Optimization-based autonomous remote sensing of surface objects using an unmanned aerial vehicle

This manuscript presents an optimization-based approach for path planning of an aerial mobile sensor that monitors a set of moving surface objects. The purpose of the optimization problem is to obtain feasible mobile sensor trajectories with an objective to minimize the uncertainty of the objects, represented as the trace of the state estimation error covariance. The dynamic optimization problem is discretized into a large-scale nonlinear programming (NLP) problem using the direct transcription method known as simultaneous collocation. The numerical simulation periodically provides desired sensor trajectories and thus illustrates the approach.