Role of redundancy in boosting the robustness in image analysis in the automatic alignment system at the National Ignition Facility

The first demonstration of indirect drive fusion in the National Ignition Facility (NIF) has ushered in a new era of laser-based fusion work. Before every experiment in NIF all 192 laser beams passing through various laser sub-sections must be aligned to the target chamber center within 30 minutes with a 50-micron accuracy at the target chamber center. NIF uses a CCD camera-based imaging system to identify the beam location; the laser beams are then aligned using motorized mirrors within a feedback control system. The success of the automatic alignment (AA) is contingent upon calculating the exact location of the laser beam in the CCD image of the beam. When an algorithm fails due to poor imaging, gradient illumination, or high noise content, the automated operation for the affected alignment loop stops, and the alignment enters a manual mode with operator intervention. The operator/shot director must decide if the imaging condition needs to be improved to successfully pass alignment or if the beam has to be dropped from participating in the shot thus reducing the total energy delivered to the target. To minimize such failures where a legitimate but challenging beam image is present, additional alternate algorithms are added to the original algorithm. When the first algorithm fails, this alternate algorithm is executed to process the image even though it may have a slightly higher uncertainty. Although the second algorithm may report a higher uncertainty, this system design allows the loop to continue to align the laser without operator intervention. The implication of this approach may be far-reaching, for example, in the application of driver-less cars, where the original algorithm developed by training on a large set of data, may sometimes fail to account for the presence of a pedestrian and lead to a fatal accident. Here we hypothesize that a back-up algorithm based on a second approach will minimize such risks in the real-world driver-less driving scenarios.