An intelligent and cooperative collision avoidance method is proposed based on a combination of enhanced potential fields and fuzzy inference systems (FIS), where a genetic algorithm is used to optimize the FISs. The proposed approach provides a near-optimal and collision-free path in an environment with static and dynamic obstacles taking into consideration potential uncertainties. Furthermore, it is able to resolve local minima and goal non-reachable with obstacles nearby issues that exist in the traditional artificial potential field approach with minimum computational burden. A simple scenario modeled based on the issues faced by UAVs in such environments is used for training the system, and a complex scenario containing a number of dynamic UAVs in the presence of static obstacles is considered to validate the performance of the proposed approach.


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    Titel :

    Decentralized Collision Avoidance via Fuzzy Potential Fields


    Beteiligte:
    Chhabra, Anirudh (Autor:in) / Choi, Daegyun (Autor:in) / Kim, Donghoon (Autor:in)


    Erscheinungsdatum :

    16.08.2021


    Format / Umfang :

    1847612 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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