We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments


    Contributors:

    Conference:

    AIAA Aviation Technology, Integration, and Operations Conference ; 2015 ; Dallas, TX, United States


    Publication date :

    2015-06-22


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English