Abstract
A path planning framework for regional surveillance of a planar advection-diffusion process by aerial mobile sensors is proposed. The goal of the path planning is to produce feasible and collision-free trajectories for a set of aerial mobile sensors that minimize some uncertainty measure of the process under observation. The problem is formulated as a dynamic optimization problem and discretized into a large-scale nonlinear programming (NLP) problem using the Petrov–Galerkin finite element method in space and simultaneous collocation in time. Receding horizon optimization problems are solved in simulations with an advection-dominated ice concentration field. Simulations illustrate the usefulness of the proposed method.
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