Deep-space missions to icy moons like Europa require AI systems capable of surviving extreme radiation (150 krad/year), operating autonomously under hour-long communication delays, and enforcing planetary protection protocols. This study introduces an integrated framework combining 1) radiation-hardened hybrid circuits reducing hardware failures by 61.6% (Formula: see text) and single-event upsets by 85.5% under simulated Jovian radiation, 2) hierarchical reinforcement learning reducing Earth dependence by 40% and mission planning time by 50%, and 3) context-aware ethical protocols enforcing COSPAR Category IVc contamination limits (Formula: see text), reducing risks by 78% while maintaining 81% data efficiency. Validated against NASA’s Europa Clipper parameters and Mars rover analogs, the framework demonstrates scalability to Titan and exoplanet missions. Despite 133% higher unit costs, radiation-hardened components require 65% fewer replacements over 5-year missions. This work bridges critical gaps in deep-space AI, enabling sustainable exploration of ocean worlds.
Sheikder et al. (Wed,) studied this question.