Global formation-shape stabilization of relative sensing networks

This paper proposes a simple, distributed algorithm that achieves global stabilization of formations for relative sensing networks in arbitrary dimensions. Assuming the network runs an initialization procedure to equally orient all the agent reference frames, convergence to the desired formation shape is guaranteed even in partially asynchronous settings. We also characterize the algorithm robustness to errors in the initialization procedure. The technical approach merges ideas from graph drawing, algebraic graph theory, multidimensional scaling, and distributed linear iterations.