As the number of on-orbit satellites increases, the ability to repair or de-orbit them is becoming increasingly important. The implicitly required task of on-orbit inspection is challenging due to coordination of multiple observer satellites, a highly nonlinear environment, a potentially unknown or unpredictable target, and time delays associated with ground-based control. There is a critical need for autonomous, robust, decentralized solutions. To achieve this, we consider a hierarchical, learned approach for the decentralized planning of multi-agent inspection of a tumbling target. Our solution consists of two components: a viewpoint or high-level planner trained using deep reinforcement learning, and a low-level planner that will handle the point-to-point maneuvering of the spacecraft. Operating under limited information, our trained multi-agent high-level policies successfully contextualize information within the global hierarchical environment and are correspondingly able to inspect over 90% of nonconvex tumbling targets, even in the absence of additional agent attitude control.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Q-Learning for Decentralized Multi-Agent Inspection of a Tumbling Target


    Contributors:

    Published in:

    Publication date :

    2024-03-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Multi-Agent Deep Reinforcement Learning for Decentralized Cooperative Traffic Signal Control

    Zhao, Yang / Hu, Jian-Ming / Gao, Ming-Yang et al. | ASCE | 2020



    DEEP REINFORCEMENT LEARNING FOR MULTI-AGENT AUTONOMOUS SATELLITE INSPECTION

    Lei, Henry H. / Shubert, Matt / Damron, Nathan et al. | Springer Verlag | 2024



    Semisynchronizing Strategy for Capturing a High-Speed Tumbling Target

    Ma, Chuan / Wei, Caisheng / Yuan, Jianping | AIAA | 2018