We propose an online single-query sampling-based feedback motion re-planning algorithm using finite-time invariant sets, “funnels". We combine concepts from nonlinear systems analysis, sampling-based motion planning, and graph-search methods to create a single framework that enables feedback motion planning/replanning for general nonlinear dynamical systems in a dynamic workspace. We introduce a novel graph data structure to represent a network of volumetric funnels, enabling the use of quick graph-replanning techniques. The use of incremental search techniques and a pre-computed library of motion-primitives ensure that our method can be used for quick on-the-fly rewiring of controllable motion plans in response to changes in the environment. We validate our approach on a simulated 6DOF quadrotor platform operating in a maze, and random forest environment.


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    Titel :

    PiP-X: Funnel-Based Online Feedback Motion Planning/Replanning in Dynamic Environments


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    LaValle, Steven M. (Herausgeber:in) / O’Kane, Jason M. (Herausgeber:in) / Otte, Michael (Herausgeber:in) / Sadigh, Dorsa (Herausgeber:in) / Tokekar, Pratap (Herausgeber:in) / Jaffar, Mohamed Khalid M (Autor:in) / Otte, Michael (Autor:in)

    Kongress:

    International Workshop on the Algorithmic Foundations of Robotics ; 2022 ; , MD, USA June 22, 2022 - June 24, 2022



    Erscheinungsdatum :

    15.12.2022


    Format / Umfang :

    17 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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