سال انتشار:
2020
عنوان انگلیسی مقاله:
Adaptive guidance and integrated navigation with reinforcement meta-learning
ترجمه فارسی عنوان مقاله:
راهنمای تطبیقی و ناوبری یکپارچه با تقویت فرا یادگیری
منبع:
Sciencedirect - Elsevier - Acta Astronautica, 169 (2020) 180-190. doi:10.1016/j.actaastro.2020.01.007
نویسنده:
Brian Gaudeta, Richard Linaresc,∗, Roberto Furfaroa,b
چکیده انگلیسی:
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
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0