Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground

In this paper, a method for predicting the landing stability of lunar lander by classification map is proposed, considering soft soil characteristics and slope angle surface. First, condition in terms safe (=stable), sliding (=unstable), tip-over (=statically unstable) possibilities was checked dropping onto flat surfaces through finite-element (FE) simulation according to angle, friction coefficient, soft/rigid ground, while vertical touchdown velocity maintained at 3 m/s. All results were classified with aid logistic regression, machine-learning algorithm. Finally, status efficiently predicted Monte Carlo (MC) just referring 10,000 input datasets, consisting angles, rigid/soft ground. To demonstrate performance, two virtual employed based on 3D terrain LRO mission. Then, validated an FE model requiring high computation cost. The prediction showed excellent agreement those simulations negligible computational cost around few seconds.