In this paper, we solve a joint cooperative localization and path planning problem for a group of Autonomous Aerial Vehicles (AAVs) in GPS-denied areas using nonlinear model predictive control (NMPC). A moving horizon estimator (MHE) is used to estimate the vehicle states with the help of relative bearing information to known landmarks and other vehicles. The goal of the NMPC is to devise optimal paths for each vehicle between a given source and destination while maintaining desired localization accuracy. Estimating localization covariance in the NMPC is computationally intensive; hence, we develop an approximate analytical closed-form expression based on the relationship between covariance and path lengths to landmarks. Using this expression while computing NMPC commands reduces the computational complexity significantly. We present numerical simulations to validate the proposed approach for different numbers of vehicles and landmark configurations. We also compare the results with EKF and RRT $^{\text {*}}$ based methods to show the superiority of the proposed closed-form approach.
Multi-AAV Cooperative Path Planning Using Nonlinear Model Predictive Control With Localization Constraints
IEEE Transactions on Intelligent Transportation Systems ; 25 , 8 ; 9453-9466
01.08.2024
5850435 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Cooperative path planning with reconnaissance duration time constraints
British Library Online Contents | 2016
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