This paper describes a learning-based strategy for selecting conflict avoidance maneuvers for autonomous unmanned aircraft systems. The selected maneuvers are provided by a formally verified algorithm and they are guaranteed to solve any impending conflict under general assumptions about aircraft dynamics. The decision-making logic that selects the appropriate maneuvers is encoded in a stochastic policy encapsulated as a neural network. The network’s parameters are optimized to maximize a reward function. The reward function penalizes loss of separation with other aircraft while rewarding resolutions that result in minimum excursions from the nominal flight plan. This paper provides a description of the technique and presents preliminary simulation results.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    A Learning-Based Guidance Selection Mechanism for a Formally Verified Sense and Avoid Algorithm


    Contributors:

    Conference:

    Digital Avionics Systems Conference (DASC) ; 2019 ; San Diego, CA, United States


    Publication date :

    2019-09-08


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English




    A Learning-Based Guidance Selection Mechanism for a Formally Verified Sense and Avoid Algorithm

    Balachandran, Swee / Bajaj, Viren / Feliu, Marco A. et al. | IEEE | 2019




    Efficiency analysis of formally verified adaptive cruise controllers

    Loos, Sarah M. / Witmer, David / Steenkiste, Peter et al. | IEEE | 2013


    Software safety architecture that can be formally verified

    Cossy,M. / STZ Softwaretechnik,DE | Automotive engineering | 2004