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In this paper, we solve a joint cooperative localization and path planningproblem for a group of Autonomous Aerial Vehicles (AAVs) in GPS-denied areasusing nonlinear model predictive control (NMPC). A moving horizon estimator(MHE) is used to estimate the vehicle states with the help of relative bearinginformation to known landmarks and other vehicles. The goal of the NMPC is todevise optimal paths for each vehicle between a given source and destinationwhile maintaining desired localization accuracy. Estimating localizationcovariance in the NMPC is computationally intensive, hence we develop anapproximate analytical closed form expression based on the relationship betweencovariance and path lengths to landmarks. Using this expression while computingNMPC commands reduces the computational complexity significantly. We presentnumerical simulations to validate the proposed approach for different numbersof vehicles and landmark configurations. We also compare the results withEKF-based estimation to show the superiority of the proposed closed formapproach.

Multi-AAV Cooperative Path Planning using Nonlinear Model Predictive Control with Localization Constraints