This paper introduces a guidance strategy for spacecraft proximity operations, which leverages deep reinforcement learning, a branch of artificial intelligence. This technique enables guidance strategies to be learned rather than designed. The learned guidance strategy feeds velocity commands to a conventional controller to track. Control theory is used alongside deep reinforcement learning to lower the learning burden and facilitate the transfer of the learned behavior from simulation to reality. In this paper, a proof-of-concept spacecraft pose tracking and docking scenario is considered, in simulation and experiment, to test the feasibility of the proposed approach. Results show that such a system can be trained entirely in simulation and transferred to reality with comparable performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Reinforcement Learning for Spacecraft Proximity Operations Guidance


    Contributors:

    Published in:

    Publication date :

    2021-01-14


    Size :

    11 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Deep Learning Techniques for Autonomous Spacecraft Guidance During Proximity Operations

    Federici, Lorenzo / Benedikter, Boris / Zavoli, Alessandro | AIAA | 2021


    ON DEEP REINFORCEMENT LEARNING FOR SPACECRAFT GUIDANCE

    Hovell, Kirk / Ulrich, Steve | TIBKAT | 2020


    On Deep Reinforcement Learning for Spacecraft Guidance

    Hovell, Kirk / Ulrich, Steve | AIAA | 2020


    Machine Learning Techniques for Autonomous Spacecraft Guidance during Proximity Operations

    Federici, Lorenzo / Benedikter, Boris / Zavoli, Alessandro | AIAA | 2021


    MACHINE LEARNING TECHNIQUES FOR AUTONOMOUS SPACECRAFT GUIDANCE DURING PROXIMITY OPERATIONS

    Federici, Lorenzo / Benedikter, Boris / Zavoli, Alessandro | TIBKAT | 2021