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Deep reinforcement learning for six degree-of-freedom planetary landing
یادگیری تقویتی عمیق برای فرود سیاره ای شش درجه آزادی-2020 This work develops a deep reinforcement learning based approach for Six Degree-of-Freedom (DOF) planetary powered descent and
landing. Future Mars missions will require advanced guidance, navigation, and control algorithms for the powered descent phase to target
specific surface locations and achieve pinpoint accuracy (landing error ellipse <5 m radius). This requires both a navigation system
capable of estimating the lander’s state in real-time and a guidance and control system that can map the estimated lander state to a commanded
thrust for each lander engine. In this paper, we present a novel integrated guidance and control algorithm designed by applying
the principles of reinforcement learning theory. The latter is used to learn a policy mapping the lander’s estimated state directly to a
commanded thrust for each engine, resulting in accurate and almost fuel-optimal trajectories over a realistic deployment ellipse. Specifically,
we use proximal policy optimization, a policy gradient method, to learn the policy. Another contribution of this paper is the use of
different discount rates for terminal and shaping rewards, which significantly enhances optimization performance. We present simulation
results demonstrating the guidance and control system’s performance in a 6-DOF simulation environment and demonstrate robustness
to noise and system parameter uncertainty. Keywords: Reinforcement learning | Mars landing | Integrated guidance and control | Artificial intelligence | Autonomous maneuvers |
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