Efficient computing through AI will address the limitations of onboard processing power by developing lightweight algorithms tailored for deep space deployment.

The scientific objectives of this mission include studying interplanetary dust, the interstellar medium, Anomalous Cosmic Rays, and the 'hydrogen wall' at the solar system's edge.

China's initiative to integrate artificial intelligence (AI) into its solar system boundary mission marks a significant advancement in space exploration, reminiscent of historic missions such as Pioneer, Voyager, and New Horizons.

This ambitious mission will involve two spacecraft powered by radioisotope thermoelectric generators, which will utilize flybys of Jupiter and may visit other planets and Kuiper belt objects.

AI is being explored to tackle challenges inherent in deep space missions, such as navigating unknown environments, managing complex mission dynamics, and overcoming communication delays.

With its autonomous perception capabilities, AI will enable spacecraft to sense and model unknown environments, facilitating the detection of significant events like solar storms or asteroid impacts.

Additionally, AI can optimize data transmission by processing and compressing information, which minimizes errors and reduces the volume of data sent back to Earth.

The mission was officially organized by the China National Space Administration in 2020, with strategic plans further detailed in China's space white paper of 2021 and a long-term roadmap published in October 2024.

China aims to achieve a distance of 100 astronomical units (AU) by 2049 and ultimately reach 1,000 AU by the end of the century.

AI-driven navigation systems will allow for real-time trajectory adjustments, enhancing mission resilience while minimizing the need for Earth-based intervention.

By enhancing spacecraft autonomy through improved data processing, autonomous perception, and decision-making, AI will significantly reduce reliance on Earth-based control.

The findings related to these advancements were published in the Journal of Deep Space Exploration by researchers from the Beijing Institute of Technology, China’s Deep Space Exploration Laboratory, and the Shanghai Academy of Spaceflight Technology.