This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: human-robot collaborative search. It is a flexible approach capable of handling the collaborative task, human-robot interaction and environment restrictions, all integrated on a common environment. We modelled task rewards based on unexplored area observability and isolation and evaluated the model through different levels of human-robot communication. The models are validated through quantitative evaluation against both agents’ individual performance and qualitative surveying of participants’ perception. After that, the three proposed communication levels are compared against each other using the previous metrics.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Human-Robot Collaborative Navigation Search Using Social Reward Sources


    Additional title:

    Advs in Intelligent Syst., Computing



    Conference:

    Iberian Robotics conference ; 2019 ; Porto, Portugal November 20, 2019 - November 22, 2019



    Publication date :

    2019-11-20


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Human-Robot Collaborative Navigation Search Using Social Reward Sources

    Dalmasso, Marc / Garrell, Anaís / Jiménez, Pablo et al. | TIBKAT | 2020




    Learned Tree Search for Long-Horizon Social Robot Navigation in Shared Airspace

    Navarro, Ingrid / Patrikar, Jay / Dantas, Joao P. A. et al. | ArXiv | 2023

    Free access

    Human aware robot navigation

    Kafle, A. (Aayush) | BASE | 2022

    Free access