This paper proposes a nonlinear model predictive control (NMPC) scheme to tackle the problem of localization and path planning of a group of unmanned aerial vehicles (UAVs) in global positioning system (GPS) denied environments. It is assumed that the UAVs can cooperate by sharing information among themselves. It is also assumed that the area under consideration contains some landmarks with known locations. The NMPC computes the optimal control inputs for the vehicles such that the vehicles cooperate to transit from a source location to a destination while choosing a path that will cover enough landmarks for localization. An Extended Kalman Filter (EKF) is used to estimate the vehicle positions using only relative bearing measurements. The efficacy of the proposed method was evaluated through numerical simulations, and the results are discussed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Nonlinear Model Predictive Control to Aid Cooperative Localization


    Contributors:


    Publication date :

    2019-06-01


    Size :

    237768 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multi-AAV Cooperative Path Planning Using Nonlinear Model Predictive Control With Localization Constraints

    Manoharan, Amith / Sharma, Rajnikant / Baliyarasimhuni, Sujit | IEEE | 2024


    Cooperative Trajectory Planning for Autonomous Driving Using Nonlinear Model Predictive Control

    Viana, Icaro Bezerra / Kanchwala, Hussain / Aouf, Nabil | IEEE | 2019



    Nonlinear Model Predictive Control

    Taghavipour, Amir / Vajedi, Mahyar / Azad, Nasser L. | Springer Verlag | 2018


    Model predictive control for cooperative control of space robots

    Kannan, Somasundar / Alamdari, Seyed Amin Sajadi / Dentler, Jan et al. | American Institute of Physics | 2017