国际小行星探测任务从初期的近距飞越探测，发展到环绕着陆探测，再到采样与巡视探测。小行星附近轨道设计和运动控制存在如下难点：首先，小行星附近引力场复杂而不规则，同时受到太阳光压等多种摄动力影响，动力学环境十分复杂，因此，针对规则形状天体的开普勒轨道设计理论以及摄动理论不适用于小行星附近的轨道精确设计；其次，小行星物理参数缺乏先验信息，不同小行星表面性质差异巨大，或为坚硬地表，或为松散砂砾，具有很大的不确定性，给着陆与巡视探测提出了巨大挑战；第三，小行星引力十分微弱，探测器着陆、锚定、巡视等控制更加困难；第四，小行星与地球距离遥远存在通信延时，地面无法对探测器进行实时监视与操控。因此，高适应性、高自主性、高可靠性、高拓展性的轨道设计与运动控制成为小行星探测迫切需要解决的关键问题。借助近年来飞速发展的人工智能，特别是深度学习，是解决这个关键问题的一种可能方法，值得深入研究。

Asteroid exploration will be the spotlight of the research in aerospace science in upcoming decade. Various missions, such as close flyby, orbiting, landing, driving and sampling were proposed by major space agencies. The difficulty of the orbit design and motion control in the vicinity of asteroids at least comes from the following four aspects. First, the complex nature of this type of problems. Asteroids are diverse in shapes, chemical composition and interior structures, and accordingly the gravitational field around them could be irregular and very different for each object. The orbital behavior around asteroids may differ significantly from that around the planets. Second, due to the limitation of measurements, spacecraft cannot obtain the complex information before approaching the target asteroid, e.g., the surface properties, the shape model. It has to repeat measurements and corrections simultaneously during the approaching, which increases the risk of near-field flight. Third, the low-gravity conditions of asteroids make traditional landing, anchoring and driving all the more hazardous. Finally, in deep space, it is hard to navigate, guide and control a spacecraft on a real-time basis remotely from the earth mainly due to the communication delay. Therefore, the intelligent control of a lander with high adaptability, high autonomy, high reliability and high extendibility is required for asteroid exploration. Artificial intelligence, especially deep learning which has profoundly change human social life and the world, is a possible solution to this crucial issue and deserves further study.