712. 特別編 Tsukamoto 05.21.26

Interstellar object interception demands autonomous, real-time decision-making to overcome the extreme velocity and positional uncertainty inherent in objects originating outside the solar system. Hiroyasu Tsukamoto, a professor at the University of Illinois, applies control theory and machine learning to develop adaptive systems capable of managing these challenges without relying on ground-based computation. Beyond space exploration, this research extends to robotics, where collision-tolerant ornithopters mimic biological flight to navigate complex environments efficiently. These advancements highlight the necessity of creating interpretable, safety-critical AI frameworks that can handle unstructured uncertainty. As autonomous systems evolve, the focus shifts toward designing robust, multi-purpose spacecraft architectures that can adapt to unknown mission parameters, ultimately enhancing the sustainability and feasibility of deep-space exploration while addressing the ethical responsibilities of deploying advanced, embodied AI.