers. First, pre-flight design should use atmosphere-specific uncertainty sets, with conservative min–max envelopes for poorly characterized bodies (Girija, 2023). Second, entry conditions should be selected by maximizing worst-case corridor margin while accounting for delivery error (Girija, 2023). Third, onboard guidance should estimate atmospheric density mismatch and modulate available control authority—bank, drag state, or both—to track energy removal or exit-state objectives (Girija, 2023, Sonandres et al., 9 May 2025). Fourth, when navigation uncertainty is high or the failure set is genuinely multimodal, probability-aware or tail-risk-aware decision layers can be added to existing predictor-corrector loops to bias commands away from escape, impact, or high-$100$32 outcomes (Calkins et al., 7 Jul 2025, Rose et al., 12 Jun 2026).