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This research proposes a new integrated framework for identifying safelanding locations and planning in-flight divert maneuvers. The state-of-the-artalgorithms for landing zone selection utilize local terrain features such asslopes and roughness to judge the safety and priority of the landing point.However, when there are additional chances of observation and diverting in thefuture, these algorithms are not able to evaluate the safety of the decisionitself to target the selected landing point considering the overall descenttrajectory. In response to this challenge, we propose a reinforcement learningframework that optimizes a landing site selection strategy concurrently with aguidance and control strategy to the target landing site. The trained agentcould evaluate and select landing sites with explicit consideration of theterrain features, quality of future observations, and control to achieve a safeand efficient landing trajectory at a system-level. The proposed framework wasable to achieve 94.8 $\%$ of successful landing in highly challenging landingsites where over 80$\%$ of the area around the initial target lading point ishazardous, by effectively updating the target landing site and feedback controlgain during descent.

Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers