As space exploration moves farther from Earth, robotics will play an ever-increasing role in NASA’s upcoming missions. Farther scenarios present new challenges, such as the absence or a very limited presence of human supervision for long periods of time. Spacecraft will have to be more dependent on automation and robotic agents to perform maintenance and repair tasks. Rather than having robots work side by side with humans, robots will have to work side by side with each other. In response to these needs, this chapter presents PLUMMRS (A Collection of Plan Ledgers and Unified Maps for Multi-Robot Safety), a software framework that facilitates sharing of environmental and internal state information to enable safe, efficient navigation and manipulation tasks by heterogeneous robot teams working in a shared workspace. The goal of PLUMMRS is to provide simple APIs for existing single-agent planning and execution systems to leverage. This will allow previously individualistic robots to be used safely in multi-agent contexts. This chapter describes PLUMMRS architecture and a prototype implementation, showcasing its utility in scenarios such as the ISS with a small team of robots involving 2 Astrobees and a Robonaut, performing both colocated and collaborative tasks.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    PLUMMRS: Towards Safe Multi-robot Task Planning and Execution


    Additional title:

    Springer Aerospace Techn.


    Contributors:

    Published in:

    Space Operations ; Chapter : 6 ; 141-162


    Publication date :

    2022-03-16


    Size :

    22 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    PLUMMRS: Towards Safe Multi-robot Task Planning and Execution

    Quispe, Ana Huamán / Hart, Stephen / Gee, Seth | TIBKAT | 2022


    Compliant Task Execution and Learning for Safe Mixed-Initiative Human-Robot Operations

    Dong, Shuonan / Shah, Julie / Conrad, Patrick et al. | AIAA | 2011


    Compliant Task Execution and Learning for Safe Mixed-Initiative Human-Robot Operations

    Dong, Shuonan / Conrad, Patrick R. / Shah, Julie A. et al. | NTRS | 2011


    Compliant Task Execution and Learning for Safe Mixed-Initiative Human-Robot Operations

    Dong, S. / Shah, J. / Conrad, P. et al. | British Library Conference Proceedings | 2011


    Towards multi-robot task-motion planning for navigation in belief space

    Thomas A. / Mastrogiovanni F. / Baglietto M. | BASE | 2020

    Free access