Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions

08/19/2020
by   Alessandro Zavoli, et al.
0

This paper investigates the use of Reinforcement Learning for the robust design of low-thrust interplanetary trajectories in presence of severe disturbances, modeled alternatively as Gaussian additive process noise, observation noise, control actuation errors on thrust magnitude and direction, and possibly multiple missed thrust events. The optimal control problem is recast as a time-discrete Markov Decision Process to comply with the standard formulation of reinforcement learning. An open-source implementation of the state-of-the-art algorithm Proximal Policy Optimization is adopted to carry out the training process of a deep neural network, used to map the spacecraft (observed) states to the optimal control policy. The resulting Guidance and Control Network provides both a robust nominal trajectory and the associated closed-loop guidance law. Numerical results are presented for a typical Earth-Mars mission. First, in order to validate the proposed approach, the solution found in a (deterministic) unperturbed scenario is compared with the optimal one provided by an indirect technique. Then, the robustness and optimality of the obtained closed-loop guidance laws is assessed by means of Monte Carlo campaigns performed in the considered uncertain scenarios. These preliminary results open up new horizons for the use of reinforcement learning in the robust design of interplanetary missions.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/24/2022

Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology

A general control policy framework based on deep reinforcement learning ...
research
03/09/2021

Computational Impact Time Guidance: A Learning-Based Prediction-Correction Approach

This paper investigates the problem of impact-time-control and proposes ...
research
08/09/2022

Neural-Rendezvous: Learning-based Robust Guidance and Control to Encounter Interstellar Objects

Interstellar objects (ISOs), astronomical objects not gravitationally bo...
research
01/08/2019

Trajectory Design of Multiple Near Earth Asteroids Exploration Using Solar Sail Based on Deep Neural Network

In the preliminary trajectory design of the multi-target rendezvous prob...
research
01/08/2019

Solar-Sail Trajectory Design of Multiple Near Earth Asteroids Exploration Based on Deep Neural Network

In the preliminary trajectory design of the multi-target rendezvous prob...
research
01/08/2019

Solar-Sail Trajectory Design for Multiple Near Earth Asteroid Exploration Based on Deep Neural Networks

In the preliminary trajectory design of the multi-target rendezvous prob...
research
01/10/2020

Optimal Disturbance Attenuation Approach with Measurement Feedback to Missile Guidance

Pursuit-evasion differential games using the Disturbance Attenuation app...

Please sign up or login with your details

Forgot password? Click here to reset