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نتیجه جستجو - Mars landing

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Adaptive guidance and integrated navigation with reinforcement meta-learning
راهنمای تطبیقی و ناوبری یکپارچه با تقویت فرا یادگیری -2020
This paper proposes a novel adaptive guidance system developed using reinforcement meta-learning with a recurrent policy and value function approximator. The use of recurrent network layers allows the deployed policy to adapt in real time to environmental forces acting on the agent. We compare the performance of the DR/ DV guidance law, an RL agent with a non-recurrent policy, and an RL agent with a recurrent policy in four challenging environments with unknown but highly variable dynamics. These tasks include a safe Mars landing with random engine failure and a landing on an asteroid with unknown environmental dynamics. We also demonstrate the ability of a RL meta-learning optimized policy to implement a guidance law using observations consisting of only Doppler radar altimeter readings in a Mars landing environment, and LIDAR altimeter readings in an asteroid landing environment thus integrating guidance and navigation.
Keywords: Guidance | Meta learning | Reinforcement learning | Landing guidance
مقاله انگلیسی
2 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
مقاله انگلیسی
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