In this investigation, the integrated attitude and landing control scheme for quadruped robots in asteroid landing mission scenario is discussed. Compared with the gravitational field environment of the Earth, the gravitational field near most asteroids has a smaller gravitational acceleration, a non-negligible horizontal acceleration component, and obvious uneven distribution characteristics. In this study, an integrated control method is proposed which focuses on the challenging attitude and landing control schemes of quadruped robots near asteroids by using reinforcement learning in conjunction with an auto-tuned reward function. In the proposed method, attitude adjustment and landing control are trained as a whole. By relying on the trained controller, the quadruped robot can reorient automatically to the most suitable attitude for landing according to gravity and terrain information, thus only relying on the movement of mechanical legs (and not on any additional actuators like reaction wheels) during the entire process. To solve the problem of sparse reward in the process of multi-objective reinforcement learning, an autotuning method of a multi-objective reward function is proposed to improve the training speed. The effectiveness of the proposed landing control method of a quadruped robot is verified near irregular rod-shaped asteroid 216 Kleopatra. The numerical simulation results show that the quadruped robot can adjust reliably its attitude and finally land on irregular terrain without floating and escaping again, even when the gravitational acceleration is unknown and subject to large horizontal components.

在本次调查中，讨论了四足机器人在小行星着陆任务场景中的综合姿态和着陆控制方案。与地球的引力场环境相比，大多数小行星附近的引力场具有更小的重力加速度，水平加速度分量不可忽略，分布不均匀的特点明显。在这项研究中，提出了一种集成控制方法，该方法通过使用强化学习结合自动调整的奖励函数，重点关注小行星附近四足机器人具有挑战性的姿态和着陆控制方案。在所提出的方法中，姿态调整和着陆控制作为一个整体进行训练。依靠训练有素的控制器，四足机器人可以根据重力和地形信息自动重新定位到最适合着陆的姿态，因此整个过程仅依靠机械腿的运动（而不是任何额外的执行器，如反作用轮）。针对多目标强化学习过程中奖励稀疏的问题，提出了一种多目标奖励函数的自调整方法，以提高训练速度。在不规则杆状小行星 216 Kleopatra 附近验证了所提出的四足机器人着陆控制方法的有效性。数值仿真结果表明，四足机器人能够可靠地调整姿态，最终降落在不规则地形上，不会再次漂浮逃逸，