Is your feature request related to a problem? Please describe.
I'm always frustrated when our ground-to-space optical links drop entirely and crash the hardware due to environmental factors like fog, rain, or sandstorms. The current pipeline operates on the assumption of an uninterrupted, high-quality feed from the Shack-Hartmann Wavefront Sensor (SHWFS). When heavy atmospheric scattering or absorption occurs, the pipeline still blindly attempts to run the reconstruction math on corrupted frames. This results in the algorithm outputting mathematical garbage, which tries to force the deformable mirror into impossible geometries, leading to an unrecoverable hardware state and total loss of communication.
Describe the solution you'd like
I would like a Dynamic Signal Routing protocol implemented right before the wavefront reconstruction phase to evaluate frame quality and route processing through one of two fallbacks:
The AI Route (Soft Fallback for Mild Degradation):
If the system detects partial signal scattering (e.g., mild fog) where the Signal-to-Noise Ratio (SNR) drops but retains some structural integrity, the standard deterministic Zernike solver is paused. Instead, an AI agent is triggered to infer and accurately fill in the missing wavefront slopes (\Delta x, \Delta y) based on temporal consistency from past frames. To ensure this doesn't introduce latency into the control loop, the AI inference should be accelerated using custom CUDA kernels or TensorRT, allowing the corrected phase reconstruction \phi(r, \theta) to be seamlessly passed to the mirror.
The Hard Fallback Route (Critical System Override):
If the system detects a catastrophic signal drop (e.g., sandstorms or monsoons), it immediately aborts the optical pipeline. The deformable mirror actuators are parked at a neutral voltage (V = 0) to prevent mechanical strain or software crashes. The system then instantly executes a network handshake to switch the satellite connection to a low-frequency backup radio wave (RF) array, while the optical sensor enters a low-power atmospheric polling loop.
Describe alternatives you've considered
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Classical Mathematical Interpolation: I considered using standard bicubic or spline interpolation to fill in the missing data gaps from the sensor. However, classical methods fail to accurately model the complex, non-linear dynamics of atmospheric turbulence, making AI a much more robust solution for filling the gaps.
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Heavy PID Dampening: We could theoretically aggressively dampen the mirror's control loop so it ignores sudden spikes caused by bad data. However, this doesn't solve the root issue of ingesting corrupted Zernike polynomial coefficients, and would still eventually lead to a saturated state during prolonged bad weather.
Additional context
This hybrid fallback approach aligns with modern Adaptive Optics (AO) research. For scientific backing on the AI route, refer to recent SPIE and IEEE proceedings on "Deep Learning applied to Shack-Hartmann Wavefront Sensing" and "Atmospheric Turbulence Mitigation in Free-Space Optical (FSO) Communications." The primary mathematical challenge here will be ensuring the AI's output matrix strictly conforms to the expected actuator command space to avoid triggering the very crashes we are trying to prevent.
Is your feature request related to a problem? Please describe.
I'm always frustrated when our ground-to-space optical links drop entirely and crash the hardware due to environmental factors like fog, rain, or sandstorms. The current pipeline operates on the assumption of an uninterrupted, high-quality feed from the Shack-Hartmann Wavefront Sensor (SHWFS). When heavy atmospheric scattering or absorption occurs, the pipeline still blindly attempts to run the reconstruction math on corrupted frames. This results in the algorithm outputting mathematical garbage, which tries to force the deformable mirror into impossible geometries, leading to an unrecoverable hardware state and total loss of communication.
Describe the solution you'd like
I would like a Dynamic Signal Routing protocol implemented right before the wavefront reconstruction phase to evaluate frame quality and route processing through one of two fallbacks:
The AI Route (Soft Fallback for Mild Degradation):
If the system detects partial signal scattering (e.g., mild fog) where the Signal-to-Noise Ratio (SNR) drops but retains some structural integrity, the standard deterministic Zernike solver is paused. Instead, an AI agent is triggered to infer and accurately fill in the missing wavefront slopes (\Delta x, \Delta y) based on temporal consistency from past frames. To ensure this doesn't introduce latency into the control loop, the AI inference should be accelerated using custom CUDA kernels or TensorRT, allowing the corrected phase reconstruction \phi(r, \theta) to be seamlessly passed to the mirror.
The Hard Fallback Route (Critical System Override):
If the system detects a catastrophic signal drop (e.g., sandstorms or monsoons), it immediately aborts the optical pipeline. The deformable mirror actuators are parked at a neutral voltage (V = 0) to prevent mechanical strain or software crashes. The system then instantly executes a network handshake to switch the satellite connection to a low-frequency backup radio wave (RF) array, while the optical sensor enters a low-power atmospheric polling loop.
Describe alternatives you've considered
Classical Mathematical Interpolation: I considered using standard bicubic or spline interpolation to fill in the missing data gaps from the sensor. However, classical methods fail to accurately model the complex, non-linear dynamics of atmospheric turbulence, making AI a much more robust solution for filling the gaps.
Heavy PID Dampening: We could theoretically aggressively dampen the mirror's control loop so it ignores sudden spikes caused by bad data. However, this doesn't solve the root issue of ingesting corrupted Zernike polynomial coefficients, and would still eventually lead to a saturated state during prolonged bad weather.
Additional context
This hybrid fallback approach aligns with modern Adaptive Optics (AO) research. For scientific backing on the AI route, refer to recent SPIE and IEEE proceedings on "Deep Learning applied to Shack-Hartmann Wavefront Sensing" and "Atmospheric Turbulence Mitigation in Free-Space Optical (FSO) Communications." The primary mathematical challenge here will be ensuring the AI's output matrix strictly conforms to the expected actuator command space to avoid triggering the very crashes we are trying to prevent.